Mapping fractional woody cover in an extensive semi-arid woodland area at different spatial grains with Sentinel-2 and very high-resolution data

Woody canopy cover is an essential variable to characterize and monitor vegetation health, carbon accumulation and land–atmosphere exchange processes. Remote sensing-based global woody and forest cover maps are available, yet with varying qualities. In arid and semi-arid areas, existing global products often underestimate the presence of woody cover due to the sparse woody cover and bright soil background. Case studies on smaller regions have shown that a combination of collected field data and medium-to-high resolution free satellite data (e.g., Landsat / Sentinel-2) can provide woody cover estimates with practically-sufficient accuracies. However, most earlier studies focused on comparably small regions and relied on costly field data. Here, we present a fully remote sensing-based work-flow to derive woody cover estimates over an area covering more than 0.5 million km2. The work-flow is showcased over the Zagros Mountains, a semi-arid mountain range covering western Iran, the northeast of Iraq and some smaller fraction of southeast Turkey. We use the Google Earth Engine to create homogeneous Sentinel-2 mosaics of the region using data from several years. These data are combined with reference woody cover values derived by a semi-automatic procedure from Google® and Bing® very high resolution (VHR) imagery. Several random forest (RF) models at different spatial grains were trained and at each grain validated with iterative splits of the reference data into training and validation sets (100 repetitions). Best results (considering the trade-off between model performance and spatial detail) were obtained for the model with 40 m spatial grain which showed stable relationships between the VHR-derived reference data and the Sentinel-2 based estimates of woody cover density. The model resulted in median values of coefficient of determination (R2) and RMSE of 0.67 and 0.11, respectively. Our work-flow is potentially also applicable to other arid and semi-arid regions and can contribute to improve currently available global woody cover products, which often perform poorly in semi-arid and arid regions. Comparisons between our woody cover products with common global woody or forest-cover products indicate a clear superiority of our approach. In future studies, these results may be further improved by taking into account regional differences in the drivers of woody-cover patterns along the environmental gradient of the Zagros area

Detecting semi-arid forest decline using time series of Landsat data

Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations

Detecting semi-arid forest decline using time series of Landsat data

ABSTRACTDetecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations

Detecting semi-arid forest decline using time series of Landsat data

Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations.</p

A novel linear spectral unmixing-based method for tree decline monitoring by fusing UAV-RGB and optical space-borne data

Remote sensing-assisted monitoring of forest health entails methods that can provide up-to-date and accurate information on decline and mortality of individual trees, while maintaining time and cost efficiency. However, the trade-off of applying consumer-grade UAV-RGB data as the most affordable and accessible data source at the catchment level is constrained by its poor spectral information content. We developed a method based on the fusion of UAV-RGB data with space-borne Sentinel-2 Multispectral Instrument (MSI) at the level of tree crowns, with the specific target of supporting studies on semi-arid tree decline. We applied linear spectral unmixing (Spectral Unmixing-Based data Fusion method, LSUBF) by considering a limited number of endmembers and calculating the abundances (fractional covers) from the UAV data, and evaluated the results by high-resolution MSI space-borne data including SPOT-6 (1.5 m spatial resolution) and PlanetScope (3 m spatial resolution). This method suggested an increase in the coefficient of determination of the applied generalized additive model for decline severity estimation at tree crown level from 0.61 to 0.69, while it was improved from 0.70 to 0.91 when fitting a non-parametric random forest model. The results of sensitivity analysis demonstrated that the additional spectral information obtained from the proposed method results in higher accuracy in estimating decline severity. We suggest this method as a cost-effective alternative to monitor periodical tree decline, in particular across semi-arid ecosystems.</p