Study on shoreline migration and island dynamics over the last five decades in the Muriganga River using multi-temporal satellite images.

The Muriganga River, also known as channel creek, underwent morphological changes often since it is an alluvial as well as a tidal river. The present study analyses the morphological changes in the Muriganga River and its islands with the help of the Remote Sensing and Geographical Information System (GIS) and digital shoreline analysis tool (DSAS 5.0). Moreover, the computation of morphological changes was also performed on two islands, i.e. Sagar and Ghoramara, which are situated just outside the river reach. Eight cloud-free satellite images of Landsat MSS (1972-1980), Landsat TM (1988-2011) and Landsat OLI (2017-2021) have been used to investigate the river shoreline shifting and island dynamics of the Muriganga River resulted from the erosion-accretion process during the last 49 years. For the short-term study, the erosion-accretion rates are derived from one Landsat image to the next, whereas for long-term analysis, the erosion-accretion rates are estimated based on the difference between 1972 as the reference image and the succeeding images. Short-term and long-term analysis shows that the average rate of erosion is more than that of accretion in Muriganga River. It is also found that the areas of Sagar, Ghoramara, Mousuni and Pushpa islands are shrinking continuously, whereas the Niogi and Basit islands are expanding enormously. These may indicate that the shoreline erosion results in widening the river and the eroded materials are accumulated in Niogi and Basit islands. The results suggest that there is an urge for a better coastal management strategy for the erosion control scheme. This study also helps in gaining knowledge of maintaining the navigability in the Muriganga River.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03 ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlidere, and Elmali), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33 ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data.

To meet the challenge of food security, it is necessary to obtain information about rice fields accurately, quickly and conveniently. In this study, based on the analysis of existing rice fields extraction methods and the characteristics of intra-annual variation of normalized difference vegetation index (NDVI) in the different types of ground features, the NDVI difference method is used to extract rice fields using Sentinel data based on the unique feature of rice fields having large differences in vegetation between the pre-harvest and post-harvest periods. Firstly, partial correlation analysis is used to study the influencing factors of the rice harvesting period, and a simulation model of the rice harvesting period is constructed by multiple regression analysis with data from 32 sample points. Sentinel data of the pre-harvest and post-harvest periods of rice fields are determined based on the selected rice harvesting period. The NDVI values of the rice fields are calculated for both the pre-harvest and post-harvest periods, and 33 samples of the rice fields are selected from the high-resolution image. The threshold value for rice field extraction is determined through statistical analysis of the NDVI difference in the sample area. This threshold was then utilized to extract the initial extent of rice fields. Secondly, to address the phenomenon of the "water edge effect" in the initial data, the water extraction method based on the normalized difference water index (NDWI) is used to remove the pixels of water edges. Finally, the extraction results are verified and analyzed for accuracy. The study results show that: (1) The rice harvesting period is significantly correlated with altitude and latitude, with coefficients of 0.978 and 0.922, respectively, and the simulation model of the harvesting period can effectively determine the best period of remote sensing images needed to extract rice fields; (2) The NDVI difference method based on sentinel data for rice fields extraction is excellent; (3) The mixed pixels have a large impact on the accuracy of rice fields extraction, due to the water edge effect. Combining NDWI can effectively reduce the water edge effect and significantly improve the accuracy of rice field extraction.

Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery.

Flood inundation mapping and satellite imagery monitoring are critical and effective responses during flood events. Mapping of a flood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping. Flood mapping in Eastern India's Baitarani River Basin for 2018, 2019, 2020, 2021, and 2022 was performed in this study using Sentinel-1 imagery and Google Earth Engine with Otsu's algorithm. Different machine-learning algorithms were used to map the LULC of the study region. Dual polarizations VH and VV and their combinations VV×VH, VV+VH, VH-VV, VV-VH, VV/VH, and VH/VV were examined to identify non-water and water bodies. The normalized difference water index (NDWI) map derived from Sentinel-2 data validated the surface water inundation with 80% accuracy. The total inundated areas were identified as 440.3 km2 in 2018, 268.58 km2 in 2019, 178.40 km2 in 2020, 203.79 km2 in 2021, and 321.33 km2 in 2022, respectively. The overlap of flood maps on the LULC map indicated that flooding highly affected agriculture and urban areas in these years. The approach using the near-real-time Sentinel-1 SAR imagery and GEE platform can be operationalized for periodic flood mapping, helps develop flood control measures, and helps enhance flood management. The generated annual flood inundation maps are also useful for policy development, agriculture yield estimation, crop insurance framing, etc.

Spatiotemporal statistical analysis of water area changes with climatic variables using Google Earth Engine for Lakes Region in Türkiye.

In this study, trend analysis of the lake surface areas was performed on the Google Earth Engine (GEE) platform in the period of 1985-2022 with Landsat 5/7/8/9 (TM) (ETM +), and (OLI) satellite images. The study analyzed 10 lakes, including Acigol, Aksehir, Beysehir, Burdur, Egirdir, Ilgin, Isikli, Karatas, Salda, and Yarisli in the Türkiye Lakes Region. In this analysis, the normalized differentiated water index was calculated for each of the 3147 satellite images, and water surfaces were extracted from other details using Otsu's threshold method. In the study's accuracy, the overall accuracy and F1-score values were calculated to be over 90% for all lakes. Moreover, the relationship between the changes in the surface areas of the lakes was evaluated using correlation analysis, with the sea surface temperature obtained from the NOAA satellite and the evaporation, temperature, and precipitation parameters obtained from the Era-5 satellite being used. In addition, the change of the area on the lake surface was analysed using Mann-Kendall (MK), Sen's slope, and sequential MK test statistics. During the 37 years between 1985 and 2022, there was no significant change in the Acigol surface area, but a slight increasing trend was observed. Decreases of 76.07, 4.68, 41.77, 5.44, 37.56, 28.97, 78.65, 7.26, and 81.02% were determined in the lakes of Aksehir, Beysehir, Burdur, Egirdir, Ilgin, Isikli, Karatas, Salda, and Yarisli, respectively. The application of this method in the lakes region and monitoring these lakes, which are of great importance for Türkiye, provide valuable information in determining the lakes' organizational strategies.

Four-decade (1977-2017) landscape tale of tourist reservoir hotspot El Piñol-Guatapé, Colombia.

Efficient management of land use/land cover (LULC) features is vital for a balanced sustainable ecosystem. Thus, this work aimed to document the LULC changes in the less studied El Peñol-Guatapé reservoir, Antioquia, Colombia, especially in the reservoir area due to the construction of a hydro-electric power plant. For this study, Landsat images of 1977, 1986, 1997, and 2017 were used and the results indicated an increase in the settlement area and road networks by 0.10 and 0.60%, respectively, while during 1986 to 2017, cropland, plantation, dense forest, and open forest areas presented an increase of 0.52, 1.06, 2.87 and 2.61%, respectively. However, the marshy vegetation, scrub forest and fallow land decreased to - 0.51, - 3.79 and - 4.29%, respectively, in the same period. The water body before and after the completion of reservoir project denoted an increase from 13.1 km2 in 1977 to 45.7 km2 in 1986. This study provides a first-hand report on LULC dynamics in this tourism dominated municipalities that will serve as a reference for ecosystem management to reconcile the conflicts between different LULC classes in ecologically enriched regions.

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought.

Extreme events such as the summer drought of 2018 in Central Europe are projected to occur more frequently in the future and may cause major damages including increased tree mortality and negative impacts on forest ecosystem services. Here, we quantify the response of >1 million forest pixels of 10 × 10 m across Switzerland to the 2018 drought in terms of resistance, recovery, and resilience. We used the Normalized Difference Water Index (NDWI) derived from Sentinel-2 satellite data as a proxy for canopy water content and analyzed its relative change. We calculated NDWI change between the 2017 pre-drought and 2018 drought years (indicating resistance), 2018 and the 2019 post-drought (indicating recovery), and between 2017-2019 (indicating resilience). Analyzing the data from this large natural experiment, we found that for 4.3% of the Swiss forest the NDWI declined between 2017 and 2018, indicating areas with low resistance of the forest canopy to drought effects. While roughly 50% of this area recovered, in 2.7% of the forested area NDWI continued to decline from 2018 to 2019, suggesting prolonged negative effects or delayed damage. We found differential forest responses to drought associated with site topographic characteristics and forest stand characteristics, and to a lesser extent with climatic conditions and interactions between these drivers. Low drought resistance and high recovery were most prominent at forest edges, but also on south-facing slopes and lower elevations. Tree functional type was the most important driver of drought resilience, with most of the damage in stands with high conifer abundance. Our results demonstrate the suitability of satellite-based quantification of drought-induced forest damage at high spatial resolution across large areas. Such information is important to predict how local site characteristics may impact forest vulnerability to future extreme events and help in the search for appropriate adaptation strategies.

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods.

Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices "Normalized Difference Vegetation Index" (NDVI), "Enhanced Vegetation Index" (EVI), and "Normalized Difference Water Index" (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.

Assessment of habitat for disturbance-dependent species using light detection and ranging and multispectral sensors mounted on a UAV.

Recently, technological advances in UAV-mounted sensors, such as light detection and ranging (LiDAR) and multispectral sensors, have expanded the applications of unmanned aerial vehicles (UAVs) in ecosystem monitoring. LiDAR is suitable for analyzing the underlying microtopography of wetlands because it can produce a digital terrain model (DTM) with high spatial resolution. If a multispectral sensor that can also capture near-infrared wavelengths is used, it is possible to calculate the normalized difference vegetation index (NDVI), which is related to the amount of vegetation present, and the normalized difference water index (NDWI), which is related to the dryness and wetness of the soil. The purpose of this study was to understand the distribution of a disturbance-dependent species in wetlands using high spatial resolution images acquired with a consideration of phenology, and to evaluate the habitat of this disturbance-dependent species using data acquired by LiDAR and multispectral sensors. The wetland around the Omimaiko Inland Lake in Minamikomatsu, Otsu City, Shiga Prefecture, Japan, was chosen as the site for this study. I chose to examine the distribution of Euphorbia adenochlora as a disturbance-dependent species growing in the wetlands of the study area. Using high spatial resolution images acquired with a consideration of phenology, we were able to determine the distribution of the disturbance-dependent species E. adenochlora. Using the data obtained using LiDAR and multispectral sensors, we were able to evaluate its habitat and deduce its viability at six growth sites. This study aims to introduce a new way of applying UAVs in monitoring disturbance-dependent species in wetlands.

Integration of C-band SAR and high-resolution optical images for delineating palaeo-channels in Nagaur and Barmer districts, western Rajasthan, India.

Identifying hitherto unknown palaeo-channels, especially in the arid regions of the Thar Desert, is crucial since these channels may form excellent aquifers, and are also associated with valuable ore deposits of many precious minerals. This study employed integrated C-band Synthetic Aperture Radar (SAR) of Sentinel-1A and high-resolution multispectral Sentinel-2A data of pre- and post-monsoon seasons (June and November) to delineate playas and palaeo-channels. This approach is the first of its kind for this area. The palaeo-channels were delineated through a detailed visual inspection of colour composite (CC) images of Sentinel-2A data, SAR backscatter (VH) images and fused SAR and optical images. Then, we studied the topographic profiles generated from the Shuttle Radar Topography Mission - Digital Elevation Model (SRTM-DEM) across the identified palaeo-channels, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to further confirm the existence of a palaeo-channel's course and playas. As a result, several playas and palaeo-channels in the area were successfully identified, some of which were previously unmapped and undetected. The results indicate that the post-monsoon datasets are more useful for the precise delineation of palaeo-channels due to the presence of relatively higher moisture along the palaeo-channels' courses.

Comparative assessment of remote sensing-based water dynamic in a dam lake using a combination of Sentinel-2 data and digital elevation model.

Monitoring and determining the amount of water in reservoirs is of great importance in terms of water planning and management. This study proposes a geographic information system (GIS)-based methodology to estimate the water volume changes in water reservoirs. Two specific methods are proposed using Australian National University's Digital Elevation Model (ANUDEM) raster surface and Triangulated Irregular Network (TIN) surface models, both utilizing normalized difference water index (NDWI) of Sentinel 2A satellite images for water-covered area and coastline and digital elevation model (DEM) for 3D modelling of the reservoir. The most crucial part of this study is the comprehensive evaluation of the model findings considering hydrological, meteorological and anthropogenic factors, simultaneously. Application of the proposed methods is provided for the analysis of the multi-temporal water volume changes of Bayramiç Dam Lake (Çanakkale, Turkey) in two hydrological periods covering the 2015-2016 and 2016-2017 water years. The results indicate that the TINS model produced water volume values much closer to the in situ Turkish General Directorate of State Hydraulic Works (DSI) values than the ANUDEM model. The performance of these methods was also assessed by the temporal dynamics of surface hydrological processes. Regarding the water storage dynamics, hydro-meteorological factors influence the water input, while anthropogenic factors strongly influence the water output. Water consumption for irrigation and electricity generation was found to be the most important water budget components of the total water consumption.

Applications of Sentinel-2 satellite data for spatio-temporal mapping of deep pools for monitoring the riverine connectivity and assessment of ecological dynamics: a case from Godavari, a tropical river in India (2016-2021).

Rivers are dynamic ecosystems with diverse habitats that require ample connectivity to ensure the flow of ecosystem services, thus empowering the sustainable development of an entire basin. Geo-spatial tools offer powerful prospects for monitoring of aquatic ecosystems. The usefulness of Sentinel-2 datasets to assess river connectivity has been explored for an un-gauged seasonal river system. The present study was undertaken in five ecologically unique river reaches viz. Wainganga, Wardha, Pranhita, Godavari-mid and Manair in Godavari Basin in the Indian Deccan Plateau to map water spread dynamics at various time scales, i.e., fortnightly, monthly, seasonal, annual and demi-decadal during 2016-2021. The maximum value of perennial water spread per square kilometre of total floodplain area (2016-2021), determined using Sentinel-2 imageries, was observed in river Wardha (0.18) followed by Pranhita (0.12) and Wainganga (0.11). The water spread showed a decreasing trend, while the number of patches in the river corridor increased over time from post-monsoon to pre-monsoon season. The copious perennial habitat with relatively larger patches, incessant flow in river Pranhita and obstructed flow, large-sized patches reported in river Wardha during summer months, hold importance in terms of providing refuge to aquatic biota. This study provides evidence for the impact of water projects on spatio-temporal water spread dynamics in Godavari Basin. The demonstrated utility of Sentinel-2 imagery coupled with gauge station measurements for river continuity assessment and deep pool mapping would aid in enhancing our understanding on environmental flow at a spatial scale, which in turn would aid in effective river management to achieve the Sustainable Development Goals. The implications of this study for sustainable environmental management and limitations are also discussed.

Seasonal drought analysis of Aksehir Lake with temporal combined sentinel data between 2017 and 2021 spring and autumn.

The threat of drought has been felt almost worldwide in recent years. It is critical to determine the causes of drought and how seasonal changes affect it. Additionally, it is necessary to determine the speed and impact area of drought, monitor drought areas, and attempt to find solutions against drought. With the developing satellite sensing systems, remote sensing methods are being used to investigate topics such as the increase and extent of drought, uncontrolled water consumption in agricultural activities, and the effects of unnatural pollutants on freshwater resources such as lakes and rivers. Using Synthetic Aperture Radar (SAR) satellite data to monitor changes in water bodies is a relatively new area of study in remote sensing. The spatial extent and seasonal change (spring and autumn) of droughts between 2017 and 2021 in Aksehir Lake were determined from Sentinel-1A SAR satellite data, and the Normalized Differential Water Index (NDWI) was calculated using Sentinel-2A optical satellite data and Standardized Precipitation Index (SPI) in this research. In addition, a different approach was applied to determine the change of wetland boundaries more accurately by converting the linear Sigma0 band to the decibel (dB) band and applying a non-linear 3 × 3 maximum filter to the dB band to Sentinel-1A data. Consequently, it has been established that Aksehir Lake, which used to have wetlands during the spring seasons but began to dry up in the autumn seasons, had completely dried up in both periods in 2021.

Comparison of thresholding methods for shoreline extraction from Sentinel-2 and Landsat-8 imagery: Extreme Lake Salda, track of Mars on Earth.

Lake Salda's extreme environment is geologically similar to Jezero Crater paleolake on Mars due to the formation of stromatolites and its extremely alkaline and cold water. It is critical to accurately determine the shoreline in the littoral zone where stromatolite formation occurs, in alkaline clean lakes like Salda, which contain traces of life on Mars, and in monitoring the change that occurs with climate and anthropogenic effect. The performance of global automatic thresholding algorithms on shoreline determination from NDWI and mNDWI water indices is compared in this study using Sentinel-2 and Landsat-8 images atmospherically corrected by different algorithms. Satellite images data acquired on August 12, 2020 for Sentinel-2 and August 11, 2020 for Landsat-8 on Lake Salda were used to determining the shoreline. The shoreline data measured in situ concurrently with the Sentinel-2 satellite acquisition was used as reference data. In the accuracy analysis, ground control points created inside and outside the lake at a distance of 1 pixel and 0.5 pixel to the reference shoreline for each satellite image were used. The performance of the optimal threshold values determined by each thresholding algorithm in the water index images was assessed using Kappa coefficient, Overall Accuracy (OA), %OA of Inside (%OAinside) and %OA of Outside (%OAoutside) statistics metrics. The optimal threshold values vary depending on the image and the atmospheric correction algorithm applied to the image. The NDWI index produces more accurate results in both Sentinel-2 and Landsat-8 satellite images. While atmospheric correction algorithms do not affect the results in Landsat-8 images, the Sen2Cor algorithm outperforms iCOR in Sentinel-2 images. For thresholding algorithms to be used in different water index and satellite images, Intermode, Isodata, IJ_Isodata, Minimum and Otsu algorithms in Landsat-8_LaSRC_NDWI and Landsat-8_iCOR_NDWI images, and Intermode, Minimum and Huang algorithms in Sentinel-2_Sen2Cor_NDWI images produce the best results. Because the Minimum algorithm causes significant gaps in the lake surface, the Huang and Intermode algorithms should be used for Sentinel-2_Sen2Cor_NDWI images. The 0 (zero) threshold value in the water indices images has a high accuracy only in the NDWI water indices generated from the Landsat-8 image.

Spectro-temporal analysis of the Paraopeba River water after the tailings dam burst of the Córrego do Feijão mine, in Brumadinho, Brazil.

Remote sensing is an important tool for environmental assessment, especially in the event of disasters such as the tailings dam burst at the Córrego do Feijão mine, located in the Paraopeba River basin, Brazil. Thus, this study aimed to carry out a spectro-temporal analysis of the Paraopeba River water given the dam burst, using multispectral images from the MSI sensor onboard Sentinel-2 satellites. For this analysis, sections along the river were defined by the creation of buffers, with 10-km intervals each, starting from the origin of the burst. For each section, the average visible to near-infrared (NIR) reflectance values per band and the Normalized Difference Water Index (NDWI) were obtained. We found that the red edge and NIR bands (B5, B6, B7, B8, and B8A) showed higher reflectance values when compared to the visible bands in the months immediately after the disaster, especially in the first 20 km. In these months, negative NDWI values were also found for almost all sections downstream, demonstrating the large volume of mining tailings in the Paraopeba River. The seasonal variation of the observed values indicates the resuspension of the material deposited at the river bottom with the beginning of the rainy season. Finally, we highlight the usefulness of the MSI/Sentinel-2 red edge and NIR bands for further studies on the monitoring from space of water bodies subjected to contamination by large amounts of mud with iron ore tailings and contaminants, as occurred in the state of Minas Gerais, southeastern Brazil.

Integrating intraseasonal grassland dynamics in cross-scale distribution modeling to support waterbird recovery plans.

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water-vegetation dynamics into a cross-scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel-2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species' distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat-topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water-vegetation dynamic models, based exclusively on Sentinel-2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water-vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost-effective recovery planning by monitoring management effectiveness from space.

Integración de las Dinámicas Intraestacionales de los Pastizales al Modelado de la Distribución a través de Diversas Escalas para Respaldar los Planes de Recuperación de Aves Acuáticas Resumen A pesar A pesar del potencial de la teledetección para la conservación, la inclusión de estas tecnologías en los planes de recuperación de especies es muy poco habitual. En este trabajo, desarrollamos una estrategia de modelado para la integración de dinámicas ecosistémicas funcionales locales medidas espacialmente dentro de un marco de trabajo del modelado de distribución de especies (MDE), en el cual otros factores ecológicamente relevantes se modelan por separado y a escalas más generales. Para ilustrar la estrategia incorporamos las dinámicas Intraestacionales de la vegetación acuática en un MDE multiescala escalas para la agachadiza común (Gallinago gallinago), la cual es sumamente dependiente de las dinámicas del agua y la vegetación. La agachadiza común es un ave acuática de los pastizales ibéricos, característica de las praderas agrícolas de Europa y miembro de uno de los grupos de aves más amenazados. Medimos las dinámicas intraestacionales del contenido de agua de la vegetación con la desviación estándar de la serie temporal del índice de diferencia normalizada de agua a partir de las imágenes bimensuales del satélite Sentinel-2. El plan de recuperación para la agachadiza común en Galicia (noroeste de la península ibérica) proporcionó una oportunidad para aplicar nuestro marco de trabajo. La capacidad del modelo para predecir la distribución de la especie a una escala regional (resultante de la integración de proyecciones climáticas a escala reducida con modelos regionales de idoneidad hábitat-topografía) fue muy alta (área bajo la curva [AUC] de 0.981 e índice de Boyce de 0.971). El poder de predicción aumentó (AUC de 0.92 e índice de Boyce de 0.98) cuando las predicciones de los modelos locales estuvieron restringidos a áreas identificadas por los modelos continentales y regionales como prioritarias para la conservación. Nuestros modelos también tuvieron un buen desempeño (AUC de 0.90 e índice de Boyce de 0.93) cuando los proyectamos hacia las condiciones actualizadas de vegetación acuática. Nuestro marco de trabajo permitió la incorporación de procesos ecosistémicos clave intimamente relacionados con los ciclos del agua y del carbono a la vez que representaba otros factores ecológicamente relevantes para las aves acuáticas amenazadas de pastizal, a través de diferentes escalas. También permitió la identificación de áreas prioritarias para la conservación y proporcionó oportunidades para la planificación rentable de la recuperación al monitorear la efectividad del manejo desde el espacio.

On the use of remote sensing to map the proliferation of aquaculture ponds and to investigate their effect on local climate, perspectives from the Claise watershed, France.

Ponds, as landscape features, are known to regulate climate. Since ponds proliferate or recede due to natural or anthropogenic factors, a variation of pond numbers implies a variation of their climatic effect. Accordingly, this study investigates the impact of ponds on the local climate of the French Claise watershed. The latter was chosen because it contains a pond dense zone and a pondless zone. This repartition makes the Claise an adequate context to reveal the climatic impact of ponds even in the same landscape. To study the pond-climate effect, the parallel evolution of pond numbers variation and subsequent climatic impact must be tracked. Therefore, the remote sensing-derived Normalized Difference Water Index (NDWI) was extracted from LANDSAT images with different acquisition dates to track changes in pond numbers with time. When compared with a pond map established from aerial photography interpretation, the LANDSAT NDWI map revealed an accuracy of 85.74% for pond count and 75% for pond spatial allocation. This validation showed that NDWI is suitable for mapping the proliferation of ponds through time. In order to study the parallel evolution of the climatic effect, the land surface temperature (LST) index was extracted for each LANDSAT map. LST maps revealed that as a result of pond number variation, surface temperatures varied accordingly. A comparison of air temperatures between the ponded zone and pondless zones also revealed that pond zones had lower air temperatures than their direct surroundings. Accordingly, ponds were shown to buffer local microclimates even within the same landscape.

Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal.

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

Decision tree for mapping of halophyte cover around Ghannouch, Tunisia.

Environment of Ghannouch in the south-east of Tunisia is characterized by the wide-spread hypersaline soils, typically colonized by halophytes. The study of their distribution is required in order to reveal the extent of salinization and its dynamic. Mapping and monitoring with a remote sensing approach are foreseen as the ways to trace the spatial and temporal dimensions of the phenomenon. The identification of halophyte vegetation can take advantage by analyzing optical remote sensing data. Here, we propose using a decision tree approach applied to European Space Agency Sentinel-2 imagery, for an accurate land cover mapping of Ghannouch district in Gabès governorate. Data pre-processing was carried out using the European Space Agency's Sentinel Application Platform and the SEN2COR toolboxes. The mapping approach combines the spectral information in several channels of the visible-near-infrared spectrum. The land cover identification was performed following a spectral classification approach, exploiting several optical indices, normalized difference water index, normalized difference vegetation index, and several soil salinity index, in order to elaborate a decision tree algorithm. As a result, for an area of interest of 50 × 50 km2, at least 68% was classified as halophyte land cover. This mapping exercise represents an important step toward improved halophytes mapping in Tunisia and could be used to monitor the status of other salinity prone regions in the world.

Inter-comparison of remote sensing-based shoreline mapping techniques at different coastal stretches of India.

Many techniques are available for detection of shorelines from multispectral satellite imagery, but the choice of a certain technique for a particular study area can be tough. Hence, for the first time in literature, an inter-comparison of the most widely used shoreline mapping techniques such as Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Improved Band Ratio (IBR) Method, and Automatic Water Extraction Index (AWEI) has been done along four different coastal stretches of India using multitemporal Landsat data. The obtained results have been validated with the high-resolution images of Cartosat-2 (panchromatic) and multispectral images from Google Earth. Performance of the above indices has been analyzed based on the statistics, such as overall accuracy, kappa coefficient, user's accuracy, producer's accuracy, and the average deviation from the reference line. It is observed that the performance of NDWI and IBR techniques are dependent on the physical characteristics of the sites, and therefore, it varies from one site to another. Results indicate that unlike these two indices, the AWEI algorithm performs consistently well followed by MNDWI irrespective of the land cover types.