Remote Sensing Applications-Society and Environment最新文献

{"title":"Obtaining estimation algorithms for water quality variables in the Jaguari-Jacareí Reservoir using Sentinel-2 images","authors":"Zahia Catalina Merchan Camargo ,&nbsp;Xavier Sòria-Perpinyà ,&nbsp;Marcelo Pompêo ,&nbsp;Viviane Moschini-Carlos ,&nbsp;Maria Dolores Sendra","doi":"10.1016/j.rsase.2024.101317","DOIUrl":"10.1016/j.rsase.2024.101317","url":null,"abstract":"<div><p>Satellite images are essential tools for monitoring aquatic ecosystems and assessing water quality, as they enable the measurement of parameters such as chlorophyll-<em>a</em> (Chl-<em>a</em>) concentration, phycocyanin (PC), and cyanobacteria density. These indicators aid in evaluating eutrophication processes and detecting cyanobacteria in aquatic ecosystems. This study utilized field data and images captured by the Sentinel-2 sensor from 2015 to 2022 to investigate the Jaguari-Jacareí reservoirs (JAG-JAC). Two atmospheric corrections from the Case 2 Regional Coast Color (C2RCC) processor, namely C2X and C2XC, were applied, and algorithms were developed to estimate the parameters using both <em>in situ</em> data measurements and reflectance data extracted from the images. For Chl-<em>a</em> concentration, the dataset was divided into two blocks: one for model calibration (70% of the data) and the other for validation (30% of the data). As for PC, the entire dataset was utilized to calibrate the model, and validation was conducted through cross-validation using the Automated Radiative Transfer Model Operator (ARTMO) software. Cyanobacteria density was indirectly estimated from the Chl-<em>a</em> concentrations determined in the field samples, as these variables exhibited a strong correlation, also validating the model previously proposed for the Cantareira system for estimating cyanobacteria density from Chl-<em>a</em> data. Additionally, the automatic chlorophyll-<em>a</em> products (con_chla) derived from the C2X and C2XC processors were validated. The findings revealed that the C2X processor exhibited the greatest potential for estimating water quality parameters. It was observed that the most effective algorithms were derived using the R705/R665 band ratio for Chl-<em>a</em> and the R705/R490 ratio for PC. For cyanobacteria density, the optimal algorithm was established based on the relationship between cyanobacteria density and Chl-<em>a</em> using the data obtained in this study.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101317"},"PeriodicalIF":3.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228666","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":"Dynamics of land use and land cover changes in Amibara and Awash-fentale districts, Ethiopia","authors":"Ameha Tadesse,&nbsp;Degefa Tolossa,&nbsp;Solomon Tsehaye,&nbsp;Desalegn Yayeh","doi":"10.1016/j.rsase.2024.101315","DOIUrl":"10.1016/j.rsase.2024.101315","url":null,"abstract":"<div><p>The analysis of land-use and land-cover (LULC) changes is crucial for rural development planning, food security monitoring, and natural resource conservation. This study focuses on detecting LULC changes in Amibara and Awash-Fentale districts from 1985 to 2021. We utilized five sets of Landsat data (Landsat 5 TM for 1985, 1995, 2002, and Landsat 8 OLI for 2015 &amp; 2020) and applied supervised maximum likelihood classification. Accuracy assessments revealed overall accuracies ranging from 88.9% to 95.3% for Amibara and 89.5%–93.2% for Awash-Fentale. Both districts exhibited six main LULC classes: agriculture, bareland, built-up, mixed forest, shrubland, and water bodies. In Amibara LULC changes from 1985 to 2021 revealed significant shifts, maintaining its primary bareland characteristic, concentrated agriculture, and expanding <em>Prosopis</em>-dominated shrubland due to livestock-mediated seed dispersal. Conversely, in Awash-Fentale bareland dominance decreased from 92.28% to 67.02%, while agriculture, built-up areas, and shrubland expanded. Water bodies emerged between 2015 and 2021 which is associated with the construction of Kesem Kebena dam for sugar cane farm production. The net gains were observed in shrubland (12.9%), agriculture (5.8%), mixed forest (4.1%), water bodies (1.5%), and built-up areas (0.9%), with bareland experiencing a loss of 25.3%. In conclusion, Amibara and Awash-Fentale underwent both comparable and distinct LULC shifts, featuring prevalent bareland and central agriculture, alongside <em>Prosopis</em>-driven shrubland expansion due to livestock dispersal. While mixed forest exhibited fluctuations, built-up areas and water bodies remained limited. Notably, Awash-Fentale showed higher LULC variability. Understanding these land cover changes helps assess vulnerability to climate impacts like droughts and floods, enhancing climate resilience. Insights from this study can inform sustainable land-use planning, conservation strategies, and policy interventions in the Afar region and similar areas. These observations highlight the need for integrated land management approaches that balance socioeconomic development with environmental sustainability.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101315"},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020788","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":"Predictive modelling of mineral prospectivity using satellite remote sensing and machine learning algorithms","authors":"Muhammad Ahsan Mahboob ,&nbsp;Turgay Celik ,&nbsp;Bekir Genc","doi":"10.1016/j.rsase.2024.101316","DOIUrl":"10.1016/j.rsase.2024.101316","url":null,"abstract":"<div><p>In today's world of falling returns on fixed exploration budgets, complex targets, and ever-increasing volumes of multi-parameter datasets, the effective management and integration of existing data are essential to any mineral exploration operation. Machine learning (ML) algorithms like Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machine (SVM) are powerful data-driven methods that are not implemented very often with remote sensing-derived hydrothermal alternation information and limited field datasets for mapping mineral prospectivity. The application of machine learning algorithms with satellite remote sensing data and limited field data, they have not been compared and evaluated together thoroughly in this field. A data science approach was applied to create nine predictor maps, incorporating limited field data and satellite remote sensing information. A confusion matrix, statistical measures, and a Receiver Operating Characteristic (ROC) curve were used to evaluate the prediction models efficacy on both the training and test datasets. The results suggested that the RF model exhibited the highest predictive accuracy, consistency and interpretability among the three ML models evaluated in this study. RF model also achieved the highest predictive efficiency in capturing known copper (Cu) deposits within a small prospective area. In comparison to the SVM and CNN models, the RF model outperformed them in terms of predictive accuracy and interpretability. These results imply that the RF model is the most suitable for Cu potential mapping in the Pakistan's North Waziristan region. Consequently, all the models including the RF model were used to generate a prospectivity map, which contained low to very-high potential zones, to support further exploration in the region. The newly discovered deposit inside the predicted prospective areas demonstrates the robustness and efficacy of the prospectivity modelling approach as proposed in this research for generating exploration targets.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101316"},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524001800/pdfft?md5=0d80c11e8d639fef33b2ad612b779085&pid=1-s2.0-S2352938524001800-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953599","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":"Land use and land cover changes without invalid transitions: A case study in a landslide-affected area","authors":"Renata Pacheco Quevedo ,&nbsp;Daniel Andrade Maciel ,&nbsp;Mariane Souza Reis ,&nbsp;Camilo Daleles Rennó ,&nbsp;Luciano Vieira Dutra ,&nbsp;Clódis de Oliveira Andrades-Filho ,&nbsp;Andrés Velástegui-Montoya ,&nbsp;Tingyu Zhang ,&nbsp;Thales Sehn Körting ,&nbsp;Liana Oighenstein Anderson","doi":"10.1016/j.rsase.2024.101314","DOIUrl":"10.1016/j.rsase.2024.101314","url":null,"abstract":"<div><p>Land use and land cover (LULC) analysis provides valuable information to understand environmental changes and their effects on landslide occurrence. However, LULC time series can be affected by errors in classifications that lead to invalid transitions and, therefore, to misinterpretations. One solution is to include temporal approaches that reduce the effects of invalid transitions. Here, we aimed to evaluate how such methods can improve the LULC analysis for a landslide-affected area. For that, we integrated the Random Forest (RF) class likelihoods with the temporal approach provided by the Compound Maximum a Posteriori (CMAP) algorithm, named here as RF-CMAP. Results from RF-CMAP were compared to those obtained from the traditional RF in a post-classification comparison approach. Although both methods presented high performance, with overall accuracy (OA) values greater than 0.87, RF-CMAP reached higher OA than RF for all the analysed years and corrected 99.92 km² (12% of the total area) of invalid transitions presented by the traditional RF. Furthermore, RF-CMAP was capable of correctly classifying more areas than RF in landslides (e.g., 66% and 21% for RF-CMAP and RF in 2000, respectively). Finally, this study contributes to exploring the integration between RF and CMAP algorithms to avoid invalid transitions and to assess how the existence of LULC invalid transitions can impact subsequent analyses.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101314"},"PeriodicalIF":3.8,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524001782/pdfft?md5=08c7752e03b68275e5732b08895efa93&pid=1-s2.0-S2352938524001782-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228716","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":"Trends in socioeconomic disparities in urban heat exposure and adaptation options in mid-sized U.S. cities","authors":"Shijuan Chen,&nbsp;Simon Bruhn,&nbsp;Karen C. Seto","doi":"10.1016/j.rsase.2024.101313","DOIUrl":"10.1016/j.rsase.2024.101313","url":null,"abstract":"<div><p>There is ample evidence that environmental justice communities experience high levels of extreme heat. However, it is unknown how disparities in urban heat exposure and adaptation options change over time. This study investigates socioeconomic disparities over time in urban heat exposure and adaptation options in eight mid-sized Northeastern cities. We ask: How were socioeconomic factors associated with heat exposure and adaptation options over time? We analyzed disparities at the census block group level and census block level, respectively. At the census block group level, we ran spatial regression models between socioeconomic variables, including race, income, gender, and age, and heat exposure and adaptation variables, including land surface temperature, normalized different vegetation index (NDVI), tree cover, and air conditioning ownership rate. We found that: Low median household income is always associated with high LST and low NDVI from 1990 to 2020; Low percentages of females are always associated with high LST and low NDVI from 1990 to 2020. High percentages of POC are associated with high LST in 2010 and 2020, but not in 1990 and 2000; Low median household income and low percentages of elderly are associated with lower tree covers; High percentages of POC, low percentages of elderly, and low median household income are associated with lower AC rates. In analysis at the census block level by city, we found that disparities in urban heat exposure between predominantly POC and predominantly white communities increased in most cities during 1990–2020. Predominantly POC communities consistently have lower vegetation cover over time in most cities. Disparities in vegetation cover per unit area increased in most cities, whereas disparities in vegetation cover per capita decreased in most cities. Our findings of the trends in disparities in heat exposure and adaptation are useful for forecasting disparities in the future. These findings also suggest that interventions should prioritize cities with increasing disparities in heat exposure and adaptation.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101313"},"PeriodicalIF":3.8,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141961790","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":"Effect of spatial scale, color infrared and sample size on learning poverty from aerial images","authors":"Joep Burger ,&nbsp;Harm Jan Boonstra ,&nbsp;Jan van den Brakel","doi":"10.1016/j.rsase.2024.101304","DOIUrl":"10.1016/j.rsase.2024.101304","url":null,"abstract":"<div><p>There is a growing amount of literature that focuses on using machine learning algorithms to predict poverty from satellite and aerial images on a low regional level, particularly for countries without a high-quality official statistical system. The data used for annotating images and training an algorithm are generally based on sample surveys. In The Netherlands, statistics on income and poverty are derived from tax registers resulting in a complete enumeration of the Dutch population. In this paper, we use this complete enumeration to simulate to which extent satellite or aerial images can predict poverty on low regional levels. After geocoding these households, aerial images are annotated and a deep learning algorithm is trained to predict poverty. The precision of the predictions is evaluated by comparing it with the true poverty fractions known from tax registers. The effect of different spatial scales (1-ha vs. 25-ha images), spectral bands (RGB vs. CIR), and sample sizes for the training set are compared. It is discussed how this information can be used in the production of low regional statistics on poverty in countries where high-quality official statistical systems are lacking.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101304"},"PeriodicalIF":3.8,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141952384","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 comparative analysis to assess the efficiency of lineament extraction utilizing satellite imagery from Landsat-8, Sentinel-2B, and Sentinel-1A: A case study around suez canal zone, Egypt","authors":"Hadeer Ahmed Desoky ,&nbsp;Mohamed Abd El-Dayem ,&nbsp;Mahmoud Abd El-Rahman Hegab","doi":"10.1016/j.rsase.2024.101312","DOIUrl":"10.1016/j.rsase.2024.101312","url":null,"abstract":"<div><p>Satellite remote sensing data has been extensively utilized in various fields, for example topography, geology, and hydrogeology, to extract lineament information. With notable advancements in remote sensing techniques, the process of lineament extraction and identification can now be performed in a more efficient and accurate manner, surpassing traditional manual methods. This study presents a comparative analysis utilizing Landsat-8, Sentinel-2B, and Sentinel-1A data to automatically extract lineaments. The approach includes ground truth data, an existing geological map, and a Digital Elevation Model (DEM) in addition to the data on satellite images. Through the use of a semi-totally automatic method that combines a line-linking algorithm and an edge-line detection technique, within the study area, we have determined the optimal parameters for automated lineament extraction. It has been demonstrated through further comparison and assessment of the data that using Sentinel-1A data resulted in more efficient restitution of lineaments. This demonstrates how well radar data performs in this kind of investigation when compared to optical data.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101312"},"PeriodicalIF":3.8,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839285","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":"Integrated GNSS-derived precipitable water vapor and remote sensing data for agricultural drought monitoring and impact analysis","authors":"Piyanan Pipatsitee ,&nbsp;Sarawut Ninsawat ,&nbsp;Nitin Kumar Tripathi ,&nbsp;Mohanasundaram Shanmugam","doi":"10.1016/j.rsase.2024.101310","DOIUrl":"10.1016/j.rsase.2024.101310","url":null,"abstract":"<div><p>Agricultural drought is a natural disaster that impacts soil water deficiency, plant water stress, and yield loss. It has several effective drought indices to monitor the impact on agriculture, particularly the evapotranspiration deficit index (ETDI). However, this index has exposed the inconsistency of spatial potential evapotranspiration (PET) because of the restricted spatial distribution of meteorological stations and the influence of spatial heterogeneity. The present study aims to develop the fine spatial PET using the Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and remote sensing data for enhancing the ETDI and determining the impacts of drought on sugarcane yield. The grid PET (GPET) model is developed by the correlation between the land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS LST) and the PET from the Revised Potential Evapotranspiration (RPET) model as the ground observations to estimate daily PET at 30-m spatial resolution using spatial extrapolation technique. In addition, the actual evapotranspiration (AET) was evaluated using the Surface Energy Algorithms for Land (SEBAL) algorithm. Both spatial PET and AET were utilized to compute the ETDI as an agricultural drought index. Then, the ETDI was correlated with sugarcane yield to investigate the impact of drought on yield. The results indicated that the GPET model had a strong correlation with the RPET model (R² = 0.73 and RMSE = 0.84 mm) and relatively good accuracy (RSR = 0.57 and NSE = 0.68). This proposed model could be applied to compute the ETDI with fine spatial resolution. Moreover, the normalized yield of sugarcane exhibited a negative correlation with ETDI in the period from March to April 2020 with a strong relationship (r = −0.83). Therefore, the ETDI is an appropriate index for drought monitoring and determining the effects of drought on yield. These findings are useful for supporting the decision-makers to enhance the national policies for water management in agriculture.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101310"},"PeriodicalIF":3.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846722","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":"Predicting forest fire probability in Similipal Biosphere Reserve (India) using Sentinel-2 MSI data and machine learning","authors":"Rajkumar Guria ,&nbsp;Manoranjan Mishra ,&nbsp;Richarde Marques da Silva ,&nbsp;Minati Mishra ,&nbsp;Celso Augusto Guimarães Santos","doi":"10.1016/j.rsase.2024.101311","DOIUrl":"10.1016/j.rsase.2024.101311","url":null,"abstract":"<div><p>The global escalation in forest fires, characterized by increasing frequency and severity, results from a complex interplay of natural and anthropogenic factors, exacerbated by climate change. These fires devastate habitats, threaten species, reduce biodiversity, disrupt natural cycles, and harm local ecosystems. The impacts are particularly damaging in biological reserves. The Similipal Biosphere Reserve (SBR) in Odisha State is one of India’s major forest fire hotspots, experiencing forest fires almost every year. The objective of this study is to develop a predictive model using Sentinel-2 MSI data and machine learning (ML) techniques to estimate the probability of forest fires in the SBR, India, thereby enhancing disaster management and prevention in the region. This research maps and quantifies forest fire intensity by leveraging ML algorithms, namely Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Random Forest (RF). To develop a Forest Fire Probability (FFP) map, twenty conditioning factors, along with pre- and post-fire Normalized Burn Ratio (NBR) and delta Normalized Burn Ratio (dNBR), were utilized. Furthermore, four statistical methods—Mean Absolute Error, Mean Square Error, Root Mean Square Error, and Overall Accuracy—were employed to analyze the FFP. The results were validated using the Area Under Curve (AUC) method. The analysis identifies 2021 as the year with the highest incidence of forest fires, accounting for 29.19% of the occurrences. Among the models, the GBM exhibits superior performance, highlighting its efficacy in handling large, multidimensional datasets. Predictive mapping suggests that approximately 1400–1500 km², or 25–30% of the studied area, faces a high to very high risk of forest fires.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101311"},"PeriodicalIF":3.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141838790","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":"The power of spectrally enhanced artificial night-time lights data: Assessing NTL risks along the urban-natural interface","authors":"Nataliya Rybnikova ,&nbsp;Dani Broitman","doi":"10.1016/j.rsase.2024.101309","DOIUrl":"10.1016/j.rsase.2024.101309","url":null,"abstract":"<div><p>Artificial night-time lights (NTL) have long been known for their adverse effects on humans and the environment. Recent studies report that the severity of NTL impact on organisms is associated not only with its intensity but also a spectrum. The spectral resolution of freely available satellite NTL data is restricted to red, green, and blue sub-spectra, which are significantly wider than the ranges of vulnerability, reported by laboratory studies for various species. The present study is the first attempt to overlap spectrum-specific NTL data, describing the intensities of light emitted by different lamp types with relatively narrow emission peaks, with the sites where species vulnerable to specific NTL sub-spectra were detected. We overlap those light intensity maps with increasingly detailed maps of natural areas located along the urban-natural interface of the Haifa region. We analyze light pollution in the ecological corridors, which host numerous species with <em>different, but unknown, spectrum-specific effects of NTL</em> (a coarse-level analysis), and in the sites of several species, with either <em>known or unknown spectrum-specific effects of NTL</em> (a fine-level analysis). We show that a considerable part of the ecological corridors is polluted by metal halide and high-pressure sodium lamps which may negatively influence plants, bees, sea turtles, birds, and mammals. One habitat site of the Near Eastern fire salamander (<em>Salamandra infraimmaculata</em>) is polluted by lamps with green-light emission peaks which may explain the low reproductive success of this population. Despite the study limitations, related to the region-specific NTL data of spectrum-specific resolution and scarcity of evidence about the spectrum-specific NTL harmful effects on organisms, we believe that the obtained results would contribute to the elaboration of more informed fine-tuned artificial lighting policies which would diminish the burden of urban built-up zones on their neighboring natural areas.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101309"},"PeriodicalIF":3.8,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848639","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}