Remote Sensing Applications-Society and Environment最新文献

{"title":"Revealing the impact of spatial bias in survey design for habitat mapping: A tale of two sampling designs","authors":"Stanley Mastrantonis ,&nbsp;Tim Langlois ,&nbsp;Ben Radford ,&nbsp;Claude Spencer ,&nbsp;Simon de Lestang ,&nbsp;Sharyn Hickey","doi":"10.1016/j.rsase.2024.101327","DOIUrl":"10.1016/j.rsase.2024.101327","url":null,"abstract":"<div><p>Submerged aquatic vegetation, referring to benthic macroalgae and plants that obligately grow underwater, are critical components of marine ecosystems and are frequently found to provide preferential recruitment habitats. The mapping and monitoring of aquatic vegetation through remote sensing and machine learning is becoming an important aspect of managing coastal environments at scale. Accurate mapping and monitoring require robust sampling and occurrence data to assess predictive error and quantify submerged vegetation extents. The form of ground truthing survey design (preferential, random, grid-based or spatially balanced) could significantly influence predictive model outcomes and the overall accuracy of mapping and monitoring. Here, we test and contrast mapping aquatic vegetation extent ground-truthed using two different sampling designs: we used both preferential and spatially balanced sampling designs across four coastal sites along the midwest of Australia. We validate the map outcomes using spatial cross-validation and demonstrate that spatially balanced ground truthing significantly outperforms preferential sampling designs regarding modelled extent and map accuracy. In our comparison, we found that, on average, preferential designs overestimated vegetation extent by 25 percent compared to balanced designs and achieved an average kappa statistic, F1 score and Area under the Curve of 0.48, 0.615 and 0.517, respectively; whereas balanced designs achieved a kappa statistic, F1 score and AUC of 0.84, 0.85 and 0.83 respectively. We strongly recommend that sampling designs for remote sensing-derived habitat models be spatially balanced where habitat extent is proposed as a metric for monitoring.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101327"},"PeriodicalIF":3.8,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring invasive exotic grass species in ecological restoration areas of the Brazilian savanna using UAV images","authors":"Marcos Vinicius Rezende de Ataíde ,&nbsp;Silvia Barbosa Rodrigues ,&nbsp;Tamilis Rocha Silva ,&nbsp;Augusto Cesar Silva Coelho ,&nbsp;Ana Wiederhecker ,&nbsp;Daniel Luis Mascia Vieira","doi":"10.1016/j.rsase.2024.101328","DOIUrl":"10.1016/j.rsase.2024.101328","url":null,"abstract":"<div><p>Identifying and monitoring invasive exotic grasses (IEG) is critical for the ecological restoration of grasslands and savannas, as they are the main barrier to the successful recovery of native grasslands and savannas. The integration of high-resolution remote sensing data, acquired through UAVs (Unmanned Aerial Vehicles), with machine learning algorithms is advancing restoration monitoring. The present study aimed to estimate IEG cover and identify plots with different invasive species dominance in ecological restoration areas in the Brazilian savanna. For ground truth, species cover estimates were carried out in plots through point-line intercept sampling. Then, the areas were classified according to the dominance of each invasive species (&gt;40% vegetation cover) or of a mix of native species. A multispectral camera onboard a UAV was used to acquire images in the visible to near-infrared spectrum. From the images, vegetation indices and texture metrics were derived as predictor variables. The Random Forest (RF) algorithm was used to estimate the percentage of invasive species cover and to classify plots in terms of species dominance. The final RF regression for invasive species cover percentage presented an R² of 0.71 and selected the blue band, NIR and Ratio Vegetation Index (RVI) as the most important variables. The overall accuracy of plot classification according to species dominance was 84%. The most prominent predictors were the Green Chlorophyll Index (GCI), the atmospherically resistant vegetation index (ARVI), and the RVI. The structural and photosynthetic characteristics of exotic and native species influenced the spectral responses. In conclusion, multispectral images acquired with UAV can be used to estimate the proportion of invasion in restoration sites and to map areas dominated by different invasive grass species in grasslands and savannas. This is a useful tool for evaluating restoration success and can help indicate areas that require management interventions.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101328"},"PeriodicalIF":3.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping forest phenological shift in Nilgiri Biosphere Reserve, Western Ghats: Response to climate change","authors":"Bodi Surya Pratap Chandra Kishore ,&nbsp;Amit Kumar ,&nbsp;Purabi Saikia","doi":"10.1016/j.rsase.2024.101325","DOIUrl":"10.1016/j.rsase.2024.101325","url":null,"abstract":"<div><p>In the present study, the spatiotemporal alterations in phenological metrics were analyzed in different forest types of Nilgiri Biosphere Reserve (NBR), Western Ghats, India with special reference to the impacts of changing climate on the forest phenology over two-phase periods (Phase 1: 2001–2010 and Phase 2: 2011–2020). Three phenological measures including the start/end/length of the season (SOS/EOS/LOS) were obtained using 16 days of MODIS Enhanced Vegetation Index (EVI) dataset over the two decades. The study exhibited a gradual delay in SOS during phase 1, and an advancing SOS during phase 2 in the tropical forests of NBR. Delayed EOS was observed in all forest types in NBR barring Moist Deciduous Forests (MDF) during phase 1, while opposite trend of advancing EOS was observed in deciduous forests (MDF and DDF: Dry Deciduous Forests), but delayed EOS was observed in evergreen forests (SEF: Semi Evergreen Forests and WEF: Wet Evergreen Forests) during phase 2. These variations in forest phenology are linked with an increase in annual mean temperature (0.01 °C year⁻¹) along with a decrease in annual mean precipitation (3.97 mm year⁻¹) during 1950–2018 as observed using IMD-based meteorological datasets. The results highlighted reduction in the total LOS with a delayed SOS and advanced EOS in NBR, which were prevalent during phase 1 conversely to phase 2 due to the varied intensity of changes in climatic conditions. However, disaggregating decade-long intervals into 5-year segments enables a finer resolution analysis of phenological trends. The study contributes to the development of long-term strategies for forest ecosystem restoration under the influence of global climate change by providing an insightful understanding of the non-systematic shifts in forest phenology attributed by rising warming impacts and erratic precipitation patterns.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101325"},"PeriodicalIF":3.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atmospheric pollutants in Rosario, Argentina analysed through remote sensing: Wildfires and COVID-19 lockdown effects","authors":"M.F. Valle Seijo ,&nbsp;M.I. Micheletti ,&nbsp;L.A. Otero ,&nbsp;R.D. Piacentini","doi":"10.1016/j.rsase.2024.101326","DOIUrl":"10.1016/j.rsase.2024.101326","url":null,"abstract":"<div><p>Spatial-temporal dynamics of atmospheric pollutants can be analysed by space-based observations, contributing to environmental management and public health interventions. The influence of wildfires and anthropogenic activities on air quality is studied for the city of Rosario and its surroundings, including both urban and non-urban areas. Utilizing advanced satellite-based observations, we assess Aerosol Optical Depth (AOD) and Nitrogen Dioxide (NO₂). The study employs Multi-Angle Implementation of Atmospheric Correction (MAIAC) and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra and Aqua satellites for AOD analysis. NO₂ measurements were derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. Backward trajectory analysis using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) shows the connection between fire pixels and air masses reaching Rosario. During the COVID-19 lockdown, a period with no significant fire events in the studied region was selected. This provided, for the first time, a baseline on the AOD median value of 0.05 for Rosario city and its surroundings, while NO₂ total column median values were close to 3.00 x 10¹⁵ molecules/cm². In a business-as-usual scenario, AOD increased by approximately 52.8% (March 2022) up to 111.3% (March 2019). NO₂ median values remain almost the same (March 2019) or reached a median value of 3.38 x 10¹⁵ molecules/cm² (March 2022). During wildfire events, such as March 2023, AOD surged by around 50.9%–108.6% compared to the business-as-usual scenario (March 2019 and March 2022, respectively). NO₂ median values ranged from 3.52 x 10¹⁵ molecules/cm² (March 2023) to 3.66 x 10¹⁵ molecules/cm (March 01, 2020 to March 19, 2020). NO₂ levels correlated with intense fire periods. The analysis provides valuable insights into the complex interplay between natural events, human activities, and air quality dynamics in the region.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101326"},"PeriodicalIF":3.8,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone","authors":"Xinyue Wang ,&nbsp;Jun Yang ,&nbsp;Wenbo Yu ,&nbsp;Huisheng Yu ,&nbsp;Xiangming Xiao ,&nbsp;Jianhong Cecilia Xia","doi":"10.1016/j.rsase.2024.101324","DOIUrl":"10.1016/j.rsase.2024.101324","url":null,"abstract":"<div><p>Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101324"},"PeriodicalIF":3.8,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of lineament extraction: Analysis and comparison of digital elevation models in the Ait Semgane region, Morocco","authors":"Mohamed Ali EL-Omairi ,&nbsp;Abdelkader El Garouani ,&nbsp;Ali Shebl","doi":"10.1016/j.rsase.2024.101321","DOIUrl":"10.1016/j.rsase.2024.101321","url":null,"abstract":"<div><p>An automated lineament extraction model was successfully applied in the mountainous region of Ait Semgane, Morocco, enabling the identification of geological and tectonic linear features. Our research implemented several approaches including Topographic Position Index (TPI), shading, and Digital Elevation Models (DEMs), as input for the lineament extraction algorithm and applied to various Dems. We aimed to compare all of these strategies to determine the optimal method and the most favorable input DEM. Results revealed variable performance among methods, emphasizing the importance of choosing the optimum method based on specific objectives. TPI and Hillshade methods showed high sensitivity in detecting lineaments, while ALOS PALSAR and Sentinel 1 InSAR datasets were effective for subtle features. Lineament density exhibited specific orientations for highly-dissected zones, with TPI highlighting NE-SW and E-W orientations. Lineament orientations demonstrated consistency with established geology, confirming pre-existing tectonic knowledge. Cartographic analysis of faults emphasized the success of the SRTM DEM model with the TPI method, highlighting significant faults. Results also revealed a concentration of faults in the NW and southern sectors, corroborating bibliographic references and the well-documented tectonic setting of the study area. This automated methodology facilitated lineament extraction in unmapped areas, reinforcing the validity of the undertaken cartographic analysis.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101321"},"PeriodicalIF":3.8,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235293852400185X/pdfft?md5=28e27eb290afcce91240137b40c50da1&pid=1-s2.0-S235293852400185X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A geospatial web service for small pelagic fish spatial distribution modeling and mapping with remote sensing","authors":"Spyros Spondylidis ,&nbsp;Marianna Giannoulaki ,&nbsp;Athanassios Machias ,&nbsp;Ioannis Batzakas ,&nbsp;Konstantinos Topouzelis","doi":"10.1016/j.rsase.2024.101322","DOIUrl":"10.1016/j.rsase.2024.101322","url":null,"abstract":"<div><p>Small pelagic fish are an essential resource for coastal countries worldwide and their assessment and monitoring are a key part of successful fisheries management. Advances in marine satellite remote sensing can contribute to the creation of methodologies for continual small pelagic fish spatial distribution monitoring that can act as supplementary tools for fisheries management decision making, enhancing traditional field practices. In this work a comprehensive Geospatial Web Service (GWS) is proposed that utilizes Sentinel-3 data to publish Spatial Distribution Modeling (SDM) maps for anchovy (Engraulis encrasiculous) and sardine (Sardina pilchardus). The proposed GWS is developed through the sole use of open-source programming languages and software and provides fishery management related data through various parameters: A) Sea Surface Temperature (SST) and Chlorophyll-a Concentration (CHL), B) mesoscale oceanic fronts and C) the SDM maps for the target species. The SDM results are produced through a Random Forest algorithm and utilized oceanographic parameters relevant to the ecological needs of the target species (CHL, SST, oceanic fronts and bathymetry). All data are processed and gap-free through a spatiotemporal DINEOF interpolation, allowing the continuous provision of information independently of the weather conditions. Furthermore, the service integrates auxiliary information, such as weather and sea state forecasts, that aim to contribute to maritime safety for effective decision-making. The resulting GWS offers an easy to use and interactive tool that bridges the gap between the scientific community and the decision makers. The utilization of satellite remote sensing enhances the scalability of the proposed service for future improvements and continuous monitoring.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101322"},"PeriodicalIF":3.8,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is the actual composition of specific land cover? An evaluation of the accuracy at a national scale – Remote sensing in comparison to topographic land cover","authors":"Joanna Bihałowicz,&nbsp;Wioletta Rogula-Kozłowska,&nbsp;Paweł Gromek,&nbsp;Jan Stefan Bihałowicz","doi":"10.1016/j.rsase.2024.101319","DOIUrl":"10.1016/j.rsase.2024.101319","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Satellite imagery allows us to capture and collect land cover information for increasingly large areas. This allows us to represent current land cover on maps in a simple and standardized way; however, any land cover determined in this way is subject to some algorithmic uncertainty. This paper aims, for the first time, to indicate the magnitude of this uncertainty through the empirical probability distribution of a given land cover at a given location. By analyzing 3 data sources, i.e. the Corine Land Cover map, the POLSA land cover map and the classic map - the BDOT10k database of topographic objects. Empirical distributions of the occurrence of land cover class data in areas with a given land use on a topographic map were determined. The work was carried out on a large scale, i.e. on the maximum possible sample for Poland, i.e. on the area of the whole country. This makes it possible to introduce and quantify uncertainties. Spatial analyses were carried out using satellite-based methods to determine land cover or using a topographic map. This work and its results will be useful to all users who want to assess the occurrence of a phenomenon in a given area, taking into account the uncertainty of the land cover, and thus obtain more accurate and reliable results. It also provides, for the first time, a methodology for verifying such map correspondences, which can be replicated in work by other researchers, using the confusion matrix and as evaluation metrics the true positive rate (TPR) and weighted accuracy have been adopted. The paper proposes a link between land cover classes in all databases. It was shown that the TPR for BDOT10k was higher than 50% only with CLC Level 1 (72.0%) and POLSA Land Cover (61%), while the TPR for RS classes for each remote sensing data was always higher than 60% with BDOT10k. The class with the highest remote sensing classes was related to water, especially marine (92.0% for POLSA and 85.3% for CLC level 3), arable land (98% for POLSA, lowest for CLC level 3 (80%), and forests (coniferous POLSA – 89%, CLC level 1 and 2–85%), while low values were obtained for wetlands, peatbogs. The authors do not state which land cover approach is better, as each may have multiple uses, but the values presented in this work must raise awareness of uncertainties in land cover and critical implementation in decision-making processes for multiple areas of human activity. The study provides ready-to-use values of the probability of a given land cover class being present on a topographic map, given that remote sensing has classified it as such. These functions can also be used in reverse, to determine the probability of a given land cover class being present in remote sensing, given that a specific class has been identified on a topographic map. The results of the consistency assessment, with the composition structure, can be used by a wide range of users, including public administration, land managers, land architects, public ","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101319"},"PeriodicalIF":3.8,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524001836/pdfft?md5=d0a05e1497f836a49c8eb60c42d40a97&pid=1-s2.0-S2352938524001836-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital mapping of soil quality and salt-affected soil indicators for sustainable agriculture in the Nile Delta region","authors":"Mohamed M. Metwaly ,&nbsp;Mohamed R. Metwalli ,&nbsp;Mohammed S. Abd-Elwahed ,&nbsp;Yasser M. Zakarya","doi":"10.1016/j.rsase.2024.101318","DOIUrl":"10.1016/j.rsase.2024.101318","url":null,"abstract":"<div><p>The study addresses the challenge of sustainable land management, which is crucial for agricultural production and soil quality (SQ), in the face of land degradation that negatively impacts crop production and SQ. The goal of the current work is to assess SQ using digital soil mapping (DSM) in Kafr El-Sheikh province, Egypt, to develop a framework employing two methods for soil quality index (SQI) assessment: the total data set (SQI-TDS) and a selected minimum data set (SQI-MDS) to choose indicators, along with a weighted additive SQI (<em>SQI</em>_<em>w</em>), and a Random Forest (RF) model to predict and map the SQI, as well as the salt-affected soil indicators (EC, pH, and ESP). This framework uses remote sensing data: time series of Sentinel-1 (S-1) and Sentinel-2 (S-2) greenest pixel composite. Additionally, we incorporated environmental covariates derived from S-1 and S-2 imagery to understand their influence on SQ, which in turn informs land management practices, land degradation assessment, and crop productivity. The findings reveal a clear negative impact of salinity and alkalinity on SQ. We demonstrate the importance of Variance Inflation Factor (VIF) and Sequential Feature Selection (SFS) techniques for improving the performance of the RF model used for prediction. Notably, the greenest pixel composite imagery proved promising for SQI assessment using DSM beneath vegetation cover, crop mapping, and land-use dynamics. The precise SQI obtained is essential for decision-makers to detect land degradation, develop sustainable agricultural management strategies, and assess their appropriateness for developing plans and strategies to increase agricultural productivity.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101318"},"PeriodicalIF":3.8,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating temporal-aggregated satellite image with multi-sensor image fusion for seasonal land-cover mapping of Shilansha watershed, rift valley basin of Ethiopia","authors":"Assefa Gedle ,&nbsp;Tom Rientjes ,&nbsp;Alemseged Tamiru Haile","doi":"10.1016/j.rsase.2024.101320","DOIUrl":"10.1016/j.rsase.2024.101320","url":null,"abstract":"<div><p>Accurate land-cover mapping in regions with frequent cloud-cover and rapidly changing agricultural land cover by crop growth cycles cannot be guaranteed by use of single sensor images, or an image from a single-acquisition-date. This study addressed these challenges by applying temporal-aggregation of single sensor image features that is integrated with multi-sensor image fusion. Results of land-cover classification target fallow, growing, and harvest/post-harvest agricultural seasons. Satellite based features used were frequency bands of Sentinel-1 (S1) and Sentinel-2 (S2), including vegetation indices (VIs) and biophysical variables (BPVs). Temporal aggregation improved classification accuracy. The single-acquisition-date S2 image, overall accuracy (OA) ranged from 0.81 to 0.85, increased to 0.86 to 0.87 after temporal-aggregation. Meanwhile, for single-acquisitions of S1, OA ranged from 0.44 to 0.79 increased to 0.6 to 0.86 across respective seasons. Fusing temporally aggregated S1 and S2 image features including VIs and BPVs increased OA up to 0.90. Selecting 11, 8, and 10 out of 18 optimum numbers of features for fallow, growing, and harvest/post-harvest seasons respectively improved OA by 3%, 2%, and 1.86%. PCA fusion of the temporally aggregated best performing feature set enhanced harvest/post-harvest season, fallow, and growing seasons with OA of 0.98, 0.96 and 0.94 respectively. Accuracy was enhanced when selecting different best performing feature set for the three seasons. The study enhanced knowledge of advanced remote sensing for agricultural land cover mapping, with practical implications of land monitoring and management.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101320"},"PeriodicalIF":3.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524001848/pdfft?md5=56d0f1810f25616f8ddb5489c0129764&pid=1-s2.0-S2352938524001848-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}