Remote Sensing Applications-Society and Environment最新文献

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.

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.

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.

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 different, but unknown, spectrum-specific effects of NTL (a coarse-level analysis), and in the sites of several species, with either known or unknown spectrum-specific effects of NTL (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 (Salamandra infraimmaculata) 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.

Agricultural landscape structure (e.g., the shape of fields, crop diversity, and landscape heterogeneity) greatly influences the selection of methods for large-scale crop mapping using remote sensing data. However, in-depth assessments of its impacts on crop mapping remain infrequent in the existing literature. This study investigated the optimal crop identification features and image analysis methods including pixel- and object-based approaches on crop classification, through the integration of spectral and textural features across various quantitative agricultural landscapes. In the experiments, crop fields were initially delineated into four distinct landscapes using the K-means clustering algorithm based on analyzing 13 selected landscape metrics such as PLAND, LSI and SHDI. Both pixel- and object-based approaches were then employed to conduct crop classification was then conducted using 48 selected features including 9 band reflectance, 23 vegetation indices (VIs), and 16 textures) and two image analysis methods. Specifically, five classification schemes for the different combinations of feature datasets and image analysis methods were explored to assess the impacts of crop heterogeneity on crop classification. Results indicated the five landscape metrics (e.g., SPLIT, SHEI, Average distance, etc.) performed best in assessing crop heterogeneity. In general, spectral bands and VIs had a higher contribution in the compositional heterogeneity, while textural features and VIs played a more important role in the configurational heterogeneity. VIs in the object-based approach and texture features in the pixel-based approach can improved crop classification accuracy in configurational landscapes. The findings provide a theoretical basis on selecting optimal features and image analysis methods for crop classification in complex agricultural landscapes.

Unplanned and uncontrolled industrialization leads to environmental pollution, which ends up impacting human life and destroying the economy. Especially in the era of global warming, coastal regions worldwide are the most vulnerable and hold significant ecological importance for human habitation. In 1998, the establishment of the Mongla Export Processing Zone (MEPZ) in the coastal town of Mongla Thana, which is already famous for its seaport, led the area to the challenges of salinity intrusion and the shrinking of agricultural land and its fertility. Unplanned industrialization in the area causes vegetation loss, severe droughts, and other environmental challenges, threatening local biodiversity and agricultural sustainability. In this paper, the effects of unplanned industrialization inside the Mongla EPZ on the area land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and urban heat island (UHI) spanning from 2007 to 2023 have been investigated. Along with that, a machine-learning-based artificial neural network (ANN) model was employed to forecast the situation in 2027 and 2031. Our industrial settlement analysis reveals that a substantial rise in industrial building was seen in 2015 in the EPZ area, whereas the EPZ area was almost settlement-free before 2011. With this increase in 2015, above 2% of the total municipal area faced drought, which will become over 30% by 2023. The NDVI values are decreasing year-wise, which reveals that the area is becoming less vegetation-rich. Also, the increasing industrial activities in the EPZ led to an LST increment. Our CA-ANN algorithm-based future prediction shows that about 30% of the whole municipality will face LST 27 °C by 2031. Along with that, the area's UHI value, over 2 °C higher than the rural surrounding area, will reach 6.5% by 2031. Our findings indicate that the municipal area will face a devastating future, including vegetation loss, a high probability of severe drought, and ultimately, environmental degradation. This study will help raising awareness and decision-making process to mitigate the environmental risks and supporting sustainable development.

The growing anthropogenic pressure near estuarine areas is evidence of the relevance of these systems to human well-being, especially because of their delivery of essential ecosystem services and benefits. Estuaries are composed of a rich large selection of habitats frequently organised in complex patterns. Mapping and further understanding of these habitats can contribute significantly to environmental management and conservation. The main goal of this study was to integrate different data sources to perform a supervised image classification, using remote-sensing products with different spatial resolutions and features. It was focused on the Sado Estuary, located on the Portuguese Atlantic coast. Considering the limitation of using free satellite images to map estuary habitats (i.e. limited spectral range and spatial resolution), this study uses a semi-automated supervised and pixel-based classification to overcome some of the derived classification problems. Support Vector Machine classifier was used to map the estuary for future evaluation of ecosystem services provided by each habitat. High-resolution remote sensing data (i.e., Planet Scope satellite images, aerial photographs) with different spectral and spatial features (3 m and 20 cm resolution, respectively) were used with ground truthing data to train the classifier and validate the derived maps. The first step of the classification identified broader classes of habitats in the satellite images based on visual interpretation of ground-truth data. From this output, aerial images were classified into detailed classes, the same procedure was hindered on the satellite images due to spatial resolution constraints. The sand class had the best overall accuracy (96%), due to its contrasts with surrounding objects. While the vegetation (i.e., pioneer saltmarshes) and algae classes had lower accuracy values (49.6–89.0%), possibly due to being still damp or covered in fine sediment This is a common challenge in transitional systems across land-water interfaces, such as wetlands, where the abiotic conditions (e.g. solar exposure, tides) fluctuate heterogeneously over time and space. The findings presented herein revealed the considerable success of this approach. For the purpose of local decision-making, these are relevant outputs that can be replicated in other regions worldwide.

This research aims to compare different shoreline extraction methods in assessing shoreline variability at Arroio do Sal, Southern Brazil. The methodology included the automatic extraction of shoreline positions by CoastSat and Cassie and the manual vectorization of shorelines using two different shoreline proxies. Digital Shoreline Analysis System was used to compute the shoreline displacement for each extraction method and shoreline mission. The results were compared in terms of rates, uncertainties, and methodologies. The results show that the CoastSat lines are generally displaced towards the land, while Cassie is displaced towards the sea. To concerning shape, Cassie has a more undulating shape and a greater number of indentations, with more exaggerated features, while CoastSat has a more rectilinear line, with smoother indentations next to the washouts. The RMSE values are 8.89 m for CoastSat and 27.27 m for Cassie. Despite the variations in the coastline position between the algorithms, the analyses of the rates of change have similar trends. Both algorithms establish an erosion trend for the Sentinel lines, but with different magnitudes; for the Landsat lines, both algorithms show a stable coastline, with the same average and uncertainty. Arroio do Sal can be considered a stable coastline, with rates of change in the −0.5 m–0.5 m range. Both algorithms were able to determine this general trend.

Illegal dumping is challenging for municipalities to keep track of and clean. There is limited research on the quantity of illegal dumpsites within cities. Through a manual image interpretation technique, this study used aerial imagery to quantify all illegal dumpsites within Nelson Mandela Bay Metro, South Africa. All illegal dumps were marked out in 2015 and 2021 aeroplane aerial imagery at 50 cm and 25 cm GSD, respectively. The total coverage of land surveyed was 1331 km², with an urban area of 308 km². The number of illegal dumpsites increased from 4969 to 7800 (57% increase) between 2015 and 2021. The study also showed the quantity of waste within dumps increased, dumps were spatially clustered and close to urban areas and roads. The technique presented can easily be replicated in other cities to track and monitor illegal dumping.

Artificial reefs are anthropogenic structures that are submerged in purpose to mimic some of the attributes of natural reefs. Here we describe our workflow for 3D mapping of artificial reefs, particularly shipwrecks, and release a dataset containing two 3D models of some of the most epic dive sites in Israel. Our goal is to share our 3D models and protocol with the general public and to enable the scientific and recreational community to document artificial reefs in 3D and use the models in 3D visualization and printing applications. We envision that the models will be used by divers and 3D printing enthusiasts, dive operators, Non-Governmental Organizations, and government agencies dealing with underwater monitoring and marine spatial planning.