Search Results (5)

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to its intricate vegetation mosaics and the limited availability of reliable ground reference data. Accurate land cover maps are urgently needed to support conservation and sustainable land-use planning in this frontier region, especially for distinguishing critical vegetation types such as Amazon rainforest, Cerradão (dense woodland), and Savanna. In this study, we produce the first high-resolution land cover map of the CAT by integrating PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data within a U-net deep learning framework. This data fusion approach enables improved discrimination of ecologically similar vegetation types across heterogeneous landscapes. We systematically compare classification performance across single-sensor and fused datasets, demonstrating that multi-source fusion significantly outperforms single-source inputs. The highest overall accuracy was achieved using the fusion of PlanetScope, Sentinel-2, and Sentinel-1 (F1 = 0.85). Class-wise F1 scores for the best-performing model were 0.91 for Amazon Forest, 0.76 for Cerradão, and 0.76 for Savanna, indicating robust model performance in distinguishing ecologically important vegetation types. According to the best-performing model, 50.3% of the study area remains covered by natural vegetation. Cerradão, although ecologically important, covers only 8.4% of the landscape and appears highly fragmented, underscoring its vulnerability. These findings highlight the power of deep learning and multi-sensor integration for fine-scale land cover mapping in complex tropical ecotones and provide a critical spatial baseline for monitoring ecological changes in the CAT region. Full article

Seagrasses provide ecosystem services worth USD 2.28 trillion annually. However, their direct threats and our incomplete knowledge hamper our capabilities to protect and manage them. This study aims to evaluate if the NICFI Satellite Data Program basemaps could map Seychelles’ extensive seagrass meadows, directly supporting the country’s ambitions to protect this ecosystem. The Seychelles archipelago was divided into three geographical regions. Half-yearly basemaps from 2015 to 2020 were combined using an interval mean of the 10th percentile and median before land and deep water masking. Additional features were produced using the Depth Invariant Index, Normalised Differences, and segmentation. With 80% of the reference data, an initial Random Forest followed by a variable importance analysis was performed. Only the top ten contributing features were retained for a second classification, which was validated with the remaining 20%. The best overall accuracies across the three regions ranged between 69.7% and 75.7%. The biggest challenges for the NICFI basemaps are its four-band spectral resolution and uncertainties owing to sampling bias. As part of a nationwide seagrass extent and blue carbon mapping project, the estimates herein will be combined with ancillary satellite data and contribute to a full national estimate in a near-future report. However, the numbers reported showcase the broader potential for using NICFI basemaps for seagrass mapping at scale. Full article

Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified after September 2020 at Amazonas, Mato Grosso, and Colombia jurisdictions using Landsat-8 and PlanetScope NICFI satellite imagery. Non-photosynthetic vegetation (NPV), green vegetation (GV), and shade fractions were calculated for each image and sensor using spectral mixture analysis in Google Earth Engine. The results showed that PlanetScope NICFI data provided more regular and higher-spatial-resolution observations of blowdown areas than Landsat-8, allowing for more accurate characterization of post-disturbance vegetation recovery. Specifically, NICFI data indicated that just four months after the blowdown event, nearly half of ΔNPV, which represents the difference between the NPV after blowdown and the NPV before blowdown, had disappeared. ΔNPV and GV values recovered to pre-blowdown levels after approximately 15 months of regeneration. Our findings highlight that the precise timing of blowdown detection has huge implications on quantification of the magnitude of damage. Landsat data may miss important changes in signal due to the difficulty of obtaining regular monthly observations. These findings provide valuable insights into vegetation recovery dynamics following blowdown events. Full article

PlanetScope (PL) high-resolution composite base maps have recently become available within Google Earth Engine (GEE) for the tropical regions thanks to the partnership between Google and the Norway’s International Climate and Forest Initiative (NICFI). Object-based (OB) image classification in the GEE environment has increased rapidly due to the broadly recognized advantages of applying these approaches to medium- and high-resolution images. This work aimed to assess the advantages for land cover classification of (a) adopting an OB approach with PL data; and (b) integrating the PL datasets with Sentinel 2 and Sentinel 1 data both in Pixel-based (PB) or OB approaches. For this purpose, in this research, we compared ten LULC classification approaches (PB and OB, all based on the Random Forest (RF) algorithm), where the three satellite datasets were used according to different levels of integration and combination. The study area, which is 69,272 km² wide and located in central Brazil, was selected within the tropical region, considering a preliminary availability of sample points and its complex landscape mosaic composed of heterogeneous agri-natural spaces, including scattered settlements. Using only the PL dataset with a typical RF PB approach produced the worse overall accuracy (OA) results (67%), whereas adopting an OB approach for the same dataset yielded very good OA (82%). The integration of PL data with the S2 and S1 datasets improved both PB and OB overall accuracy outputs (82 vs. 67% and 91 vs. 82%, respectively). Moreover, this research demonstrated the OB approaches’ applicability in GEE, even in vast study areas and using high-resolution imagery. Although additional applications are necessary, the proposed methodology appears to be very promising for properly exploiting the potential of PL data in GEE. Full article