In this study we evaluated the applicability of a space-borne hyperspectral sensor, Hyperion, to resolve for chlorophyll a (Chl a) concentration in Lake Atitlan, a tropical mountain lake in Guatemala. In situ water quality samples of Chl a concentration were collected and correlated with water surface reflectance derived from Hyperion images, to develop a semi-empirical algorithm. Existing operational algorithms were tested and the continuous bands of Hyperion were evaluated in an iterative manner. A third order polynomial regression provided a good fit to model Chl a. The final algorithm uses a blue (467 nm) to green (559 nm) band ratio to successfully model Chl a concentrations in Lake Atitlán during the dry season, with a relative error of 33%. This analysis confirmed the suitability of hyperspetral-imagers like Hyperion, to model Chl a concentrations in Lake Atitlán. This study also highlights the need to test and update this algorithm with operational multispectral sensors such as Landsat and Sentinel-2.

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.

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.

Given the rapidity and intensity of anthropogenic impacts on natural systems, assessing the effectiveness of current protected areas in preserving biodiversity is especially important in Mesoamerica and the Caribbean, which contain a wide array of species and ecosystems. In light of the growing need to consider climate change in policymaking, combining climate change projections with biodiversity maps allows scientists and decision-makers to understand possible climate change impacts on biodiversity. In this study, we use GIS to identify spatial relationships between regional climate change models and species habitat ranges for Mesoamerica and the Caribbean. Evaluating possible effects of climate change in terms of temperature and precipitation involves three factors: historical averages, historical ranges, and future averages. Because different ecosystems and species exist at different temperature and precipitation ranges, we consider “comfort zones” of each area. We develop a quantitative, spatial measurement of climate change intensity of each area by calculating the difference between historical and future averages and dividing that difference by the area's comfort zone, at a spatial resolution of 1km2. The result is a normalized grid of projected climate change severity. According to the modeling results, should worst case scenario conditions prevail, by the 2020s, the Caribbean coasts of Costa Rica, Honduras, Nicaragua, Panama, and the Dominican Republic, will be significantly impacted by climate change. By the 2080s, all of the ecosystems and species of Central America and the Dominican Republic may be subjected to conditions well outside of their traditional comfort zone, while most of Mexico's ecosystems and species are at lower risk of severe climate change impacts. Integrating species richness data with the climate change severity analyses identifies critical areas that may require specific interventions to facilitate the adaptation of species to climate change. The information generated points not only to the utility of current protected areas, but is also useful in guiding the development of new protected areas and biological corridors, for the reduction of the potential impacts of future climate change.

Increased tourism in Belize over the last decade and the growth of the local population have led to coastal development and infrastructure expansion. Land use alteration and anthropogenic activity may change the sediment and nutrient loads in coastal systems, which can negatively affect ecosystems via mechanisms such as reducing photosynthetically active radiation fields, smothering sessile habitats, and stimulating eutrophication events. Accurate monitoring and prediction of water quality parameters such as Total Suspended Sediments (TSS), are essential in order to understand the influence of land-based changes, climate, and human activities on the coastal systems and devise strategies to mitigate negative impacts. This study implements machine learning algorithms such as Random Forests (RF), Extreme Gradient Boosting (XGB), and Deep Neural Networks (DNN) to estimate TSS using Sentinel-2 reflectance data in the Belize Coastal Lagoon (BCL) and validates the results using TSS data collected in situ. DNN performed the best and estimated TSS with a testing R² of 0.89. Time-series analysis was also performed on the BCL’s TSS trends using Bayesian Changepoint Detection (BCD) methods to flag anomalously high TSS spatio-temporally, which may be caused by dredging events. Having such a framework can ease the near-real-time monitoring of water quality in Belize, help track the TSS dynamics for anomalies, and aid in meeting and maintaining the sustainable goals for Belize.