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1. Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

   https://doi.org/10.3390/rs13101966  

   Smith, Christopher W ; Panda, Santosh K ; Bhatt, Uma S ; Meyer, Franz J ; Badola, Anushree ; Hrobak, Jennifer L ( May 2021 , Remote Sensing)

   null (Ed.)

   In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent. 

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2. Coastal erosion and sediment reworking caused by hurricane Irma – implications for storm impact on low‐lying tropical islands

   https://doi.org/10.1002/esp.5293  

   Spiske, Michaela ; Pilarczyk, Jessica E. ; Mitchell, Stephen ; Halley, Robert B. ; Otai, Tiffany ( December 2021 , Earth Surface Processes and Landforms)

   Hurricane Irma (September 2017) was one of the most devastating hurricanes in recent times. In January 2018, a post‐hurricane field survey was conducted on Anegada (British Virgin Islands) to report on the erosional and depositional evidence caused by Hurricane Irma's storm surge and waves. We document the type and extent of hurricane‐induced geomorphological changes, allowing for an improved risk assessment of hurricane‐related inundation on low‐lying islands and carbonate platforms.

   Anegada's north shore was most impacted by Hurricane Irma. The surge reached about 3.8 m above sea level and onshore flow depths ranged between 1.2 to 1.6 m. Storm wave action created 1 to 1.5 m high erosional scarps along the beaches, and the coastline locally retreated by 6 to 8 m.

   Onshore sand sheets reached up to 40 m inland, overlie a sharp erosive contact and have thicknesses of 7 to 35 cm along the north shore. In contrast, lobate overwash fans in the south are 2 to 10 cm thick and reach 10 to 30 m inland.

   Moreover, the hurricane reworked a pre‐existing coast‐parallel coral rubble ridge on the central north shore. The crest of the coral rubble ridge shifted up to 10 m inland due to the landward transport of cobbles and boulders (maximum size 0.5 m³) that were part of the pre‐hurricane ridge.

   A re‐survey, 18 months after the event, assessed the degree of the natural coastal recovery. The sand along the northern shoreline of Anegada that was eroded during the hurricane and stored in the shallow water, acted as a nearshore source for beach reconstruction which set in only days after the event. Beach recovery peaked in February 2018, when beaches accreted within hours during a nor'easter‐like storm that transported large volumes of nearshore sand back onto the beach.

    

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3. A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

   https://doi.org/10.3390/agriculture10040131  

   Rahman, Md Shahinoor ; Di, Liping ( April 2020 , Agriculture)

   This article reviews case studies which have used remote sensing data for different aspects of flood crop loss assessment. The review systematically finds a total of 62 empirical case studies from the past three decades. The number of case studies has recently been increased because of increased availability of remote sensing data. In the past, flood crop loss assessment was very generalized and time-intensive because of the dependency on the survey-based data collection. Remote sensing data availability makes rapid flood loss assessment possible. This study groups flood crop loss assessment approaches into three broad categories: flood-intensity-based approach, crop-condition-based approach, and a hybrid approach of the two. Flood crop damage assessment is more precise when both flood information and crop condition are incorporated in damage assessment models. This review discusses the strengths and weaknesses of different loss assessment approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat are the dominant sources of optical remote sensing data for flood crop loss assessment. Remote-sensing-based vegetation indices (VIs) have significantly been utilized for crop damage assessments in recent years. Many case studies also relied on microwave remote sensing data, because of the inability of optical remote sensing to see through clouds. Recent free-of-charge availability of synthetic-aperture radar (SAR) data from Sentinel-1 will advance flood crop damage assessment. Data for the validation of loss assessment models are scarce. Recent advancements of data archiving and distribution through web technologies will be helpful for loss assessment and validation. 

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4. Madagascar’s extraordinary biodiversity: Threats and opportunities

   https://doi.org/10.1126/science.adf1466  

   Ralimanana, Hélène ; Perrigo, Allison L. ; Smith, Rhian J. ; Borrell, James S. ; Faurby, Søren ; Rajaonah, Mamy Tiana ; Randriamboavonjy, Tianjanahary ; Vorontsova, Maria S. ; Cooke, Robert S. ; Phelps, Leanne N. ; et al ( December 2022 , Science)

   BACKGROUND Madagascar is one of the world’s foremost biodiversity hotspots. Its unique assemblage of plants, animals, and fungi—the majority of which evolved on the island and occur nowhere else—is both diverse and threatened. After human arrival, the island’s entire megafauna became extinct, and large portions of the current flora and fauna may be on track for a similar fate. Conditions for the long-term survival of many Malagasy species are not currently met because of multiple anthropogenic threats. ADVANCES We review the extinction risk and threats to biodiversity in Madagascar, using available international assessment data as well as a machine learning analysis to predict the extinction risks and threats to plant species lacking assessments. Our compilation of global International Union for Conservation of Nature (IUCN) Red List assessments shows that overexploitation alongside unsustainable agricultural practices affect 62.1 and 56.8% of vertebrate species, respectively, and each affects nearly 90% of all plant species. Other threats have a relatively minor effect today but are expected to increase in coming decades. Because only one-third (4652) of all Malagasy plant species have been formally assessed, we carried out a neural network analysis to predict the putative status and threats for 5887 unassessed species and to evaluate biases in current assessments. The percentage of plant species currently assessed as under threat is probably representative of actual numbers, except in the case of the ferns and lycophytes, where significantly more species are estimated to be threatened. We find that Madagascar is home to a disproportionately high number of Evolutionarily Distinct and Globally Endangered (EDGE) species. This further highlights the urgency for evidence-based and effective in situ and ex situ conservation. Despite these alarming statistics and trends, we find that 10.4% of Madagascar’s land area is protected and that the network of protected areas (PAs) covers at least part of the range of 97.1% of terrestrial and freshwater vertebrates with known distributions (amphibians, freshwater fishes, reptiles, birds, and mammal species combined) and 67.7% of plant species (for threatened species, the percentages are 97.7% for vertebrates and 79.6% for plants). Complementary to this, ex situ collections hold 18% of vertebrate species and 23% of plant species. Nonetheless, there are still many threatened species that do not occur within PAs and are absent from ex situ collections, including one amphibian, three mammals, and seven reptiles, as well as 559 plants and more yet to be assessed. Based on our updated vegetation map, we find that the current PA network provides good coverage of the major habitats, particularly mangroves, spiny forest, humid forest, and tapia, but subhumid forest and grassland-woodland mosaic have very low areas under protection (5.7 and 1.8% respectively). OUTLOOK Madagascar is among the world’s poorest countries, and its biodiversity is a key resource for the sustainable future and well-being of its citizens. Current threats to Madagascar’s biodiversity are deeply rooted in historical and present social contexts, including widespread inequalities. We therefore propose five opportunities for action to further conservation in a just and equitable way. First, investment in conservation and restoration must be based on evidence and effectiveness and be tailored to meet future challenges through inclusive solutions. Second, expanded biodiversity monitoring, including increased dataset production and availability, is key. Third, improving the effectiveness of existing PAs—for example through community engagement, training, and income opportunities—is more important than creating new ones. Fourth, conservation and restoration should not focus solely on the PA network but should also include the surrounding landscapes and communities. And finally, conservation actions must address the root causes of biodiversity loss, including poverty and food insecurity. In the eyes of much of the world, Madagascar’s biodiversity is a unique global asset that needs saving; in the daily lives of many of the Malagasy people, it is a rapidly diminishing source of the most basic needs for subsistence. Protecting Madagascar’s biodiversity while promoting social development for its people is a matter of the utmost urgency Visual representation of five key opportunities for conserving and restoring Madagascar’s rapidly declining biodiversity identified in this Review. The dashed lines point to representative vegetation types where these recommendations could have tangible effects, but the opportunities are applicable across Madagascar. ILLUSTRATION: INESSA VOET 

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5. Expanding infrastructure and growing anthropogenic impacts along Arctic coasts

   https://doi.org/10.1088/1748-9326/ac3176  

   Bartsch, Annett ; Pointner, Georg ; Nitze, Ingmar ; Efimova, Aleksandra ; Jakober, Dan ; Ley, Sarah ; Högström, Elin ; Grosse, Guido ; Schweitzer, Peter ( November 2021 , Environmental Research Letters)

   null (Ed.)

   Abstract The accelerating climatic changes and new infrastructure development across the Arctic require more robust risk and environmental assessment, but thus far there is no consistent record of human impact. We provide a first panarctic satellite-based record of expanding infrastructure and anthropogenic impacts along all permafrost affected coasts (100 km buffer, ≈6.2 Mio km 2 ), named the Sentinel-1/2 derived Arctic Coastal Human Impact (SACHI) dataset. The completeness and thematic content goes beyond traditional satellite based approaches as well as other publicly accessible data sources. Three classes are considered: linear transport infrastructure (roads and railways), buildings, and other impacted area. C-band synthetic aperture radar and multi-spectral information (2016–2020) is exploited within a machine learning framework (gradient boosting machines and deep learning) and combined for retrieval with 10 m nominal resolution. In total, an area of 1243 km 2 constitutes human-built infrastructure as of 2016–2020. Depending on region, SACHI contains 8%–48% more information (human presence) than in OpenStreetMap. 221 (78%) more settlements are identified than in a recently published dataset for this region. 47% is not covered in a global night-time light dataset from 2016. At least 15% (180 km 2 ) correspond to new or increased detectable human impact since 2000 according to a Landsat-based normalized difference vegetation index trend comparison within the analysis extent. Most of the expanded presence occurred in Russia, but also some in Canada and US. 31% and 5% of impacted area associated predominantly with oil/gas and mining industry respectively has appeared after 2000. 55% of the identified human impacted area will be shifting to above 0 ∘ C ground temperature at two meter depth by 2050 if current permafrost warming trends continue at the pace of the last two decades, highlighting the critical importance to better understand how much and where Arctic infrastructure may become threatened by permafrost thaw. 

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