High Spatial Resolution Satellite Images Research Articles

A Satellite Analysis: Comparing Two Medicanes

Morphological features of the Mediterranean Sea basin have recently been precursors to a significant increase in the formation of extreme events, in relation to climate change effects. It happens very frequently that rotating air masses and the formation of mesoscale vortices can evolve into events with characteristics similar to large-scale tropical cyclones. Generally, they are less intense, with smaller size and duration; thus, they are called Medicanes, a short name for Mediterranean hurricanes, or tropical-like cyclones (TLCs). In this paper, we propose a new perspective for the study and analysis of cyclonic events, starting with data and images acquired from satellites and focusing on the diagnostics of the evolution of atmospheric parameters for these events. More precisely, satellite remote sensing techniques are employed to elaborate on different high spatial-resolution satellite images of the events at a given sensing time. Two case studies are examined, taking into account their development into Medicane stages: Ianos, which intensified in the Ionian Sea and reached the coast of Greece between 14 and 21 September 2020, and Apollo, which impacted Mediterranean latitudes with a long tracking from 24 October to 2 November 2021. For these events, 20 images were acquired from two different satellite sensors, onboard two low-Earth orbit (LEO) platforms, by deeply exploiting their thermal infrared (TIR) spectral channels. A useful extraction of significant physical information was carried out from every image, highlighting several atmospheric quantities, including temperature and altitude layers from the top of the cloud, vertical temperature gradient, atmospheric pressure field, and deep convection cloud. The diagnostics of the two events were investigated through the spatial scale capabilities of the instruments and the spatiotemporal evolution of the cyclones, including the comparison between satellite data and recording data from the BOLAM forecasting model. In addition, 384 images were extracted from the geostationary (GEO) satellite platform for the investigation of the events’ one-day structure intensification, by implementing time as the third dimension.

ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ ПОПЕРЕДНЬОЇ ОБРОБКИ СУПУТНИКОВИХ ЗОБРАЖЕНЬ З ВИКОРИСТАННЯМ ЗГОРТКОВОЇ НЕЙРОННОЇ МЕРЕЖІ

With urbanization and the growing interest in geographic information systems, preci-sion mapping, and environmental monitoring, there is a need to improve methods of fusing high-resolution digital satellite images to obtain more detailed and informative data. Satellite images with high spatial resolution hold significant promise for addressing a variety of is-sues. However their utility is frequently constrained by limited contrast dynamics and re-stricted spectral data. Pansharpening methods are effective means of increasing the spatial resolution of multichannel images using panchromatic ones. Although there are a significant number of proposed pansharpening algorithms, none can be universal for all types of sensors and demonstrate different results. The paper reviews the fusion methods of high spatial reso-lution satellite images and proposes an information technology for preprocessing digital sat-ellite images using a convolutional neural network. It made it possible to effectively extract texture details and spatial dependencies for local and non-local information. The paper com-pares the results of the proposed technology with existing pan-sharpening methods, such as Gram-Shmidt, HSV, Wav+HSV, ATWT, AWLP, BDSD, Brovey, and HPF. The results of test-ing conducted on eight-channel images acquired by the WorldView-2 satellite confirm that the proposed technology (ERGAS=0.32, RMSE=0.28b SSIM=0.71, PSNR=37.60) allows for achieving high spectral and spatial quality of multichannel images and outperforms existing methods.

Deep Learning Approach to Improve Spatial Resolution of GOES-17 Wildfire Boundaries Using VIIRS Satellite Data

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

Suaeda salsa spectral index for Suaeda salsa mapping and fractional cover estimation in intertidal wetlands

Suaeda Salsa (S. salsa), with short and red-purplish plants, is a typical pioneer saltmarsh species in the intertidal wetlands of temperate East Asia. It has important ecological, economic, and recreational values. In the past few decades, S. salsa has severely degraded in coastal China, which has brought widespread attention from regional and local governments. As a result, extensive S. salsa restoration projects have been initiated in recent years. It is urgently needed to develop satellite-based methods for both S. salsa mapping and fractional cover (FC) estimation because degradation and recovery of S. salsa are manifested by changes in both area and plant cover. However, accurate mapping and FC estimation of S. salsa are challenging because (1) S. salsa in intertidal areas have low FC and (2) heterogeneous soil backgrounds in wetlands greatly impact the spectral reflectance observed by satellites. To address these issues, this study proposed a new Suaeda Salsa Spectral Index (SSSI) to support accurate detection and FC estimation of S. salsa. The SSSI was designed based on the laboratory spectral measurements by considering variations in wetland soil moisture and by taking advantage of the reddish color of S. salsa. It consists of two components, one of which utilized blue, green and red bands to separate S. salsa from green vegetation, and the other component utilized a modification of the Soil Adjusted Vegetation Index (SAVI) to reduce the impact of soil background and maintain a linear relationship with S. salsa FC. SSSI was then applied on Sentinel-2/GF-1 images over the Yellow River Delta (YRD) and Liao River Delta (LRD), China. Based on SSSI, a simple thresholding approach was used to identify S. salsa, and a linear regression model was used to estimate FC. With reference datasets provided from field investigations, Unmanned Aerial Vehicle multispectral images and high-spatial resolution satellite images, our results show that the SSSI was able to detect low-coverage S. salsa (FC > 20 % in YRD and FC > 10 % in LRD), and the S. salsa maps had an overall accuracy over 94%. The SSSI-FC models achieved good estimation accuracies (R2 = 0.77 ∼ 0.86, RMSE = 7.55 % ∼ 9.79 %). Compared to the Normalized Difference Vegetation Index (NDVI) and SAVI, SSSI alleviated the impacts from soil backgrounds and provided better S. salsa FC estimations, particularly for low-coverage S. salsa. SSSI has great potential in supporting continuous monitoring of S. salsa dynamics and evaluating the effectiveness of S. salsa restoration projects.

A New Approach for Real‐Time Erupted Volume Estimation From High‐Precision Strain Detection Validated by Satellite Topographic Monitoring

AbstractTimely estimations of magma volumes emitted during an eruption or a sequence of explosive events are vital for investigating the eruptive activity and evaluating the associated hazard. A reliable method for estimating erupted volumes is based on the analysis of digital surface models that nowadays can be obtained subsequently using stereo or tri‐stereo optical satellite imagery. However, the real‐time estimation of the erupted volumes is still an open challenge. Here, we explore the capacity of extracting volume estimates from continuous measurements of volumetric strain changes recorded by borehole dilatometers. We compare the volumes derived from numerous high spatial resolution satellite images with high precision strain records at Etna during 2020–2022, when more than 60 lava fountains occurred. The good correlation between the two data sets shows that strain changes can be used as a proxy to estimate the emitted volumes both over time and in real‐time.

Повышение эффективности текстурной сегментации лесного полога по изображениям сверхвысокого пространственного разрешения

The issue of increasing the accuracy of tree canopy segmentation structures in the processing of very high spatial resolution satellite images by joint use of textural features extracted by different methods is considered. The TTSPCA method (Total Texture Statistics Principal Component Analysis) is proposed and its effectiveness is shown for a number of test cases. Estimates of TTSPCA effectiveness are obtained for the forest stands segmentation and growth types. The panchromatic Worldview-2 image of the test area (Bronnitskoye forestry, Moscow region) obtained in summer was used as remote data. To perform texture segmentation, in addition to TTSPCA, several standard second-order statistical methods and a spectral (energy) method based on the wavelet transform were also used. It is shown that almost all considered statistical and spectral methods provide forest stand segmentation with errors not exceeding 3–4 %. It has been established that the TTSPCA method makes it possible to reduce the probability of errors in the forest inventory zone, as well as to identify sections with a predominance of natural and artificial plantations with an accuracy of over 85 %. The results obtained can be further recommended for use in order to improve the system we are developing for joint spectral-textural processing of satellite images with different spatial resolutions.

Exploring the capability of high-resolution satellite data in delineating the potential distribution of common invasive alien plant species in the Tshivhase Tea Estate

Invasive alien plants (IAPs) continue to exert significant impacts on agriculture in many countries, resulting in food insecurity. IAPs reduce agricultural production through competition and parasitism with planted crops. More recently, the IAPs continue to extend their plasticity to tea plantations, especially in tropical and subtropical areas. This study thus aimed at exploring the potential of SPOT 7 and Sentinel 2 satellite data in mapping the occurrence and co-occurrence of three common IAPs Solanum mauritianum, Lantana camara, and Chromolaena odorata in the Tshivhase Tea Estate in Limpopo Province, South Africa. The stepwise logistic regression models were generated for Solanum mauritianum and Lantana camara occurrence as well as the observed and conditional co-occurrence probability of S. mauritianum (P1), L. camara (P2) and C. odorata (P3). From the remote sensing indices, the Brightness Index (BI) was significant in most SPOT 7 stepwise logistic regression models at p<0.05 whereas the blue, red, and near infrared (NIR) bands and standard deviation (STDv) variables were significant at p<0.05 in most of the Sentinel 2 models. The SPOT 7 model performed Sentinel-2 models, thus resulting in the area under the curve (AUC) of 0.96 for the conditional co-occurrence of S. mauritianum (P1) and L. camara (P2). The Sentinel 2 model yielded an AUC of 0.83. The SPOT 7 model performed superior in mapping the conditional co-occurrence of S. mauritianum and L. camara than the Sentinel 2 model. These results suggest that high spatial resolution satellite images like SPOT 7 can delineate the potential distribution of IAPs in the tea plantation and thus assisting in management strategies geared towards IAP’s elimination and control.

Linking PM10 and PM2.5 Pollution Concentration through Tree Coverage in Urban Areas

AbstractParticulate matter, PM10 and PM2.5, represents common air pollutants in cities and constitute a considerable threat to public health impacting daily activity of people living in city. In large cities, the main sources of PM10 and PM2.5 are diesel engine exhaust, brake dust, and particulate matter from vehicle tires. These particles can be deposited, filtered, and considerably reduced if there is a vegetative surface in the neighborhoods, thus eliminating a part of these particles and reducing their harmful footprint. This study evaluates the effect of tree coverage in urban areas on PM10 and PM2.5 removal considering air quality monitoring stations. Estimation of tree coverage is made by using high spatial and temporal resolution satellite images from Planet constellations. An empirical relationship between these two variables, with an acceptable correlation (R2 = 0.478 and R2 = 0.589 for PM10 and PM2.5, respectively), is obtained. A higher abundance of green space is associated with significantly lower PM10 and PM2.5 values. Preliminary results suggest that the amount of tree coverage do cause some degree of air quality improvement and can be used to inform national clean air strategies aimed at reducing pollutant emissions.

Combining 2D encoding and convolutional neural network to enhance land cover mapping from Satellite Image Time Series

The use of high spatial resolution Satellite Image Time Series (SITS) provides an opportunity for a wide spectrum of Earth surface monitoring applications such as Land Use/Land Cover (LULC) mapping. Whereas the majority of Time Series (TS) classification literature concentrates on the analysis of raw 1D signals, here, we investigate a framework for LULC mapping based on 2D encoded multivariate SITS data to enhance their classification performances. In this novel approach, multivariate SITS data are transformed from 1D signals to 2D images using several encoding techniques namely Gramian Angular Summation field (GASF), Gramian angular difference field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Successively, a new multi-band image is derived and it is used as input to a state-of-the-art convolutional neural network (CNN) classification model. The possibility to effectively encode multivariate TS data into 2D representations paves the way to reuse the huge amount of research findings coming from the general field of computer vision and build on reliable and robust methods that have been demonstrated their quality in a multitude of downstream applications. Experiments carried out on three real-world benchmarks covering large spatial areas with contrasted land cover features, namely: Dordogne department in France, Reunion Island an oversee French territory and Koumbia municipality in Burkina Faso, underline the quality of the proposed framework when compared to standard approaches for land cover mapping from SITS and recent methods for multivariate TS classification. Matter of fact, our new framework outperforms the classification performances of standard land cover classification strategies based on the raw TS information achieving an average F1-score of 89.34%, 90.26% and 78.94% for the Reunion Island, Dordogne and Koumbia study site, respectively with an increasing of at least 2.5 points w.r.t. the best competing approach.

SEGMENTATION OF ELECTRICAL SUBSTATIONS USING DEEP CONVOLUTIONAL NEURAL NETWORK

Abstract. The location of electrical substations is one of the factors affecting the improvement of electrical energy distribution, as well as the management and control of this energy source. Less cost and manpower will be spent through automating the process of detection and segmentation of these features with the help of deep neural networks and the potential of existing high spatial resolution satellite images. In this study, a deep encoder-decoder neural network was used. This network is one of the most updated deep learning methods in image processing and segmentation. This network has been trained in three RGB bands with the help of high-resolution satellite images (∼1m) and eventually segmented the areas related to electrical substations with relatively high accuracy. As the results of this convolutional neural network, the IOU and Precision parameters were obtained, and their values were 88.2 and 93.7%, respectively, indicating the efficiency of the proposed deep learning method in the segmentation of existing satellite images.

On the Choice of the Most Suitable Period to Map Hill Lakes via Spectral Separability and Object-Based Image Analyses

Technological advances in Earth observation made images characterized by high spatial and temporal resolutions available, nevertheless bringing with them the radiometric heterogeneity of small geographical entities, often also changing in time. Among small geographical entities, hill lakes exhibit a widespread distribution, and their census is sometimes partial or shows unreliable data. High resolution and heterogeneity have boosted the development of geographic object-based image analysis algorithms. This research analyzes which is the most suitable period for acquiring satellite images to identify and delimitate hill lakes. This is achieved by analyzing the spectral separability of the surface reflectance of hill lakes from surrounding bare or vegetated soils and by implementing a semiautomatic procedure to enhance the segmentation phase of a GEOBIA algorithm. The proposed procedure was applied to high spatial resolution satellite images acquired in two different climate periods (arid and temperate), corresponding to dry and vegetative seasons. The segmentation parameters were tuned by minimizing an under- and oversegmentation metric on surfaces and perimeters of hill lakes selected as the reference. The separability of hill lakes from their surrounding was evaluated using Euclidean and divergence metrics both in the arid and temperate periods. The classification accuracy was evaluated by calculating the error matrix and normalized error matrix. Classes’ reflectances in the image acquired in the arid period show the highest average separability (3–4 higher than in the temperate one). The segmentation based on the reference areas performs more than that based on the reference perimeters (metric ≈ 20% lower). Both separability metrics and classification accuracies indicate that images acquired in the arid period are more suitable than temperate ones to map hill lakes.

Using a Deep Learning Method for the Detection of Buildings from a High Spatial Resolution Satellite Image

Using a Deep Learning Method for the Detection of Buildings from a High Spatial Resolution Satellite Image

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands.

Identificação de processos erosivos com geotecnologias gratuitas.

Linear erosion is one of the types of water erosion that cause the most environmental problems due to the concentration of water flows that has great potential for land degradation. This work aims to identify areas of eroded soil that occur in the Paraíso River Watershed using free geotechnologies through the vectorization of erosion identified through the analysis of high spatial resolution satellite images freely available on the Google Earth platform. The results obtained point out that in the Paraíso River watershed most of the linear erosions are furrow-type features, the mildest form of this type of erosive process. A total of 463 erosion axes were identified, composed of furrows, ravines, and gullies. The temporal monitoring of images has elucidated the origin of the silting identified in a stretch of the Paraíso River near the MS-316 highway. Thus, the availability of high spatial resolution satellite images associated with the resources available for processing spatial data makes it possible to analyze extensive areas and identify erosive processes with greater agility, helping to identify the measures to be adopted to contain and/or recover the sites affected by this environmental problem.

Application of artificial intelligence technology in typhoon monitoring and forecasting

In recent years, with the emergence of new artificial intelligence (AI) technology and more observational data from automatic meteorological stations, radars and satellites, the deep learning has very broad application scenarios in the context of meteorological big data. The deep learning has powerful data learning ability and feature capturing ability of complex structures, which has now occupied an important position in the meteorological field and also become a hot topic in meteorological research. Especially, AI has shown great potential advantages in image recognition, which can provide new ideas and new directions for typhoon monitoring and forecasting. In this study, the data used include the typhoon best track data set provided by the China Meteorological Administration and the Himawari-8 and FY4 satellite image data from 2005 to 2020. We use the deep learning model to conduct the typhoon vortex identification, the determination of typhoon location and intensity, and the detection of typhoon intensity mutation with AI techniques. The main research content includes a typhoon vortex identification model based on deep image target detection, an intelligent typhoon intensity determination model based on image classification and retrieval, and a typhoon rapid intensification identification model. Then, a typhoon intelligent monitoring and forecasting system is constructed. The results show that the system can correctly identify typhoon vortices above the strong tropical storm grade in a percentage of 88.6%. The mean absolute error (MAE) and Root mean square deviation (RMSE) of typhoon intensity estimation are 3.8 m/s and 5.05 m/s, respectively, and the comprehensive accuracy of rapid intensification estimation of annual independent samples reaches 92.0%. The system is capable of performing the automatic identification, location and intensity determination, and intelligent tracking of tropical cyclones in real time by using high spatial and temporal resolution satellite images. This study may help further improve the operational techniques for typhoon monitoring and forecasting.

Beyond tree cover: Characterizing southern China's forests using deep learning

AbstractMapping forests with satellite images at local to global scale is done on a routine basis, but to go beyond the mapping of forest cover and towards characterizing forests according to their types, species and use, requires a dense time‐series of images. This knowledge is important, because ecological and economic values differ between forests. A new generation of low cost very high spatial resolution satellite images and the advent of deep learning enables improved abilities for distinguishing objects based on their structure, which could potentially also be applied to map different forest classes related to type, species and use. Here we use GF‐1 images at 2 m resolution and map six forest classes including different planted species for the karst region in southwest China, covering 806,900 km2. We compare the results with field data and show that accuracies range between 78% and 90%. We show a dominance of plantations (15%) and secondary forests (70%), and only remnants of natural forests (6%). The possibility to map forest classes based on their crown structure derived from low cost very high‐resolution satellite imagery paves the road towards sustainable forest management and restoration activities, supporting the creation of connected habitats, increasing biodiversity and improved carbon storage. No temporal information is needed for our approach, which saves costs and leads to rapid results that can be updated at a high temporal frequency.

DSMSA-Net: Deep Spatial and Multi-scale Attention Network for Road Extraction in High Spatial Resolution Satellite Images

DSMSA-Net: Deep Spatial and Multi-scale Attention Network for Road Extraction in High Spatial Resolution Satellite Images

Abrupt increase in thermokarst lakes on the central Tibetan Plateau over the last 50 years

Thermokarst lakes are an important ecosystem component in permafrost terrains; therefore, quantifying changes in these lakes is important for evaluating the water balance and carbon budget on the Qinghai-Tibet Plateau (QTP). In this study, we utilized high spatial resolution aerial and satellite images from 1969, 2010, and 2019 to quantify changes in thermokarst lakes > 0.1 ha across four regions on the central QTP. The results indicated an overall significant increase in the lake number (+158%) and surface area (+123%) for all study regions over the last five decades, despite variations in change in these trends among the different landform types. Changes in individual lake processes indicated that the above lake changes were mainly characterized by a significant increase in the number of small lakes and area enlargements of large lakes, whereas thermokarst lake drainage only occurred in some gentle slope areas. By analyzing the potential factors that drive changes in thermokarst lakes in the study region, we found that persistent climate warming and the increasing of precipitation were the most likely explanations for the observed results. With continued climate warming and permafrost degradation, we expect a persistent increase in the number and surface area of thermokarst lakes; however, the probability of lake drainage may also increase with lake expansion.

Preliminary Application of Space-Based Remote Sensing and Geospatial Technology for Investigation on the Geo-Environmental Consequences of Cyclone Aila 2009 in the NW Bangladesh

Climate change phenomena have become a great concern in the context of global change and increased frequency and magnitude of natural disasters throughout the world in the recent time. Natural disasters like floods, cyclones, storm surges etc. often cause significant losses of life, large-scale economic and social impacts, and considerable environmental damages. This paper dealt with study on the overall methodological development to investigate the consequences of climate change extremities particularly, the after effects of cyclone using Remote Sensing (RS) and Geographic Information System (GIS) technology. In this connection, individual functional components have been investigated, tested and verified. Time series multi-sensor satellite data particularly of Landsat, World view (from Google origin) have been utilized to infer information on the consequences of Cyclone Aila 2009 hitting part of southwestern Bangladesh as a test case under the present study. Information retrieval mechanism utilized is based on specially designed methodological framework using satellite-based RS technology along with GIS. Developed technical approach consists of a three-fold components – (i) Establishment of input foundation layer with high spatial details to support characterization, recognition and identification of important surface features using high resolution satellite data; (ii) Effective operational procedure to process, analyze, interpret and finally to archive the retrieved information; (iii) Functional computations were made using spatial modeler language (SML) programming environment under ERDAS Imagine image processing software. Satellite image processing, analysis operation together with image-based spectral characterization of surface features under different stressing conditions etc. have been exercised to derive useful surface information. Specially designed geospatial database has been established in GIS using ArcInfo professional software. Varieties of geospatial data from diverse sources have been incorporated categorically as column-based attributes in GIS. Dual spatial data layers have been generated in GIS for two different dates representing the pre-cyclone and post-cyclone time sequences utilizing appropriate high spatial resolution satellite images. Finally, a geospatial image-based combinational technique has been utilized employing high temporal and moderate spatial resolution time series satellite data with low temporal and high spatial resolution satellite data. Such an operation results in an improved spatial and temporal resolution of output products enabling capture of dynamics of surface features in the spatiotemporal domain providing more precision and details in the output. This study has been supplemented with necessary Ground Position System (GPS)-based ground truthing, selected field data collection and field-based group discussions outcomes.

Multiscale Diagnosis of Mangrove Status in Data-Poor Context Using Very High Spatial Resolution Satellite Images: A Case Study in Pichavaram Mangrove Forest, Tamil Nadu, India

Highlighting spatiotemporal changes occurring within mangrove habitats at the finest possible scale could contribute fundamental knowledge and data for local sustainable management. This study presents the current situation of the Pichavaram mangrove area, a coastal region of Southeast India prone to both cyclones and reduced freshwater inflow. Based on the supervised classification and visual inspection of very high spatial resolution (VHSR) satellite images provided with a pixel size of &lt;4 m, we generated time-series maps to analyze the changes that occurred in both the natural and planted mangroves between 2003 and 2019. We achieved a high mapping accuracy (&gt;85%), which confirmed the potential of classification techniques applied to VHSR images in capturing changes in mangroves on a very fine scale. Our diagnosis reveals variable expansion rates in plantations made by the local authorities. We also report an ongoing mangrove dieback and confirm progressive shoreline erosion along the coastline. Despite a lack of field data, VHSR images allowed for the multiscale diagnosis of the ecosystem situation, thus constituting the first fine-scale assessment of the fragile Pichavaram mangrove area upon which the coastal community is dependent.