Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

Mangrove forests are a biodiverse ecosystem known for a wide variety of crucial ecological services, including carbon sequestration, coastal erosion control, and prevention of saltwater intrusion. Given the ecological importance of mangrove forests, a comprehensive and up-to-date mangrove extent mapping at broad geographic scales is needed to define mangrove forest changes, assess their implications, and support restoration activities and decision making. The main objective of this study is to evaluate mangrove classifications derived from a combination of Landsat-8 OLI, Sentinel-2, and Sentinel-1 observations using a random forest (RF) machine learning (ML) algorithm to identify the best approach for monitoring Guyana’s mangrove forests on an annual basis. Algorithm accuracy was tested using high-resolution planet imagery in Collect Earth Online. Results varied widely across the different combinations of input data (overall accuracy, 88–95%; producer’s accuracy for mangroves, 50–87%; user’s accuracy for mangroves, 13–69%). The combined optical–radar classification demonstrated the best performance with an overall accuracy of 95%. Area estimates of mangrove extent ranged from 908.4 to 3645.0 hectares. A ground-based validation exercise confirmed the extent of several large, previously undocumented areas of mangrove forest loss. The results establish that a data fusion approach combining optical and radar data performs marginally better than optical-only approaches to mangrove classification. This ML approach, which leverages free and open data and a cloud-based analytics platform, can be applied to mapping other areas of mangrove forests in Guyana. This approach can also support the operational monitoring of mangrove restoration areas managed by Guyana’s National Agricultural and Research Extension Institute (NAREI).

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has provided since 2014. Radiometric Terrain Correction (RTC) data are key entry-level products for multiple applications ranging from ecosystem to hazard monitoring. Various open-source software packages exist to create RTC products from Single Look Complex (SLC) or Ground Range Detected (GRD) level SAR data, including the Interferometric SAR Computing Environment (ISCE), and the Sentinel-1 Toolbox from the European Space Agency (SNAP 8). Despite the growing availability of RTC software solutions, little work has been performed to identify differences between RTC products generated using different software packages. This work evaluates several Sentinel-1 RTC products and two other Sentinel-1 Analysis Ready Data (ARD) to address the following questions: (1) Which software provides the most accurate RTC product? and (2) how appropriate for analysis are other non-RTC products that are readily available? The RTCs are produced with GAMMA, ISCE-2, and SNAP 8. The other two ARD products evaluated consisted of an angular-based radiometric slope correction produced in Google Earth Engine (GEE) following Vollrath et al., and the Sentinel-1 GRD product. Products are evaluated across 10 sites in a single image approach for (1) radiometric calibration, (2) geometric corrections, and for (3) geolocation quality. In addition, time-series stacks over two sites representing varied terrain and ecosystems are evaluated. The GAMMA-derived RTC product implemented by the Alaska Satellite Facility (ASF) is used as a reference for some of the time-series metrics. The results provide direct guidance and recommendations about the quality of the RTC and ARD products obtained from open source methods. The results indicate that it is not recommended to use the GRD product with no radiometric or geometric corrections for any applications given low performance in multiple metrics. The radiometric calibration and geometric corrections have overall good performance for all open-source solutions, only the non-RTC products (Vollrath et al. and GRD) portray some significant variances in steep terrain. The geolocation assessment indicated that the GRD product has the most significant displacement errors, followed by SNAP 8 with Digital Elevation Model (DEM) matching, and ISCE-2. RTCs created without DEM-matching performed better for both GAMMA and SNAP 8. The time-series results indicate that SNAP 8 products align more closely to GAMMA products than other open-source software in terms of radiometric and geometric quality. This understanding of software performance for SAR image processing is key to designing the affordable and scalable solutions needed for the operational application of SAR Sentinel-1 data.

Airborne laser scanning has proven useful for rapid and extensive documentation of historic cultural landscapes after years of applications mapping natural landscapes and the built environment. The recent integration of unoccupied aerial vehicles (UAVs) with LiDAR systems is potentially transformative and offers complementary data for mapping targeted areas with high precision and systematic study of coupled natural and human systems. We report the results of data capture, analysis, and processing of UAV LiDAR data collected in the Maya Lowlands of Chiapas, Mexico in 2019 for a comparative landscape study. Six areas of archaeological settlement and long-term land-use reflecting a diversity of environments, land cover, and archaeological features were studied. These missions were characterized by areas that were variably forested, rugged, or flat, and included pre-Hispanic settlements and agrarian landscapes. Our study confirms that UAV LiDAR systems have great potential for broader application in high-precision archaeological mapping applications. We also conclude that these studies offer an important opportunity for multi-disciplinary collaboration. UAV LiDAR offers high-precision information that is not only useful for mapping archaeological features, but also provides critical information about long-term land use and landscape change in the context of archaeological resources.

Understanding where forest loss occurs and the resulting carbon emissions is a critical component of assessing national carbon budgets. To complement existing greenhouse gas (GHG) guidance and evaluate input datasets used in emissions estimation, SERVIR—a joint USAID and NASA initiative—implemented the SERVIR CArbon Pilot (S-CAP) project. This study focuses on the variability and reliability of land cover and biomass datasets that serve as inputs for such calculations. Seventeen aboveground biomass and twelve land cover change datasets were analyzed to characterize the variability in forest cover loss and biomass estimates for Guatemala, Nepal, and Zambia. Forest loss estimates varied substantially, ranging from 20,733 to 441,227 ha/yr in Guatemala, 1738 to 385,087 ha/yr in Nepal, and 6141 to 1,902,957 ha/yr in Zambia. Biomass estimates also differed widely depending on the dataset and forest mask applied: mean values ranged from 54.6 to 293.3 tons/ha across countries and periods. Accuracy assessments using national reference data for forest changes ranged from 67 to 97%, while National Forest Inventory biomass estimates diverged notably from global products. The ensemble approach highlights how differences in input datasets, particularly in forest extent and biomass magnitude, can propagate through emissions calculations. These findings underscore the importance of understanding and evaluating dataset variability prior to national carbon reporting and emissions estimation.