EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 3. Breakpoint Detection
---
A first step in assessing temporal behaviour is to analyse the **rate of change** of the forest-area time series. This provides an intuitive view of how the pace of forest expansion evolves over time and helps highlight periods where the trajectory deviates from its typical pattern.

However, while such exploratory diagnostics identify points of interest, they do not by themselves confirm whether the observed changes are structurally meaningful. Year-to-year variations may reflect noise, smoothing effects, or gradual adjustments in the underlying product. To formally assess structural changes, a **statistical breakpoint detection method** is applied.

The analysis therefore combines **rate-of-change diagnostics** with **statistical segmentation**.

---

**Rate of Change Calculation**

- The **first derivative** of the forest-area time series is computed to quantify the **year-to-year change in forest extent**.
- This highlights **periods where the pace of forest expansion deviates from its typical behaviour**, providing an initial indication of potential anomalies or shifts in the series.

---

**Statistical Thresholding of Derivative Spikes**

- To identify unusually strong deviations, a dynamic threshold is defined as:

**Threshold = mean(|rate of change|) + 1.5 × standard deviation**

- Years where the **absolute rate of change exceeds this threshold** are flagged as **derivative spikes**.
- These spikes represent periods where the magnitude of change is unusually large relative to the typical variability of the series.
- This step serves as a **diagnostic indicator of anomalous growth behaviour**, but does not in itself define structural breakpoints.

---

**Breakpoint Detection Using the PELT Algorithm**

- The **Pruned Exact Linear Time (PELT)** algorithm is used to detect **changes in the statistical behaviour of the growth rate**, rather than in the cumulative forest-area series itself.

- The algorithm is therefore applied to the **first derivative (growth rate)**, allowing detection of:
  - transitions between periods with different **rates of forest expansion**, rather than absolute levels.

- The implementation uses:
  - the **L2 cost function** (`model="l2"`) to minimise variance within segments,
  - a **fixed penalty parameter** (`pen = 2.5`) applied to a standardised growth-rate series,
  - and a **minimum segment length (`min_size = 3`)** to avoid short, unstable segments.

- Prior to segmentation, the growth-rate series may be **standardised** to ensure comparability across regions with different magnitudes of forest change.

---

**Interpreting Breakpoint Timing**

Breakpoint positions should be interpreted as **approximate indicators of when a change in growth dynamics begins**, rather than exact points of maximum change.

Because segmentation identifies **changes in statistical behaviour**, the detected breakpoint may:

- precede the most visible increase in growth rate, or
- occur within a broader transition period.

As a result, breakpoint timing should be considered **indicative rather than exact**, and interpreted in conjunction with derivative-based diagnostics.

Calculate breakpoints and derivative spikes for each region
Number of subplot columns in the regional figure layout
Threshold multiplier used to flag unusually large derivative spikes:
spike if |growth rate| > mean(|growth rate|) + spike_std_factor * std(growth rate)
Minimum number of derivative observations allowed in each PELT segment.
Penalty term for PELT breakpoint detection.
Standardize the growth-rate series before applying PELT so that breakpoint
detection is comparable across regions with different magnitudes of forest change.
remove invalid and terminal interval
==================================================
==================================================

Plot Breakpoints Series
Store spikes
Store breakpoints
Background phases
Remove empty axes
Processing phases legend
Line/signal legend
Compact spacing between legends and plots

Plot the aligned breakpoints and derivative spikes with known methodological changes present in more than half of the regions
Rename Interval column
Add relation to processing transition
Drop helper column
Sort by number of regions, then by interval
Build tables
Display nicely

```text
### Breakpoint Table ###
```

```text
intervals  number of regions  \
0  2001-2002                 12
1  1996-1997                 10
2  2006-2007                  1
3  2011-2012                  1
4  2016-2017                  1


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 3. Breakpoint Detection
---
1996-1997                 10
2  2006-2007                  1
3  2011-2012                  1
4  2016-2017                  1

regions name  \
0  Andalucía, Aragón, Castilla y León, Castilla-La Mancha, Cataluña, Comunidad Foral de Navarra, Comunitat Valenciana, Galicia, La Rioja, Norte, País Vasco, Área Metropolitana de Lisboa
1                                             Alentejo, Andalucía, Cantabria, Comunidad Foral de Navarra, Extremadura, Galicia, La Rioja, Norte, País Vasco, Área Metropolitana de Lisboa
2                                                                                Cantabria
3                                                                                Comunidad de Madrid
4                                                                                Área Metropolitana de Lisboa

nearest processing changes
0        None within 2 years
1        None within 2 years
2        None within 2 years
3        None within 2 years
4        None within 2 years
```

```text
### Derivative Spike Table ###
```

```text
intervals  number of regions  \
0   1998-1999                 15
1   2003-2004                  5
2   2015-2016                  4
3   1994-1995                  2
4   2001-2002                  2
5   1997-1998                  1
6   1999-2000                  1
7   2000-2001                  1
8   2002-2003                  1
9   2004-2005                  1
10  2011-2012                  1
11  2016-2017                  1
12  2017-2018                  1
13  2020-2021                  1
14  2021-2022                  1

regions name  \
0   Alentejo, Algarve, Andalucía, Aragón, Cantabria, Castilla-La Mancha, Cataluña, Comunidad Foral de Navarra, Extremadura, Galicia, Illes Balears, La Rioja, País Vasco, Principado de Asturias, Área Metropolitana de Lisboa
1                                                                                Centro (PT), Galicia, Norte, Principado de Asturias, Región de Murcia
2                                                                                Alentejo, Castilla y León, Comunidad de Madrid, Área Metropolitana de Lisboa
3                                                                                Aragón, Comunitat Valenciana
4                                                                                Galicia, Norte
5                                                                                La Rioja
6                                                                                Región de Murcia
7                                                                                Principado de Asturias
8                                                                                Cantabria
9                                                                                Algarve
10                                                                                Comunitat Valenciana
11                                                                                Comunidad de Madrid
12                                                                                Castilla y León
13                                                                                Extremadura
14                                                                                Región de Murcia

nearest processing changes
0                                None within 2 years
1                        MERIS_baseline_start (2003)
2                                None within 2 years
3                                None within 2 years
4                                None within 2 years
5                                None within 2 years
6         AVHRR_finish (1999); SPOT-VGT_start (1999)
7         AVHRR_finish (1999); SPOT-VGT_start (1999)
8                                None within 2 years
9                        MERIS_baseline_start (2003)
10                               None within 2 years
11                               None within 2 years
12                               None within 2 years
13                              S3_OLCI_start (2020)
14  S3_OLCI_start (2020); S3_OLCI_SLSTR_start (2021)
```


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 3. Breakpoint Detection > Breakpoint and Derivative-Spike Analysis
---
Breakpoint detection and derivative analysis describe related but not identical aspects of temporal change in land-cover time series. Breakpoints indicate potential structural shifts, while derivative spikes identify years with strong year-to-year variation ([Chelali et al., 2019](https://doi.org/10.1109/JURSE.2019.8808967); [Chang et al., 2018](https://doi.org/10.1088/1755-1315/113/1/012087)). However, the repeated concentration of breakpoints and derivative spikes around similar years suggests that some of the observed variability may reflect methodological sensitivity, rather than clearly separable ecological or land-use processes.

Signals are considered more robust when they show broad spatial coherence across regions, but temporal clustering alone is not treated as evidence of multiple independent change events. In satellite-derived land-cover products, changes in sensors, classification schemes, reference layers, or processing chains can introduce apparent discontinuities or amplified year-to-year variation. Therefore, breakpoint and derivative-spike results are assessed together, with attention to both spatial consistency and possible methodological artefacts ([Chang et al., 2018](https://doi.org/10.1088/1755-1315/113/1/012087)).

The **1998–1999 derivative signal**, affecting 15 regions, is the most spatially widespread short-term variability signal. Its limited correspondence with a clear breakpoint suggests that it may represent transient variability, classification sensitivity in mixed or transitional land-cover classes, or early inconsistencies in the time series.

A major breakpoint cluster is also observed around **2001–2002**, affecting 12 regions. Although this is the most spatially coherent breakpoint signal, no clearly documented methodological transition is identified within a close temporal window. This weakens any direct attribution to a known processing-chain change. The signal may partly reflect real land-cover dynamics, including wildfire activity, forest management changes, or broader land-use transitions in the Iberian Peninsula [(Silva et al., 2011)](https://doi.org/10.1016/j.landurbplan.2011.03.001). Nevertheless, because breakpoint and derivative signals cluster around nearby years, this period is best interpreted as a strong but not fully attributable signal, potentially combining environmental change with methodological effects.

A secondary derivative signal occurs around **2003–2004**, affecting 5 regions. This timing coincides with major wildfire activity and forest loss in parts of the Iberian Peninsula [(Mato et al., 2014)](https://api.semanticscholar.org/CorpusID:265869582), as well as improvements in detection capacity.

An earlier breakpoint cluster around **1996–1997**, affecting 10 regions, suggests a possible structural shift in western and northern regions. Its timing is consistent with broader land-use transitions, including farmland abandonment and natural forest regeneration [(Palmero-Iniesta et al., 2021)](https://api.semanticscholar.org/CorpusID:238829360), as well as changes in forest management. However, given its proximity to the stronger 1998–1999 derivative signal, it should not be treated as a fully independent event without further validation.

Overall, the recurrence of breakpoint and derivative-spike detections around similar years indicates that the time series contains periods of heightened instability, but these should not be over-interpreted as discrete ecological events. The most defensible interpretation is that the late 1990s to early 2000s represent an interval of elevated variability possibly shaped by a combination of land-cover change, disturbance processes, and methodological sensitivity in the data product.

Additional breakpoint and derivative-spike detections affect only a small number of regions. These are best interpreted as localised or context-specific dynamics, potentially linked to wildfires, drought impacts, regional land-use change, plantation dynamics, or natural regeneration following abandonment [(Gouveia et al., 2012](https://nhess.copernicus.org/articles/12/3123/2012/)[; Vidal-Macua et al., 2017)](https://doi.org/10.1016/j.foreco.2017.10.011); [(Silva et al., 2011)](https://doi.org/10.1016/j.landurbplan.2011.03.001); [(Palmero-Iniesta et al., 2021)](https://api.semanticscholar.org/CorpusID:238829360).

(code-section-4)=


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 4. Trend Assessment
---
The next and final step is to determine whether the trend should be computed for the entire period (total trend) or divided into segments based on detected breakpoints.

The decision between using the total trend or segmented trends is based on Sen’s Slope (Trend Magnitude) and Mann-Kendall p-values (Trend Significance). Sen’s Slope is a non-parametric estimator that calculates the median rate of change over time, making it robust to outliers. First, Sen’s Slope is computed for the total trend, capturing the overall rate of forest change. Then, Sen’s Slope is calculated for each segmented trend to assess whether breakpoints introduce significant shifts in trend magnitude. The Mann-Kendall test is a non-parametric statistical test used to assess the presence of upward or downward trend in a time series without requiring the data to follow any particular distribution. In parallel, the Mann-Kendall p-value is evaluated for both the total and segmented trends to measure trend significance—lower p-values indicate stronger evidence of a significant trend.

The final decision is based on the following approach:

- If segmented trends show substantially different Sen’s Slopes compared to the total trend and exhibit stronger statistical significance (lower p-values), segmentation is preferred.
- If segmented trends closely resemble the total trend or do not provide a clear statistical advantage, the total trend is used to maintain a simpler and more robust interpretation.

Prepare data for trend calculation
Keep only valid rows

Calculate trends
Compute total + segmented trends
Total Sen slope
Convert to % area per decade, as requested by reviewer
Mann-Kendall
Breakpoints and segmented trends

The following summary table reports:

- **Growth rate (%/decade)** → long-term rate of forest expansion
- **Significance** → statistical robustness of the full trend
    - `***` → highly significant (p < 0.001)
    - `**` → significant (p < 0.01)
    - `*` → moderately significant (p < 0.05)
    - no symbol → not statistically significant
- **Breakpoints** → number of structural changes detected
- **Segmented trend needed?** → whether segmented trends change the interpretation
- **Confidence** → degree of agreement between full and segmented trend significance

Build summary table
forest area for each region in the latest year
Sort by growth rate
Round for display
Normalize text values
HTML table display

```text
21 regions were assessed. Segmented trends are not required in 18 of 21 regions. Confidence is High in 10 regions, Medium in 7, and Low in 4.
```


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 4. Trend Assessment > Trend Analysis
---
The results demonstrate that while breakpoints exist, whether driven by real environmental changes or influenced by sensor/methodological shifts, they do not necessarily require separate trend calculations. In most cases, the total trend provides a stable and reliable representation of forest evolution across the full dataset, reinforcing the idea that forest cover follows a largely continuous trajectory over time.

However, certain regions exhibit segmented trends that deviate significantly from the total trend. These cases merit special attention, as they may reflect actual shifts in forest dynamics due to land management, climate events, or localised disturbances.

A key consideration is the geographical and ecological diversity of the Iberian Peninsula. Larger regions with varied landscapes may show more gradual changes in forest cover, making the total trend more representative. In contrast, smaller or ecologically distinct regions may experience more abrupt shifts that justify the use of segmented trends in specific cases.

Ultimately, while breakpoints provide useful insights into short-term variations, they do not invalidate the overall trend. This analysis confirms that long-term forest trends in the Iberian Peninsula can be effectively assessed using the full dataset, with segmentation being relevant only when trends deviate sharply and are supported by contextual or statistical evidence.

(code-section-5)=


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 5. Discussion
---
The results of this analysis highlight the value of the dataset for monitoring forest dynamics in the Iberian Peninsula. The dataset proves robust for long-term trend detection, with methodological transitions and sensor shifts,having none to limited impact on overarching patterns of forest change.

For applications in spatial planning and land management, this consistency is crucial. The ability to track forest evolution across decades allows regional planners and policymakers to evaluate the outcomes of afforestation policies, conservation efforts, and land abandonment. Even in the presence of minor reclassifications or local anomalies, the dataset supports confident assessments of broader land cover trends.

Nonetheless, the analysis also underscores the importance of methodological awareness. Breakpoints, particularly those associated with known sensor transitions, can introduce artificial changes in the time series. These do not necessarily reflect ecological shifts but rather improvements in resolution, classification algorithms, or spectral capabilities. Users must remain cautious when interpreting abrupt changes without considering the underlying data lineage.

Geographic context further shapes interpretability. Larger, heterogeneous regions tend to smooth out classification noise, while smaller or fragmented areas may reflect sharper, localized breaks. This has direct implications for regional land management strategies, where scale and land use diversity influence the reliability of detected trends.

In practical terms, the dataset is well-suited for identifying general trends in forest cover, but users should:

- Complement statistical analysis with contextual information (e.g., known policy events, fire records),
- Use segmentation carefully—reserving it for regions with strong evidence of structural change,
- Remain cautious when interpreting data near known sensor transition years.

In conclusion, the dataset provides a strong foundation for forest monitoring and planning. However, optimal use requires not just quantitative analysis, but also critical interpretation of when and why changes appear in the data. This fusion of methods and context is essential for informed decision-making in land and forest management.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > ℹ If you want to know more > Key Resources
---
* The CDS catalogue entry for the data used was [Land cover classification gridded maps from 1992 to present derived from satellite observations](https://cds.climate.copernicus.eu/datasets/satellite-land-cover?tab=overview)

* Product User Guide and Specification of the dataset [version 2.1](https://dast.copernicus-climate.eu/documents/satellite-land-cover/D5.3.1_PUGS_ICDR_LC_v2.1.x_PRODUCTS_v1.1.pdf) and [version 2.0](https://dast.copernicus-climate.eu/documents/satellite-land-cover/D3.3.11-v1.0_PUGS_CDR_LC-CCI_v2.0.7cds_Products_v1.0.1_APPROVED_Ver1.pdf)

* [Eurostat NUTS](https://ec.europa.eu/eurostat/web/nuts) (Nomenclature of territorial units for statistics)

* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), c3s_eqc_automatic_quality_control, prepared by [B-Open](https://www.bopen.eu/)


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > ℹ If you want to know more > References
---
- [EUROSTAT: Forests, forestry and logging](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Forests,_forestry_and_logging)

- [New EU Forest Strategy for 2030](https://commission.europa.eu/document/cf3294e1-8358-4c93-8de4-3e1503b95201_en)

- [Mato, M.M., Legido, J.L., Miguez, E., Carballas, T., Jiménez, E., Caselles, V., & Andrade, M.I. (2014). Analysis of burnt areas and number of forest fires in the Iberian Peninsula.](https://api.semanticscholar.org/CorpusID:265869582)

- [Silva, J., Vaz, P.G., Moreira, F., Catry, F.X., & Rego, F.C. (2011). Wildfires as a major driver of landscape dynamics in three fire-prone areas of Portugal. *Landscape and forest Planning, 101*, 349-358.](https://api.semanticscholar.org/CorpusID:83540326)

- [Gouveia, C.M., Bastos, A., Trigo, R.M., & DaCamara, C.C. (2012). Drought impacts on vegetation in the pre- and post-fire events over Iberian Peninsula. *Natural Hazards and Earth System Sciences, 12*, 3123-3137.](https://nhess.copernicus.org/articles/12/3123/2012/)

- [Vidal-Macua, J.J., Ninyerola, M., Zabala, A., Domingo‐Marimon, C., & Pons, X. (2017). Factors affecting forest dynamics in the Iberian Peninsula from 1987 to 2012: the role of topography and drought. *Forest Ecology and Management, 406*, 290-306.](https://doi.org/10.1016/j.foreco.2017.10.011)

- [Sevillano, E.H., Contador, J.F., Schnabel, S., Pulido, M., & Ibáñez, J.Q. (2018). Using spatial models of temporal tree dynamics to evaluate the implementation of EU afforestation policies in rangelands of SW Spain. *Land Use Policy.*](https://doi.org/10.1016/j.landusepol.2018.06.054)

- [Palmero-Iniesta, M., Espelta, J.M., Padial-Iglesias, M., González-Guerrero, Ó., Pesquer, L., Domingo‐Marimon, C., Ninyerola, M., Pons, X., & Pino, J. (2021). The Role of Recent (1985–2014) Patterns of Land Abandonment and Environmental Factors in the Establishment and Growth of Secondary Forests in the Iberian Peninsula. *Land.*](https://api.semanticscholar.org/CorpusID:238829360)

- [Silva, J., Vaz, P.G., Moreira, F., Catry, F.X., & Rego, F.C. (2011). Wildfires as a major driver of landscape dynamics in three fire-prone areas of Portugal. *Landscape and forest Planning, 101*, 349-358.](https://doi.org/10.1016/j.landurbplan.2011.03.001)

- [Regos, A., Ninyerola, M., Moré, G., & Pons, X. (2015). Linking land cover dynamics with driving forces in mountain landscapes of the Northwestern Iberian Peninsula. *Int. J. Appl. Earth Obs. Geoinformation, 38*, 1-14.](https://doi.org/10.1016/j.jag.2014.11.010)

- [Toté, C., Swinnen, E., & Henocq, C. (2024). An Evaluation of Sentinel-3 SYN VGT Products in Comparison to the SPOT/VEGETATION and PROBA-V Archives. Remote Sensing, 16(20), 3822. https://doi.org/10.3390/rs16203822 ](https://doi.org/10.3390/rs16203822)


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > ℹ If you want to know more > References
---
BA-V Archives. Remote Sensing, 16(20), 3822. https://doi.org/10.3390/rs16203822 ](https://doi.org/10.3390/rs16203822)

- [Zhao, T., Zhang, X., Gao, Y., Mi, J., Liu, W., Wang, J., Jiang, M., & Liu, L. (2023). Assessing the Accuracy and Consistency of Six Fine-Resolution Global Land Cover Products Using a Novel Stratified Random Sampling Validation Dataset. Remote Sensing, 15(9)](https://doi.org/10.3390/rs15092285)


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management
---
Production date: 05-06-2026

Produced by: Inês Girão e Luís Figueiredo(+ATLANTIC)


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Quality assessment question
---
* **How consistent with other datasets are satellite observations in capturing land cover changes, such as urbanisation?**

Land cover data is a vital resource across a wide range of fields, from climate change research to urban and regional planning. Products with long historical timelines allow scientists, policymakers, and planners to assess how land use and land cover have evolved over time, supporting evidence-based decision-making.

In this notebook, we use the ***Land Cover Classification Gridded Maps from 1992 to present derived from satellite observations*** (hereafter referred to as **LC**) provided by the Climate Data Store (CDS) of the [Copernicus Climate Change Service (C3S)](https://climate.copernicus.eu/esotc/2023). The analysis focuses on evaluating the consistency of C3S-derived settlement (Artificial Land) classes with independent statistical records from **EUROSTAT**, across selected years and NUTS2 regions in the Iberian Peninsula.


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Quality assessment statement
---
These are the key outcomes of this assessment

* The dataset demonstrates physical consistency and precision among the different datasets as it aligns closely with equivalent statistics from official sources [(EUROSTAT,2025)](https://ec.europa.eu/eurostat/databrowser/bookmark/63d3fd90-2f5b-4bec-99f3-4f88814e2624?lang=en). Specifically, the analysis shows that the Urban/Settlements category closely matches EUROSTAT's statistics for the Artificial Land category in the most populated NUTs 2 regions. Differences are typically small, within 0.3–5% absolute difference for the majority of regions and years assessed.

* Beyond the scope of this analysis, additional work could examine whether the dataset aligns with findings reported in peer-reviewed studies, such as [Fernández-Nogueira and Corbelle-Rico (2018)](https://doi.org/10.1016/j.apgeog.2009.07.003). This would help validate the robustness and reliability of the dataset through comparison with multiple datasets.

* Another aspect of the analysis supporting the dataset's precision is the identification of trade-offs between urban expansion and the decline of agricultural areas, a well-documented phenomenon. Numerous studies, including observations across multiple European regions, note that urban growth frequently replaces agricultural lands [Bagan and Yamagata (2014)](https://doi.org/10.1088/1748-9326/9/6/064015).

```

![Urbanization_Map_Series_v2.png](attachment:Urbanization_Map_Series_v2.png)


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Methodology
---
**[](code-section-1)**

**[](code-section-2)**

**[](code-section-3)**

**[](code-section-4)**

**[](code-section-5)**

**[](code-section-6)**


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data.
---
Before we begin we must prepare our environment. This includes installing the Application Programming Interface (API) of the CDS, and importing the various python libraries that we will need.


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data. > Import all the libraries/packages
---
We will be working with data in NetCDF format. To best handle this type of data we will use libraries for working with multidimensional arrays, in particular Xarray.
We will also need libraries for plotting and viewing data.


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data. > Data Overview
---
To search for data, visit the CDS website: http://cds.climate.copernicus.eu Here you can search for 'Satellite observations land' using the search bar. The data we need for this tutorial is the ***Land cover classification gridded maps from 1992 to present derived from satellite observations***. This catalogue entry provides global Land Cover Classification (LCC) maps with a very high spatial resolution, with a L4 processing level, on an annual basis with a one-year delay, following the [Global Climate Observing System (GCOS) convention requirements](https://library.wmo.int/records/item/58111-the-2022-gcos-ecvs-requirements-gcos-245), under which land cover is recognised as an Essential Climate Variable (ECV) due to its importance for monitoring climate change and its impacts on terrestrial ecosystems. These Land Cover (LC) maps correspond to a global classification scheme encompassing 22 land cover classes.

**Note:** Although the dataset is officially described as having 22 global land cover classes, many of these classes are internally subdivided into more detailed subcategories (e.g., types of cropland, forest canopy structure, types of shrubland). These subdivisions allow for greater ecological detail but can be aggregated back into the 22 main classes for standard analysis and intercomparison purposes.

Data specifications for this use case:
- **Years:** 2009, 2012, 2015, 2018 and 2022
- **Versions:** v2.0.7 for years up to 2015; v2.1.1 for 2018
- **Format:** Downloaded as .zip files

At the end of the data request form on CDS, select `Show API request` to generate Python code, which can be pasted directly into a Jupyter Notebook cell. Running this cell will retrieve the requested files, provided that you have accepted the dataset's `terms and conditions` on the CDS platform. It is advisable to define the desired time period and Area of Interest (AoI) explicitly when preparing the API request, as shown in the example cells below.

Urban areas are represented by **class 190: Urban areas** on the LC dataset. In the IPCC aggregation used by the product, this class belongs to the **Settlement** category. In the underlying UN/FAO Land Cover Classification System coding, class 190 is mapped to B15 Artificial Surfaces, labelled as “Artificial surfaces and associated areas.” The product guide does not provide a more detailed breakdown of what this class includes, such as specific built-up elements or infrastructure types. It only identifies the class as Urban areas / Artificial Surfaces and notes that it relies on external urban reference datasets, specifically the Global Human Settlement Layer and the Global Urban Footprint.

Years to download

List of requests to retrieve data

Download and regionalise by AoI

```text
100%|██████████| 5/5 [00:00<00:00, 14.65it/s]
/data/common/miniforge3/envs/wp5/lib/python3.12/site-packages/earthkit/data/readers/netcdf/fieldlist.py:202: FutureWarning:

In a future version of xarray the default value for data_vars will change from data_vars='all' to data_vars=None. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set data_vars explicitly.
```

(code-section-2)=


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data
---
Now that we have downloaded the data, we can inspect it. In the previous step, the data were downloaded and automatically loaded into an Xarray dataset using the `download.download_and_transform()` helper function.


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data > Label Color Definition and Class Correspondence
---
To facilitate visual inspection of the Land Cover (LC) classes, we define a dictionary containing each class label (the "keys"), the corresponding color code (the "colors"), and the associated numeric identifier (the "values"). In addition, we create a second dictionary to establish the correspondence between the original land cover classes provided in the metadata and the aggregated IPCC classes, as described in the Product User Guide (see resources).

Define LC labels dictionary
Helper function to format the labels text

```text
{'No Data': ('#000000', np.uint8(0)),
 'Cropland Rainfed': ('#ffff64', np.uint8(10)),
 'Cropland Rainfed Herbaceous Cover': ('#ffff64', np.uint8(11)),
 'Cropland Rainfed Tree Or Shrub Cover': ('#ffff00', np.uint8(12)),
 'Cropland Irrigated': ('#aaf0f0', np.uint8(20)),
 'Mosaic Cropland': ('#dcf064', np.uint8(30)),
 'Mosaic Natural Vegetation': ('#c8c864', np.uint8(40)),
 'Tree Broadleaved Evergreen Closed To Open': ('#006400', np.uint8(50)),
 'Tree Broadleaved Deciduous Closed To Open': ('#00a000', np.uint8(60)),
 'Tree Broadleaved Deciduous Closed': ('#00a000', np.uint8(61)),
 'Tree Broadleaved Deciduous Open': ('#aac800', np.uint8(62)),
 'Tree Needleleaved Evergreen Closed To Open': ('#003c00', np.uint8(70)),
 'Tree Needleleaved Evergreen Closed': ('#003c00', np.uint8(71)),
 'Tree Needleleaved Evergreen Open': ('#005000', np.uint8(72)),
 'Tree Needleleaved Deciduous Closed To Open': ('#285000', np.uint8(80)),
 'Tree Needleleaved Deciduous Closed': ('#285000', np.uint8(81)),
 'Tree Needleleaved Deciduous Open': ('#286400', np.uint8(82)),
 'Tree Mixed': ('#788200', np.uint8(90)),
 'Mosaic Tree And Shrub': ('#8ca000', np.uint8(100)),
 'Mosaic Herbaceous': ('#be9600', np.uint8(110)),
 'Shrubland': ('#966400', np.uint8(120)),
 'Shrubland Evergreen': ('#966400', np.uint8(121)),
 'Shrubland Deciduous': ('#966400', np.uint8(122)),
 'Grassland': ('#ffb432', np.uint8(130)),
 'Lichens And Mosses': ('#ffdcd2', np.uint8(140)),
 'Sparse Vegetation': ('#ffebaf', np.uint8(150)),
 'Sparse Tree': ('#ffc864', np.uint8(151)),
 'Sparse Shrub': ('#ffd278', np.uint8(152)),
 'Sparse Herbaceous': ('#ffebaf', np.uint8(153)),
 'Tree Cover Flooded Fresh Or Brakish Water': ('#00785a', np.uint8(160)),
 'Tree Cover Flooded Saline Water': ('#009678', np.uint8(170)),
 'Shrub Or Herbaceous Cover Flooded': ('#00dc82', np.uint8(180)),
 'Urban': ('#c31400', np.uint8(190)),
 'Bare Areas': ('#fff5d7', np.uint8(200)),
 'Bare Areas Consolidated': ('#dcdcdc', np.uint8(201)),
 'Bare Areas Unconsolidated': ('#fff5d7', np.uint8(202)),
 'Water': ('#0046c8', np.uint8(210)),
 'Snow And Ice': ('#ffffff', np.uint8(220))}
```

Define IPCC labels dictionary


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data > Label Color Definition and Class Correspondence
---
8(202)),
 'Water': ('#0046c8', np.uint8(210)),
 'Snow And Ice': ('#ffffff', np.uint8(220))}
```

Define IPCC labels dictionary

```text
{'No Data': ('#000000', [0]),
 'Agriculture': ('#ffffcc', [10, 11, 12, 20, 30, 40]),
 'Forest': ('#4c9900',
  [50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 160, 170]),
 'Grassland': ('#ccff99', [110, 130]),
 'Settlement': ('#ff0000', [190]),
 'Wetland': ('#99ffff', [180]),
 'Other': ('#0000ff',
  [120, 121, 122, 140, 150, 151, 152, 153, 200, 201, 202, 210])}
```


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data > Plot maps
---
Having defined the color and legends for the IPCC classes and using the metadata of the dataset to get the colors and legends for each Land Cover class it is now possible to plot our data either with the original colors or with the IPCC previously defined colors.

The function below plots the LC maps for the year of your choice , using both land cover schemes. From the output, we can already distinguish the differences in classification schemes.

unique codes
Filter both legends
1) LCCS map (only present classes in legend)
2) IPCC map (only present groups in legend)

(code-section-3)=


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 3. Data preparation and area-based calculations
---
To further identify changes in LC patterns, in this user question, Nomenclature of Territorial Units for Statistics (NUTS) 2 will be used, providing the information regarding the main regions/parcels of the Iberian Peninsula.

The [NUTS](https://ec.europa.eu/eurostat/web/nuts) are a hierarchical system divided into 3 levels. NUTS 1 correspond to major socio-economic regions, NUTS 2 correspond to basic regions for the application of regional policies, and NUTS 3 correspond to small regions for specific diagnoses. Additionally a NUTS 0 level, usually co-incident with national boundaries is also available. The NUTS legislation is periodically amended; therefore multiple years are available for download.

The step below masks the Land Cover data according to the NUTS 2 boundaries and calculates the area of each pixel (weighted by Latitude). For each NUTS 2 region, we proceed with the analysis and visual inspection of Land Cover areas per class and corresponding percentages during the selected period.


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 3. Data preparation and area-based calculations > Mask regions
---
First, we need to establish the geometry of the NUTS region (level 2) in order to make the corresponding statistics.

Shapefile with regions (NUTS2)

Define the CRS for the Iberian Peninsula (WGS84)
Data filter: load the shapefile and filter for NUTS2 regions in Spain and Portugal
Create a mask for the NUTS2 regions that intersect with the Iberian Peninsula
Apply the mask to the dataset
Only keep land areas (not ocean) by applying the mask
This ensures that only land pixels are used in the area calculations


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 3. Data preparation and area-based calculations > Compute cell area
---
Then, we can calculate the area of each pixel taking into consideration the curvature of the earth (i.e., weighted by Latitude).

[If you want to know more](https://rdrr.io/cran/raster/man/area.html)

Calculate Pixel Area after applying the mask

(code-section-4)=


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 4. Analysis and visualisation of Iberian Peninsula land cover changes > Bar Charts
---
Having the area calculated and the NUTS 2 regions assigned to each pixel, we can now proceed to create the plots of the LC areas per class, by year. First, let's inspect the total area of each LC class in this AoI. We will use the original LC classes to highlight which ones show more changes.

Fuction to calculate class area with original classes
Select the data for the given year
Initialize a dictionary to store the area for each land cover class
Loop over each unique land cover class (lccs_class)
Create a mask for the current LC class
Calculate the total area for the current LC class
Calculate areas for 1992 and 2022

Combine area dictionaries into a single dictionary
Ensure lc_classes excludes NaN
Prepare area lists for plotting
Get class names and colors from the mapping dictionaries
Bar plot settings
Unique hatching patterns for visual separation by year
Plot bars for each year
Final plot customization


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 4. Analysis and visualisation of Iberian Peninsula land cover changes > Sankey Diagrams
---
In this section we take the land-cover maps for consecutive years and quantify how much area changes from one category to another. For each time step (e.g., 2009→2012, 2012→2015, 2015→2018), every grid cell is compared between the two years. If the class changes, we add that cell’s area (km²) to the corresponding “from → to” transition. This produces a table of transitions in km² for each period. The transitions are then aggregated to broader IPCC land-cover groups (e.g., Forest, Cropland, Grassland, Settlements, etc.) so the results are easier to interpret. Finally, these aggregated transitions are visualised as Sankey diagrams.

Each Sankey diagram corresponds to one period (for example 2009→2012). The left side lists the IPCC groups in the first year and the right side lists the IPCC groups in the second year. The ribbons represent land that changed from one group to another; the thickness of a ribbon is the area that changed (km²). Hovering a ribbon shows the same area in km² and also the percentage of the source group’s total change that went to that destination. Only changes between different groups are shown.

Weighted class transitions (km²) for consecutive years
Broadcast 1D or 2D area -> (lat, lon) like a single year slice
Class codes from CF-style flags; exclude nodata_code
LUT: raw code -> contiguous [0..nC-1], else -1
Aggregate class→class transitions to IPCC groups
Membership matrix M: (nC x nG)
Sankey: km² + ONLY % of source outflow;
Denominator for outflow %: sum of non-self transitions per source row (if ignore_self)
Labels: optionally show totals for sources (outflow only)
Node hover suppression (no tooltips on nodes)

Coloring graphs

Plot Sankey Diagrams


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 4. Analysis and visualisation of Iberian Peninsula land cover changes > Bar chart and Sankey Diagrams Analysis
---
The land cover comparison across the selected years (2009, 2012, 2015, 2018 and 2022) shows a clear and persistent increase in urban area. The bar chart provides the net view of these changes, showing a steady rise in the Urban class across all years, while other classes change more gradually without abrupt or implausible shifts. This supports the interpretation that the observed urban growth is systematic over time rather than being driven by isolated anomalies.

The Sankey transition diagrams provide further evidence that the observed urban growth follows realistic land-use change pathways. When analysed using the percentage share of each source class outflow, Agriculture→Settlement transitions account for 64.46% (2009–2012) and 65.91% (2012–2015) of total agricultural change in the respective periods. In the later intervals, this proportion decreases to 23.26% (2015–2018) and 13.49% (2018–2022), indicating that although agriculture remains a major contributor to new Settlement area in absolute terms, a smaller share of overall agricultural transitions is directed toward urban expansion in the later years.

Forest→Settlement transitions represent a consistently smaller proportion of total forest outflow, accounting for 3.58%, 6.81%, 15.50%, and 0.87% across the four respective periods. These values indicate that only a limited fraction of forest change results in direct urban conversion. Grassland→Settlement transitions account for 64.72%, 74.35%, 13.46%, and 3.22% of total grassland outflow across the same periods, indicating that a larger share of grassland change was directed toward urban expansion in the earlier periods than in the later years.

These outflow-based transition patterns confirm that urban growth primarily interacts with agricultural and grassland dynamics, while forest areas play a comparatively minor direct role in settlement expansion. This behaviour aligns with Feranec et al. (2010), who show that urbanisation flows predominantly originate from agricultural classes at the European scale. Similarly, Bagan and Yamagata (2014) identify cropland as the principal donor class to urban growth in global city analyses. In the Iberian Peninsula, Fernández-Nogueira and Corbelle-Rico (2018) report that increases in artificial surfaces occurred alongside stable or increasing forest area, further supporting the interpretation that urban growth does not primarily proceed through direct forest conversion.

Overall, the combined bar chart and Sankey analysis indicates that the dataset captures urbanisation patterns that are coherent across time, internally consistent, and aligned with established land-use change processes documented in the literature.

(code-section-5)=


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 5. Calculate the area percentage and the area percentage change for the most populated regions
---
Having identified general urbanisation trends in the Iberian Peninsula, we now focus on specific NUTS2 regions in greater detail. These regions were selected based on their inclusion of the largest urban areas in terms of population, according to the Urban Audit Indicators dataset from the European Commission (Eurostat).

The selected NUTS2 regions are:

- **Comunidad de Madrid**, which includes the city of Madrid (5,098,717 inhabitants in 2022), the capital of Spain;
- **Cataluña**, which includes Barcelona (3,755,512 inhabitants in 2022);
- **Área Metropolitana de Lisboa**, which includes Lisbon (1,872,036 inhabitants in 2022), the capital of Portugal;
- **Comunitat Valenciana**, which includes Valencia (1,417,464 inhabitants in 2022);
- **Norte**, which includes Porto (955,864 inhabitants in 2022).

For each of these regions, we analyse land cover dynamics in terms of **area percentages by IPCC classes**, in order to highlight broader, aggregated land cover changes. Additionally, we compare dataset results with EUROSTAT statistics to assess the consistency between datasets.


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 5. Calculate the area percentage and the area percentage change for the most populated regions > Calculation of the percentage area, absolute change, and relative percentage change for each IPCC class category
---
1. **Area Percentage Coverage**:
   *Example*: In 1992, forest land covered 35% of the total area, while urban areas occupied 10%. By 2022, forest coverage decreased to 30%, and urban areas expanded to 15%. This metric gives the proportion of the total land occupied by each land cover class.

2. **Absolute Percentage Difference**:
   *Example*: In 1992, 10% of the region was classified as agricultural land. By 2022, this had decreased to 8%. The absolute percentage difference in agricultural land coverage is −2% (from 10% in 1992 to 8% in 2022, representing a 2% decrease in total land area occupied by agriculture).

3. **Relative Percentage Difference**:
   *Example*: In 1992, 10% of the area was classified as wetlands. By 2022, wetlands accounted for 12% of the total area. The relative percentage difference is calculated as ((12−10)/10)∗100 = +20%. This means there was a 20% increase in wetland area relative to its size in 1992.

Filter the GeoDataFrame to include only the regions of interest
Expand cell_area ONCE outside the loops
Prepare latitude and longitude grids ONCE

Create region mask using shapely
Calculate IPCC class % coverage for a given year and region
Compute absolute and relative differences
Padded y-axis limits

Ploting fuctions
Plot absolute and relative differences (side-by-side bars)
Absolute difference
Relative difference

Year to check
Storage dictionary for later reuse
Calculate everything
For plotting (only cov_2009 vs cov_2022)
Calculate global plot limits
Plot results


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 5. Calculate the area percentage and the area percentage change for the most populated regions > Bar Chart Analysis
---
Across the five most populated NUTS2 regions clear land cover change patterns are observed between 2009 and 2022, especially in relation to urban expansion and agricultural decline.

- **Settlement/Urban areas** show a consistent increase in all regions, with the most pronounced growth occurring in Área Metropolitana de Lisboa and Comunidad de Madrid.

- **Agricultural land** has decreased across all regions, with the sharpest reduction seen in Área Metropolitana de Lisboa. The decline appears moderate in Comunidad de Madrid, Cataluña, and Comunitat Valenciana, suggesting a gradual conversion of agricultural land to urban or other uses.

- **Forest and grassland areas** remained mostly stable in several regions, with small increases in Comunitat Valenciana and Cataluña, indicating preservation or limited natural regeneration in non‑urbanised areas. However, in Norte and Valencia, forest cover declined despite only a minimal change of agricultural land. This pattern suggests that the reduction in forest area is linked to the expansion of settlements.

- **Wetlands and other land cover categories** continue to represent a negligible share of total area. Their relative and absolute changes are minimal, with little to no impact on the overall land use structure of the regions.


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 5. Calculate the area percentage and the area percentage change for the most populated regions > Comparison with EUROSTAT Data
---
To validate the consistency of the C3S Land Cover dataset in the context of urbanisation, we compared the percentage of land classified as Settlement with [EUROSTAT](https://ec.europa.eu/eurostat/databrowser/view/lan_lcv_ovw/default/table?lang=en&category=lan) artificial land statistics for the five most populated NUTS2 regions in the Iberian Peninsula. In addition, key non-urban land cover classes—Forest, Grassland, and Agriculture—are included in the comparison to provide context for the land cover changes associated with urban expansion.

EUROSTAT land-cover and land-use statistics are based on the [LUCAS survey](https://ec.europa.eu/eurostat/web/lucas/methodology), an in-situ survey in which field surveyors classify the observed land cover and visible land use at sampled points using harmonised LUCAS classifications. LUCAS explicitly separates **land cover**, meaning the physical cover of the Earth’s surface, from **land use**, meaning the socio-economic function of the land. The LUCAS land-cover classification is hierarchical and includes **A00 Artificial land** as one of the main land-cover categories.

In the LUCAS classification, the settlement/artificial-surface category is constructed from the following land-cover classes:

- **A10 Roofed built-up areas**, including buildings and greenhouses;
- **A20 Artificial non built-up areas**, including sealed area features such as yards, farmyards, cemeteries and car parking areas, as well as linear features such as streets, roads, railways and runways;
- **A30 Other artificial areas**, including bridges and viaducts, mobile homes, solar panels, power plants, electrical substations, pipelines, sewage plants and open dump sites.

The table below presents values for the five years for which EUROSTAT land cover data are available (2009, 2012, 2015, 2018 and 2022).

EUROSTAT reference values (percent)
Combined table for all classes and years


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 5. Calculate the area percentage and the area percentage change for the most populated regions > Comparison with EUROSTAT Data
---
and 2022).

EUROSTAT reference values (percent)
Combined table for all classes and years

```text
Region  Year  C3S Settlement (%)  EUROSTAT Artificial (%)  Δ Settlement (%)  C3S Forest (%)  EUROSTAT Woodland (%)  Δ Forest (%)  C3S Grassland (%)  EUROSTAT Grassland (%)  Δ Grassland (%)  C3S Agriculture (%)  EUROSTAT Cropland (%)  Δ Agriculture (%)
                    Cataluña  2009                3.29                      6.3             -3.01           45.66                   41.5          4.16               4.98                    13.1            -8.12                40.30                   21.7              18.60
                    Cataluña  2012                3.49                      6.4             -2.91           45.52                   44.8          0.72               4.99                    12.8            -7.81                40.21                   19.5              20.71
                    Cataluña  2015                3.71                      6.5             -2.79           45.42                   44.8          0.62               5.01                    13.4            -8.39                40.08                   18.4              21.68
                    Cataluña  2018                3.93                      5.3             -1.37           45.51                   49.6         -4.09               5.04                     8.9            -3.86                39.79                   24.4              15.39
                    Cataluña  2022                3.98                      5.9             -1.92           45.23                   48.2         -2.97               5.16                     9.2            -4.04                39.84                   19.9              19.94
         Comunidad de Madrid  2009                9.09                      9.7             -0.61           27.87                   15.3         12.57              14.63                    25.3           -10.67                43.16                   15.5              27.66
         Comunidad de Madrid  2012                9.62                     10.1             -0.48           27.71                   20.1          7.61              14.55                    24.4            -9.85                42.83                   14.0              28.83
         Comunidad de Madrid  2015               10.30                     10.6             -0.30           27.44                   24.1          3.34              14.43                    24.9           -10.47                42.51                   15.6              26.91
         Comunidad de Madrid  2018               12.23                     11.7              0.53           28.62                   30.6         -1.98              13.65                    20.4            -6.75                40.58                   17.7              22.88
         Comunidad de Madrid  2022               12.47                     11.5              0.97           28.80                   32.1         -3.30              13.50                    17.9            -4.40                40.39                   15.1              25.29
        Comunitat Valenciana  2009                3.16                      6.1             -2.94           36.00                   23.4         12.60               5.96                    13.4            -7.44                38.93                   11.4              27.53
        Comunitat Valenciana  2012                3.46                      6.2             -2.74           35.29                   29.2          6.09               5.94                    14.1            -8.16                38.62                   14.0              24.62
        Comunitat Valenciana  2015                3.86                      6.5             -2.64           35.45                   29.8          5.65               5.90                    14.0            -8.10                38.19                   12.2              25.99
        Comunitat Valenciana  2018                4.58


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 5. Calculate the area percentage and the area percentage change for the most populated regions > Comparison with EUROSTAT Data
---
5.65               5.90                    14.0            -8.10                38.19                   12.2              25.99
        Comunitat Valenciana  2018                4.58

                      7.2             -2.62           35.66                   35.5          0.16               5.77                     5.6             0.17                37.54                   25.8              11.74
        Comunitat Valenciana  2022                4.67                      6.9             -2.23           36.03                   34.0          2.03               5.67                     6.6            -0.93                37.43                   25.6              11.83
                       Norte  2009                4.33                      6.8             -2.47           50.91                   22.6         28.31               1.63                    17.7           -16.07                40.17                   14.0              26.17
                       Norte  2012                4.68                      6.9             -2.22           50.32                   24.1         26.22               1.70                    18.2           -16.50                40.36                   12.2              28.16
                       Norte  2015                5.08                      7.0             -1.92           49.55                   26.1         23.45               1.79                    16.9           -15.11                40.62                   11.5              29.12
                       Norte  2018                5.14                      9.4             -4.26           50.07                   28.9         21.17               1.77                    14.3           -12.53                39.99                   18.0              21.99
                       Norte  2022                5.25                      8.0             -2.75           50.32                   26.7         23.62               1.73                    13.3           -11.57                39.70                   19.1              20.60
Área Metropolitana de Lisboa  2009               15.46                     14.6              0.86           18.62                   11.4          7.22               0.12                    21.4           -21.28                50.80                   18.5              32.30
Área Metropolitana de Lisboa  2012               16.58                     15.4              1.18           18.58                   22.1         -3.52               0.12                    25.7           -25.58                49.77                   14.9              34.87
Área Metropolitana de Lisboa  2015               17.66                     15.8              1.86           18.62                   23.2         -4.58               0.12                    22.7           -22.58                48.64                   13.6              35.04
Área Metropolitana de Lisboa  2018               18.12                     18.9             -0.78           19.02                   28.1         -9.08               0.11                    16.5           -16.39                47.82                   17.4              30.42
Área Metropolitana de Lisboa  2022               18.25                     17.7              0.55           19.35                   24.5         -5.15               0.11                    22.2           -22.09                47.34                   15.9              31.44
```


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 5. Calculate the area percentage and the area percentage change for the most populated regions > Comparison with EUROSTAT Data
---
24.5         -5.15               0.11                    22.2           -22.09                47.34                   15.9              31.44
```

Across all regions, both datasets indicate a systematic increase in urban land over time, with comparable magnitudes and trajectories. Absolute differences in settlement/artificial land percentages are generally small, particularly in highly urbanised regions. For Comunidad de Madrid, differences remain below ±1% throughout the period, while Área Metropolitana de Lisboa shows differences mostly within ±2%, despite its higher overall urban fraction.

In regions with more dispersed or heterogeneous urban structures—such as Cataluña, Comunitat Valenciana, and Norte—C3S settlement values are consistently lower than EUROSTAT artificial land by approximately 1–3%. This offset is stable across years and likely reflects both definitional differences and differences in dataset construction, including spatial resolution. Because C3S is a satellite-derived 300 m gridded product, small or fragmented artificial surfaces may be represented differently from EUROSTAT/LUCAS artificial land, which is based on in-situ point observations aggregated statistically. Crucially, both datasets capture similar rates and directions of urban expansion, indicating that urbanisation dynamics are robustly identified despite these differences.

Between 2009 and 2022, urban land increased in all regions in both datasets, with comparable absolute changes. For example, in Comunidad de Madrid, C3S settlement increased by +3.38 percentage points (from 9.09% to 12.47%), while EUROSTAT artificial land increased by +1.8 percentage points (from 9.7% to 11.5%). Similarly, in Área Metropolitana de Lisboa, C3S reports an increase of +2.79 percentage points, compared to +3.1 percentage points in EUROSTAT. These comparable magnitudes indicate that both datasets capture a similar volume of land converted into urban use.

The land cover losses associated with this urban expansion are also consistent in direction. Across all regions, urban growth coincides primarily with reductions in agricultural and open land classes. For instance, in Cataluña, the cumulative increase in settlement between 2009 and 2022 (approximately +0.7 percentage points in C3S) corresponds to a decrease in agricultural land of around –0.5 percentage points, while EUROSTAT reports a comparable decline in cropland over the same period. Similar patterns are observed in Comunitat Valenciana and Norte, where modest but persistent urban gains are accompanied by declining agricultural shares in both datasets.

Although C3S and EUROSTAT differ in how agricultural, grassland, and mixed-use areas are classified, these differences do not alter the identification of urban-driven land take. Both datasets indicate that urban expansion primarily occurs at the expense of agricultural and open land, while forest areas remain comparatively stable. This agreement confirms that, in terms of urbanisation impact, the datasets provide equivalent signals of land cover loss.

Overall, the comparison demonstrates that the C3S Land Cover dataset is consistent with EUROSTAT in capturing the magnitude and evolution of urban expansion at the NUTS2 level. Minor systematic offsets in absolute values do not compromise the dataset’s suitability for analysing urban growth, land take, and associated land cover loss. For regional-scale monitoring of urbanisation processes, the C3S dataset therefore provides results that are comparable to official statistics, while offering the added benefit of spatially explicit, satellite-based observations.

(code-section-6)=


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > Analysis and results > 6. Main Takeaways
---
- The dataset demonstrates physical consistency with official sources, aligning well with [EUROSTAT statistics](https://ec.europa.eu/eurostat/databrowser/bookmark/63d3fd90-2f5b-4bec-99f3-4f88814e2624?lang=en) and previously reported European land-cover analyses (e.g. [Feranec et al., 2010](https://doi.org/10.1016/j.apgeog.2009.07.003); [Fernández-Nogueira & Corbelle-Rico, 2018](https://doi.org/10.3390/land7030099)). Between 2009 and 2022, urbanisation occurred across major NUTS2 regions, with settlement areas doubling in some cases. This expansion largely came at the expense of Agriculture and Grassland classes. The observed magnitude and direction of change are consistent with broader European urbanisation trends identified using the GHSL dataset ([Alberti et al., 2019](https://publications.jrc.ec.europa.eu/repository/handle/JRC116711)). Small discrepancies (<5%) with EUROSTAT artificial land statistics do not compromise the dataset’s reliability for identifying overall land-use change trends, though users focusing on very localised patterns should interpret results with caution.

- Urban expansion mainly replaced agricultural and open land, while Forest areas remained largely stable over the study period. Settlements increased their share of total land area across regions and remain a minority land cover class. These patterns are consistent with spatial trade-off dynamics reported in global urban land-cover change analyses, where urban growth frequently draws from agricultural land rather than direct forest conversion ([Bagan & Yamagata, 2014](https://doi.org/10.1088/1748-9326/9/6/064015)).

- The use of both detailed and aggregated IPCC classes provided a comprehensive view of land cover dynamics, balancing precision and generalisation. Although class aggregation introduces some uncertainty (e.g. between Agriculture and Grassland), it enables clearer identification of dominant transitions such as urban expansion, consistent with structured land-cover change assessment approaches reported in the literature.

- Minor classification differences between datasets (e.g. coding of mixed-use or greenhouse areas) can introduce uncertainty. Nonetheless, the dataset maintains strong performance in identifying urban expansion trends, in line with consistency and accuracy assessments of global land-cover products ([Zhao et al., 2023](https://doi.org/10.3390/rs15092285)).


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

The CDS catalogue entry for the data used were:
* Land cover classification gridded maps from 1992 to present derived from satellite observations:
    (https://cds.climate.copernicus.eu/datasets/satellite-land-cover?tab=overview)

* Product User Guide and Specification of the dataset [version 2.1](https://dast.copernicus-climate.eu/documents/satellite-land-cover/D5.3.1_PUGS_ICDR_LC_v2.1.x_PRODUCTS_v1.1.pdf) and [version 2.0](https://dast.copernicus-climate.eu/documents/satellite-land-cover/D3.3.11-v1.0_PUGS_CDR_LC-CCI_v2.0.7cds_Products_v1.0.1_APPROVED_Ver1.pdf)

Additional resources:
* [Eurostat NUTS](https://ec.europa.eu/eurostat/web/nuts) (Nomenclature of territorial units for statistics)

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)


EQC Quality Assessment: "Satellite Land Cover completeness for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: consistency-assessment_q01  |  Category: Satellite_ECVs
Section: Satellite Land Cover completeness for Spatial Planning and Land Management > ℹ If you want to know more > References
---
[Feranec, J., Jaffrain, G., Soukup, T., & Hazeu, G. (2010). Determining changes and flows in European landscapes 1990–2000 using CORINE land cover data. Applied geography, 30(1), 19-35.](https://doi.org/10.1016/j.apgeog.2009.07.003)

[Fernández-Nogueira, D., & Corbelle-Rico, E. (2018). Land use changes in Iberian Peninsula 1990–2012. Land, 7(3), 99.](https://doi.org/10.3390/land7030099)

[Eurostat, Statistical Atlas](https://ec.europa.eu/statistical-atlas/viewer/?ch=gridvizChapter&mids=BKGCNT,totalPop21,CNTOVL&o=1,1,0.7&center=40.94812,-2.2287,5&lcis=totalPop21&)

[Bagan, H., & Yamagata, Y. (2014). Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells. Environmental Research Letters, 9.](https://doi.org/10.1088/1748-9326/9/6/064015)

[Alberti, V., Alonso Raposo, M., Attardo, C., Auteri, D., Ribeiro Barranco, R., Batista E Silva, F., Benczur, P., Bertoldi, P., Bono, F., Bussolari, I., Louro Caldeira, S., Carlsson, J., Christidis, P., Christodoulou, A., Ciuffo, B., Corrado, S., Fioretti, C., Galassi, M., Galbusera, L., Gawlik, B., Giusti, F., Gomez Prieto, J., Grosso, M., Martinho Guimaraes Pires Pereira, A., Jacobs, C., Kavalov, B., Kompil, M., Kucas, A., Kona, A., Lavalle, C., Leip, A., Lyons, L., Manca, A., Melchiorri, M., Monforti-Ferrario, F., Montalto, V., Mortara, B., Natale, F., Panella, F., Pasi, G., PerpiÃ±a Castillo, C., Pertoldi, M., Pisoni, E., Roque Mendes Polvora, A., Rainoldi, A., Rembges, D., Rissola, G., Sala, S., Schade, S., Serra, N., Spirito, L., Tsakalidis, A., Schiavina, M., Tintori, G., Vaccari, L., Vandyck, T., Vanham, D., Van Heerden, S., Van Noordt, C., Vespe, M., Vetters, N., Vilahur Chiaraviglio, N., Vizcaino, M., Von Estorff, U. and Zulian, G., The Future of Cities, Vandecasteele, I., Baranzelli, C., Siragusa, A. and Aurambout, J. editor(s), EUR 29752 EN, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-76-03847-4, doi:10.2760/375209, JRC116711.](https://publications.jrc.ec.europa.eu/repository/handle/JRC116711)

[Zhao, T., Zhang, X., Gao, Y., Mi, J., Liu, W., Wang, J., Jiang, M., & Liu, L. (2023). Assessing the Accuracy and Consistency of Six Fine-Resolution Global Land Cover Products Using a Novel Stratified Random Sampling Validation Dataset. Remote. Sens., 15, 2285.](https://doi.org/10.3390/rs15092285)


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Quality assessment question
---
* **Is the dataset accurate and consistent for the analysis of urbanisation trends in the Iberian Peninsula?**

Land Cover data is an invaluable resource for a wide range of fields, from climate change research to urban planning. Land Cover products that provide historical timelines enable scientists, policymakers, and planners to understand and analyse the transformation of land cover over recent decades ([Vargo et al, 2013](https://doi.org/10.1016/j.jenvman.2012.10.007); [Chang et al., 2018](https://doi.org/10.1088/1755-1315/113/1/012087)).

This notebook will access the ***Land cover classification gridded maps from 1992 to present derived from satellite observations*** (henceforth, LC) data from the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S), and analyse the spatial patterns of the LC over a given Area of Interest (AoI) and time.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Quality assessment statement
---
These are the key outcomes of this assessment

The dataset maintains strong temporal continuity, with annual updates ensuring a smooth and reliable representation of land cover changes over time. While breakpoints were identified, they generally did not indicate major disruptions, reinforcing the dataset’s stability for long-term trend analysis.

The presence of breakpoints does not necessarily indicate abrupt landscape shifts but rather highlights the sensitivity of detection methods to gradual changes. This suggests that while breakpoints can help refine analysis, their impact on overall trends remains limited, emphasising the dataset’s resilience to minor variations.

For the specific land cover type analysed, the dataset exhibits a consistent ability to capture underlying trends. The similarity in results across segmented and total trends suggests that the data structure is well-calibrated, minimising distortions that could arise from classification inconsistencies or methodological biases.

```

![Urbanization_Map_Series.png](attachment:Urbanization_Map_Series.png)


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Methodology
---
**This Use Case comprises the following steps:**

**[](code-section-1)**

**[](code-section-2)**

**[](code-section-3)**

**[](code-section-4)**

**[](code-section-5)**


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data
---
Before we begin we must prepare our environment. This includes installing the Application Programming Interface (API) of the CDS, and importing the various python libraries that we will need.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data > Install CDS API
---
To install the CDS API, run the following command. We use an exclamation mark to pass the command to the shell (not to the Python interpreter).
If you already have the CDS API installed, you can skip or comment this step.

##### Import all the libraries/packages

We will be working with data in NetCDF format. To best handle this type of data we will use libraries for working with multidimensional arrays, in particular Xarray.
We will also need libraries for plotting and viewing data.

Import Standard Libraries
Import Numerical & Statistical Libraries
import ruptures as rpt
Import Geospatial Libraries
Import Visualization Libraries
Import External Tools
Set Matplotlib Style
Set the CDSAPI location

##### Data Overview

To search for data, visit the CDS website: http://cds.climate.copernicus.eu Here you can search for 'Satellite observations' using the search bar. The data we need for this tutorial is the ***Land cover classification gridded maps from 1992 to present derived from satellite observations***. This catalogue entry provides global Land Cover Classification (LCC) maps with a very high spatial resolution, with a L4 processing level, on an annual basis with a one-year delay, following the [Global Climate Observing System (GCOS) convention requirements](https://library.wmo.int/records/item/58111-the-2022-gcos-ecvs-requirements-gcos-245). LULC maps correspond to a global classification scheme, encompassing 22 classes.

The dataset consists of 2 versions (v2.0.7 produced by the European Space Agency (ESA) Climate Change Initiative (CCI) and v2.1.1 produced by Copernicus Climate Change Service (C3S)).

Data specifications for this use case:
* **Years:** 1992 to 2022
* **Version:** v2.0.7 before 1992 and v2.1.1 after 2016
* **Format:** Zip files

At the end of the download form, select “**Show API request**”. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook. Having copied the API request, running it will retrieve and download the data you requested into your local directory. However, before you run it, the **terms and conditions** of this particular dataset need to have been accepted directly at the CDS website. The option to view and accept these conditions is given at the end of the download form, just above the “**Show API request**” option. In addition, it is also useful to define the time period and AoI parameters and edit the request accordingly, as exemplified in the cells below.

Years to download
List of requests to retrieve data

Download and regionalize by AoI

Inspect the database


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data > Install CDS API
---
exemplified in the cells below.

Years to download
List of requests to retrieve data

Download and regionalize by AoI

Inspect the database

```text
<xarray.Dataset> Size: 7GB
Dimensions:              (year: 31, latitude: 3600, longitude: 5040, bounds: 2)
Coordinates:
  * year                 (year) int64 248B 1992 1993 1994 ... 2020 2021 2022
  * latitude             (latitude) float64 29kB 45.0 45.0 44.99 ... 35.0 35.0
  * longitude            (longitude) float64 40kB -9.999 -9.996 ... 3.996 3.999
    lat_bounds           (latitude, bounds, longitude) float64 290MB dask.array<chunksize=(1200, 1, 1680), meta=np.ndarray>
    lon_bounds           (longitude, bounds) float64 81kB dask.array<chunksize=(5040, 2), meta=np.ndarray>
    time_bounds          (year, bounds, longitude) datetime64[ns] 2MB dask.array<chunksize=(1, 2, 5040), meta=np.ndarray>
Dimensions without coordinates: bounds
Data variables:
    lccs_class           (year, latitude, longitude) uint8 562MB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    processed_flag       (year, latitude, longitude) float32 2GB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    current_pixel_state  (year, latitude, longitude) float32 2GB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    observation_count    (year, latitude, longitude) uint16 1GB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    change_count         (year, latitude, longitude) uint8 562MB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    crs                  (year, longitude) int32 625kB dask.array<chunksize=(1, 5040), meta=np.ndarray>
Attributes: (12/38)
    id:                         ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-v2.0.7cds
    title:                      Land Cover Map of ESA CCI brokered by CDS
    summary:                    This dataset characterizes the land cover of ...
    type:                       ESACCI-LC-L4-LCCS-Map-300m-P1Y
    project:                    Climate Change Initiative - European Space Ag...
    references:                 http://www.esa-landcover-cci.org/
    ...                         ...
    geospatial_lon_max:         180
    spatial_resolution:         300m
    geospatial_lat_units:       degrees_north
    geospatial_lat_resolution:  0.002778
    geospatial_lon_units:       degrees_east
    geospatial_lon_resolution:  0.002778
```

(code-section-2)=


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Compute urban area for each NUTS 2 region
---
To identify changes in LC patterns, in this user question, NUTS 2 will be used, providing the information regarding the main regions of the Iberian Peninsula.

The [NUTS](https://ec.europa.eu/eurostat/web/nuts) are a hierarchical system divided into 3 levels. NUTS 1 correspond to major socio-economic regions, NUTS 2 correspond to basic regions for the application of regional policies, and NUTS 3 correspond to small regions for specific diagnoses. Additionally a NUTS 0 level, usually co-incident with national boundaries is also available. The NUTS legislation is periodically amended; therefore multiple years are available for download.

The step below masks the Land Cover data according to the NUTS 2 boundaries and calculate the area of each pixel (weighted by the cosine of Latitude). For each NUTS 2, we proceed with the analysis and visual inspection of Land Cover areas per class and corresponding percentages during the elected period.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Mask regions
---
First, we need to establish the geometry of the NUTS region (level 2) in order to make the corresponding statistics.

Configure Dask
Define CRS and bounding box for Iberian Peninsula
Load and filter GeoDataFrame
Ensure dataset CRS is set
Ensure dataset coordinates overlap with the filtered regions
Subset dataset to valid ranges
Check subsetted dataset dimensions
Create the regionmask
Create a 2D mask


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Compute cell area
---
Then, we can calculate the area of each pixel taking into consideration the curvature of the earth (i.e., weighted by the cosine of Latitude).

Scaling factor for conversion (constant longitude resolution)
Use the latitude values directly from the dataset
Calculate the difference between consecutive latitude values
Convert latitude differences to kilometers
Compute the grid cell area for each latitude
Assign attributes to the grid cell area
Add the grid cell area as a coordinate to the dataset


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Select Urban Classes and Prepare Dataset
---
Define urban classes
Create a mask for urban areas
Masked urban area using cell area
Stack latitude and longitude into a single dimension
Create a stacked mask and align dimensions
Attach the stacked mask to the dataset
Group by regions and compute urban area for each year


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Compute urban area per region and year
---
Ensure valid region indexing and naming
Save growth data by year
Add results to the list
Convert results to a DataFrame
Add geometry to results_df
Ensure results_df is a GeoDataFrame


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Map urban percentage coverage over-time by NUTS regions in the AoI
---
Reproject the GeoDataFrame to a projected CRS (EPSG:3035 is a good choice for Europe)
Calculate the total area of each region (in square kilometres)
Ensure 'Year' is converted to string if needed
Calculate Urban Percentage
Pivot the data to have years as columns
Extract years for plotting
Normalize color scale across all maps based on percentage coverage
Plot settings
Plot each year in a separate subplot
Hide unused subplots
Step 8: Adjust layout
Allocate space for the color bar
Create a single ScalarMappable object for the common colorbar


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Map Analysis
---
- Over the 28-year period, all regions maintain a consistent pattern of growth.

- Regions such as Comunidad Madrid and Área Metropolitana de Lisboa consistently exhibit the highest levels of urban percentage coverage.

- No negative fluctuations were observed

- The biggest changes are observed between 2016-2017.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Potential Drivers and Methodological Considerations in Urbanisation Trends in the Iberian Peninsula (1992–2022)
---
Urbanisation in the Iberian Peninsula over the last three decades has been influenced by a combination of **economic shifts**, **demographic changes**, and **policy decisions**. Before analysing trends, it is essential to account for abrupt shifts that may distort trend estimates. These shifts, often referred to as breakpoints, can result from:

**Economic Drivers**

- Economic Growth and Real Estate Booms:  The rapid expansion of urban areas in **Madrid, Barcelona, and Lisbon** was driven by economic growth and increased investment in housing and infrastructure, particularly during the **1996–2007 real estate boom** [(González & Leal, 2018)](https://doi.org/10.1016/j.landusepol.2018.05.023). The expansion of suburban areas led to the rise of peri-urban developments in regions like **Valencia and Porto** [(Silva et al., 2017)](https://doi.org/10.1016/j.cities.2017.04.002).

- Economic Crisis (2008–2013): The 2008 financial crisis led to a slowdown in urban expansion, particularly in southern Spain, where unfinished developments and ghost towns became a common sight [(Martínez & García, 2015)](https://doi.org/10.1016/j.habitatint.2015.01.010). However, in some areas, reduced construction pressure allowed for the recovery of natural land cover [(Palmero-Iniesta et al., 2021)](https://api.semanticscholar.org/CorpusID:238829360).

**Demographic and Social Changes**

- Rural Depopulation:  Migration from rural areas to urban centers, particularly among younger populations, has led to declining populations in **Extremadura, Castilla y León, and Alentejo**, accelerating the abandonment of agricultural lands and increasing the expansion of urban areas [(Sánchez et al., 2019)](https://doi.org/10.1016/j.geoforum.2019.06.015).

- Tourism and Second-Home Development:  The rise of tourism-driven urbanisation has significantly reshaped coastal regions like **the Costa del Sol, Algarve, and the Balearic Islands**, where seasonal housing developments have expanded rapidly [(Rullan, 2014)](https://doi.org/10.1016/j.landusepol.2014.02.009).

- Aging Population:  Aging populations in rural regions have led to increased urban migration, further depopulating countryside areas and concentrating populations in cities like **Seville, Valencia, and Bilbao** [(Gutiérrez et al., 2016)](https://doi.org/10.1016/j.jrurstud.2016.03.008).

**Infrastructure and Policy Influences**

- EU-Funded Infrastructure Projects:  Investments in transport networks, such as the expansion of **high-speed rail (AVE) in Spain and Alfa Pendular in Portugal**, have facilitated suburbanisation and commuting from secondary urban centers [(López et al., 2017)](https://doi.org/10.1016/j.jtrangeo.2017.02.005).

- Urban Planning Policies:  The implementation of urban growth controls and greenbelt policies in cities like **Barcelona and Porto** has influenced spatial expansion patterns, sometimes leading to increased land prices and informal settlements [(Silva & Fernandes, 2019)](https://doi.org/10.1016/j.landusepol.2019.04.012).

- Smart Cities and Sustainability Initiatives: Recent efforts to create more sustainable urban environments, such as Madrid’s **Madrid Central low-emission zone**, have shaped urbanisation patterns, promoting compact city models [(Delgado & Romero, 2021)](https://doi.org/10.1016/j.cities.2021.103120).

**Methodology**

- Certain shifts in urban land cover may be related to data collection and processing rather than actual urban expansion. For instance, transitions between different satellite sensors can introduce artificial breaks in the time series [(Chelali et al., 2019)](https://doi.org/10.1109/JURSE.2019.8808967).

- **The LC dataset is generated from a multi-sensor surface reflectance time series derived from several satellite missions. As sensor technology evolved, different instruments were used to produce consistent global composites.**

<div style="display: flex; justify-content: center;">