Remote Sensing Applications-Society and Environment最新文献

{"title":"Cloud computing and spatial hydrology for monitoring the Buyo and Kossou reservoirs in Côte d'Ivoire","authors":"Valère-Carin Jofack Sokeng ,&nbsp;Sekouba Oulare ,&nbsp;Koffi Fernand Kouamé ,&nbsp;Benoit Mertens ,&nbsp;Tiémoman Kone ,&nbsp;Thibault Catry ,&nbsp;Benjamin Pillot ,&nbsp;Pétin Edouard Ouattara ,&nbsp;Diakaria Kone ,&nbsp;Massiré Sow","doi":"10.1016/j.rsase.2024.101353","DOIUrl":"10.1016/j.rsase.2024.101353","url":null,"abstract":"<div><div>The Buyo and Kossou reservoirs are crucial for water supply, agricultural irrigation, and hydroelectric power generation in Côte d'Ivoire. However, climate change threatens the stability and availability of these water resources by increasing rainfall variability, extending drought periods, and intensifying extreme weather events. These challenges underscore the need for precise and continuous monitoring of water levels and surface areas to ensure sustainable management. Due to the scarcity of gauging stations, the objective of this study is to leverage cloud computing technologies along with altimetric and satellite data, for effective reservoir monitoring. Tools like the EO-Africa program's Innovation Lab and Google Earth Engine (GEE), along with advanced image processing software such as PyGEE-SWToolbox and AlTis, were used to process large datasets from the Sentinel-1, Sentinel-2, and Sentinel-3 satellites. These satellites delivered extensive, high-resolution imagery and altimetric data, crucial for monitoring changes in the reservoirs. The processed data were validated with in-situ measurements, yielding a Root Mean Square Error (RMSE) of less than 0.4 m and a correlation coefficient exceeding 0.90. The results highlighted water surface and level changes from 2016 to 2022, with downward trends and seasonal variations closely aligning with in-situ measurements. The study also revealed that the relationship between water levels and surface areas is influenced by both precipitation and the hydrological regimes of the Bandama and Sassandra rivers, demonstrating the complexity of water dynamics in these reservoirs. This research emphasizes the effectiveness of integrating spatial hydrology with cloud computing tools for fast and accurate monitoring of large reservoir. The use of these advanced technologies provides near real-time, reliable, and easily accessible data, offering a significant advantage for water resource management in Côte d'Ivoire.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101353"},"PeriodicalIF":3.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320186","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":"A review of spaceborne synthetic aperture radar for invasive alien plant research","authors":"Glen Shennan,&nbsp;Richard Crabbe","doi":"10.1016/j.rsase.2024.101358","DOIUrl":"10.1016/j.rsase.2024.101358","url":null,"abstract":"<div><p>Recently, a strong international focus has been placed on invasive species and their ecological, economic, and social impacts. Satellite remote sensing (SRS) for the detection of invasive alien plants (IAPs) is a promising and actively researched application of satellite-derived earth observation data. Despite its all-day, all-weather detection and mapping capability, synthetic aperture radar (SAR) data is underrepresented in these efforts. This review discussed the foundational elements and capabilities of spaceborne SAR for IAP monitoring and investigated the current state of the scientific literature concerning the detection and monitoring of IAPs by spaceborne SAR. Twenty-six published articles were discovered and analysed for trends.</p><p>The analysis revealed several key findings regarding the current state of SAR in the detection and monitoring of IAPs. Data fusion techniques, especially those combining SAR with multispectral data, are gaining popularity due to their improved performance compared to single-sensor approaches. However, the full potential of SAR imagery, particularly polarimetric SAR (PolSAR), remains underutilised in multi-sensor studies. SAR analyses demonstrated strong performance in scenarios where the IAP structure exhibited distinct characteristics compared to its surroundings, such as plants isolated on water surfaces or palms displacing mangroves, due to the unique interactions of microwave radiation with the structural characteristics of targets.</p><p>Several key principles in the deployment of SAR were identified, including band and polarisation selection, basic techniques such as grey-level thresholding, and more advanced analyses such as polarimetry. Also noted are the capabilities of SAR in enabling indirect methods, such as inundation mapping and soil modelling. Suggestions are made for future directions in consideration of recently launched and forthcoming spaceborne SAR sensors. Significant among these are fully polarimetric systems which will provide freely accessible data, offering huge opportunities for sophisticated PolSAR analyses. This data will need to be fully exploited to advance species-level IAP detection and monitoring. Examples of IAPs which may benefit from SAR approaches are given, with special attention paid to the Australian Weeds of National Significance (WoNS).</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101358"},"PeriodicalIF":3.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524002222/pdfft?md5=e1a8fa93f828beab2c58ded7bcf83c70&pid=1-s2.0-S2352938524002222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271600","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":"Exploring long term Impervious Surface Areas (ISA) dynamics using Landsat imagery, Μachine Learning and GEE: The case of Attica, Greece","authors":"Aikaterini Dermosinoglou,&nbsp;George P. Petropoulos","doi":"10.1016/j.rsase.2024.101338","DOIUrl":"10.1016/j.rsase.2024.101338","url":null,"abstract":"<div><div>Accurate data on Impervious Surface Areas (ISA) are essential for various studies concerning urban environments, as the constant proliferation of these surfaces is a noticeable result of urbanization, especially in metropolitan cities. The present study proposes a methodology approach in performing a long-term mapping of ISA changes in Attica Prefecture, Greece, from 1984 to 2022, exploiting the Landsat archive and contemporary machine learning (ML) methods of geospatial data processing, namely Support Vector Machines (SVM) and Random Forests (RF). Using Google Earth Engine cloud platform, the SVM and RF classifiers are developed and implemented for four single dates (in years 1984, 1999, 2013 and 2022). Accuracy assessment of the classification maps was based on the computation of a series of statistical metrics based on the confusion matrix, ans the McNemar's chi-square test which was used to evaluate the statistical significance of the difference in the classification maps, derived from SVM and RF classifiers. Both SVM and RF provided very accurate results, with Overall Accuracy (OA) higher than 90% and kappa coefficient (Kappa) higher than 0.8 for all classification maps, with SVM performing better in 1984 and 2022 and RF outperforming SVM in 2013. In addition, the McNemar's test confirmed the statistical significance of the research findings reported herein. Change detection results, highlighted the wide sprawl of the urban fabric, especially in sub-urban areas, surrounding the metropolitan center of Athens. The employed methodology represents a significant advancement in the application of GEE, beyond their general use, by integrating cutting-edge ML techniques with available remote sensing data to create an automated analysis process. This innovative fusion not only enhances the precision and efficiency of ISA mapping but also establishes the basis for a pioneering standard in the field by harnessing the power of advanced technologies and accessible data sources.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101338"},"PeriodicalIF":3.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420474","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":"Forest fragmentation and forest cover dynamics: Mining induced changes in the West Singhbhum District of Jharkhand","authors":"Md Saharik Joy,&nbsp;Priyanka Jha,&nbsp;Pawan Kumar Yadav,&nbsp;Taruna Bansal,&nbsp;Pankaj Rawat,&nbsp;Shehnaz Begam","doi":"10.1016/j.rsase.2024.101350","DOIUrl":"10.1016/j.rsase.2024.101350","url":null,"abstract":"<div><p>Forests play a crucial role in the global climate system by acting as important carbon storage sinks and controlling the flow of carbon between land and the atmosphere. They provide a wide range of ecosystem services, including the supply of resources and biodiversity conservation. Deforestation is a significant issue leading to the release of carbon dioxide and greenhouse gases. The destruction and fragmentation of existing habitats pose significant threats to biodiversity. This study examined land use/land cover (LULC) alterations in the West Singhbhum district between 1987 and 2021, specifically emphasizing the influence of mining operations on the local forest ecosystem. This study used Landsat satellite imagery to examine data from 1987 to 2021, emphasizing five primary classifications: water body, mining area, built-up areas, open/cropland, and forest/vegetation. The maps were reclassified into two categories, namely, “No-Forest\" and “Forest. Forest fragmentation maps were created using Landscape Fragmentation Tool (LFT) v2.0. A regression analysis was conducted to ascertain the correlation between mining growth and the reduction in forest cover. The analysis revealed increased mining areas, developed buildings, and cultivated land accompanied by a decline in forested areas and vegetation. There were substantial changes in land use, with mining areas expanding by 31.14 km² and open/cropland increasing by 30.39 km². The conversion of forested areas into agricultural zones and mining regions resulted in a 1.08% reduction in forest coverage.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101350"},"PeriodicalIF":3.8,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238081","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":"Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques","authors":"Atefeh Nouraki ,&nbsp;Mona Golabi ,&nbsp;Mohammad Albaji ,&nbsp;Abd Ali Naseri ,&nbsp;Saeid Homayouni","doi":"10.1016/j.rsase.2024.101354","DOIUrl":"10.1016/j.rsase.2024.101354","url":null,"abstract":"<div><p>Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R² = 0.89, RMSE = 0.04 cm³cm⁻³). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm³cm⁻³. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101354"},"PeriodicalIF":3.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271599","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":"Mining-induced forest cover change of Paschim Bardhaman, a mining-based district of India","authors":"Ankita Biswas,&nbsp;Sasanka Ghosh","doi":"10.1016/j.rsase.2024.101348","DOIUrl":"10.1016/j.rsase.2024.101348","url":null,"abstract":"<div><p>Mining activities are a recognized factor for Forest Cover Loss (FCL) worldwide. Huge forest cover areas are lost due to mining activities worldwide and in India. This study is conducted to identify the villages that experienced more FCL as a result of mining activities and also focuses on identifying the role of individual coal mines on FCL. Results indicate that the mining area increased to 70.79 km2 in 2020 from 25.56 km2 in 1990, and the vegetation area reduced to 149.22 km² from 271 km² at the same time. Mostly Jamuria, Barabani, Raniganj, and Pandabeswar blocks have lost large amounts of forest cover due to mining activities. Results also indicate that mining areas have increased nearly threefold and influenced the rate of FCL in the district. Village-level analysis of mining-induced FCL identified that more than ten villages had lost more than 10% of the total forest cover areas due to coal mine expansion resulting in environmental degradation. Analysis of spatial matrices indicates a fragmentation nature of vegetation cover areas of the selected coal mines and indicates that available vegetation areas are concentrated in some pocket areas. Local Indicators of Spatial Autocorrelation (LISA) based spatial patterns of mining-induced FCL show high cluster location in and around major coal mines of the area proving the role of open-cast coal mines on FCL and forest fragmentation. The analysis results may help the planners maintain the healthy environment of the affected villages by formulating alternative ways of forest cover increase.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101348"},"PeriodicalIF":3.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271596","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":"Early wildfire detection using different machine learning algorithms","authors":"Sina Moradi ,&nbsp;Mohadeseh Hafezi ,&nbsp;Aras Sheikhi","doi":"10.1016/j.rsase.2024.101346","DOIUrl":"10.1016/j.rsase.2024.101346","url":null,"abstract":"<div><p>Early detection of wildfires is essential for mitigating their impact on forests and surrounding areas. In this study, we propose a wireless sensor node system that combines multiple low-cost sensors with an artificial intelligence-based detection method for early wildfire detection. The system architecture includes temperature, humidity, and smoke sensors, as well as a wireless communication module. Four machine learning classifiers, including decision trees, random forests, support vector machines, and k-nearest neighbors, were evaluated for their effectiveness in predicting wildfire detection using a dataset collected in a forest area. The results showed that the random forest algorithm with optimum hyperparameters had the highest accuracy in classifying fire and non-fire samples (77.95% correctly classified). The proposed system provides an effective and cost-efficient solution for early wildfire detection in large forest areas.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101346"},"PeriodicalIF":3.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271598","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":"A fully automated model for land use classification from historical maps using machine learning","authors":"Anneli M. Ågren,&nbsp;Yiqi Lin","doi":"10.1016/j.rsase.2024.101349","DOIUrl":"10.1016/j.rsase.2024.101349","url":null,"abstract":"<div><p>Digital land use data before the age of satellites is scarce. Here, we build a machine learning model, using Extreme Gradient Boosting, that can automatically detect land use classes from an orthophoto map of Sweden (economic maps, 1:10 000 and 1:20 000) constructed from 1942 to 1988. Overall, the machine learning model demonstrated robust performance, with Cohen's Kappa and Matthews Correlation Coefficient of 0.86. The F1 values of the individual classes were 0.98, 0.95, 0.84, and 0.87 for graphics, arable land, forest, and open land, respectively. While the model can be used to detect land use changes in arable land, higher uncertainties associated with forest and open land necessitate further investigation at regional scales or exploration of improved mapping techniques. The code is publicly available to enable easy adaptation for classifying other historical maps.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101349"},"PeriodicalIF":3.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228719","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":"Remote sensing and big data: Google Earth Engine data to assist calibration processes in hydro-sediment modeling on large scales","authors":"Renata Barão Rossoni,&nbsp;Leonardo Laipelt,&nbsp;Rodrigo Cauduro Dias de Paiva,&nbsp;Fernando Mainardi Fan","doi":"10.1016/j.rsase.2024.101352","DOIUrl":"10.1016/j.rsase.2024.101352","url":null,"abstract":"<div><div>Mathematical modeling aids in understanding large-scale erosion and sedimentation. However, sediment transport models calibration is constrained by data scarcity. This study explores the use of remote sensing (RS) imagery to supplement observed data, addressing three key questions: (1) How can high-resolution RS data be obtained using cloud-based methods for hydro-sediment applications, considering river changes? (2) What are the benefits of RS data in data-scarce conditions? (3) How can RS data improve hydro-sediment modeling in data-deficient regions? We developed a method to acquire large-scale RS data using Google Earth Engine (<em>GEE</em>) to obtain red and infrared reflectance from satellite imagery. After filtering errors, the data were used to calibrate a hydro-sediment model. Results showed that RS data, when combined with observed data, provided similar outcomes but performed better for lower values. Calibration with RS data alone improved the Kling-Gupta Efficiency (<em>KGE</em>) by 5%–18% and correlation by 5%–15%. Key conclusions are: (I) Cloud-based calibration is superior to using limited virtual stations; (II) RS data effectively complements observed data in hydro-sediment modeling; (III) Calibration using only RS data is beneficial in ungauged basins and preferable to no calibration.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101352"},"PeriodicalIF":3.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315576","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":"Mangrove forest regeneration age map and drivers of restoration success in Gulf Cooperation Council countries from satellite imagery","authors":"Midhun Mohan ,&nbsp;Abhilash Dutta Roy ,&nbsp;Jorge F. Montenegro ,&nbsp;Michael S. Watt ,&nbsp;John A. Burt ,&nbsp;Aurelie Shapiro ,&nbsp;Dhouha Ouerfelli ,&nbsp;Redeat Daniel ,&nbsp;Sergio de-Miguel ,&nbsp;Tarig Ali ,&nbsp;Macarena Ortega Pardo ,&nbsp;Mario Al Sayah ,&nbsp;Valliyil Mohammed Aboobacker ,&nbsp;Naji El Beyrouthy ,&nbsp;Ruth Reef ,&nbsp;Esmaeel Adrah ,&nbsp;Reem AlMealla ,&nbsp;Pavithra S. Pitumpe Arachchige ,&nbsp;Pandi Selvam ,&nbsp;Wan Shafrina Wan Mohd Jaafar ,&nbsp;Jeffrey Q. Chambers","doi":"10.1016/j.rsase.2024.101345","DOIUrl":"10.1016/j.rsase.2024.101345","url":null,"abstract":"<div><p>Mangrove forests are found across the Gulf Cooperation Council (GCC) region despite challenging environmental extremes, including highly variable temperatures and hypersalinity. Understanding the biophysical and anthropogenic factors that influence mangrove forest growth is key to locate suitable areas for regeneration and afforestation activities. The main objectives of this study were to develop a mangrove forest regeneration age map that represents the age of all the existing secondary mangroves in the past 37 years (1986–2023). Long-term Landsat satellite imagery, the random forest classification algorithm, and logistic regression analyses were used to identify the existing secondary mangroves and determine the underlying drivers that contribute to the successful afforestation of mangroves in the region. Our results showed that only around 8.5% of secondary mangrove forests in the GCC region were older than 30 years, with mangroves younger than 5 years being the most abundant age class (41.3%). Saudi Arabia and Oman have the highest percentages of young mangroves, while relatively older secondary mangrove forests were most common in Bahrain, Qatar, and UAE. The current trends in overall mangrove area show that the UAE and Saudi Arabia have the largest total mangrove area among the GCC countries, followed by Qatar, Oman, Bahrain, and Kuwait. The results of the stepwise logistic regression show that the main drivers that influence mangrove regeneration are lower elevation, lower slope, higher available soil moisture, lower average temperatures, higher precipitation, greater proximity to freshwater sources, lower population density and greater distance from agricultural and urban areas. Our results aim to offer support to decision-making in selecting optimal areas for new planting initiatives in the region.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101345"},"PeriodicalIF":3.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235293852400209X/pdfft?md5=a01c787a80a404bb2b0c5b3dd88c5c4f&pid=1-s2.0-S235293852400209X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238080","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}