Remote Sensing Applications-Society and Environment最新文献

The risk of wildfires is increasing globally and models are critical to reducing this risk. Such models require information on fuel load, a crucial factor of fire behaviour, which is generally determined using a combination of fuel age and fuel accumulation models. Traditionally, estimating fuel load relies on manually compiled fire history data (MCFH). In this paper, we introduce an approach to estimate fuel load using readily available earth observation (EO) data, MODIS MCD64A1. The approach is applied to a wildfire-prone region in Southwestern Australia from 2001 to 2021. Results suggest that MODIS produces more accurate and reliable estimates of fuel load compared with MCFH. It is effective in maintaining spatially and temporally complete records of fires, as it reports 11,019 more hectares of burned areas associated with wildfires over the study period. MODIS performs better in capturing wildfires than prescribed burns, as the spatial overlapping ratio is higher for wildfires (0.63) than prescribed burns (0.42). The high agreement between the two datasets for fuel load estimation (weighted kappa of 0.91) results from grassland covering the majority of the landscape. However, the agreement is reduced for other vegetation types — 0.24 for pine, 0.36 for mallee heath, 0.39 for shrubland, and 0.58 for forest. MODIS has lower effectiveness in detecting small and under-canopy fires such as prescribed burns, suggesting the value in combining EO and manually compiled data to obtain improved estimates of fuel load. Due to the scope of objectives, the integration of EO and MCFH has not been fully explored in this study, which will be included in our future research. This study highlights the potential of earth observation data in assessing wildfire risk as the data are easily accessible and reliable, as well as efficient and cost-effective, and they provide the opportunity to develop mitigation strategies at regional scales.

The identification of susceptible dust sources (SDSs) based on the analysis of effective factors (i.e. dust drivers) is considered to be one of the primary and cost-effective solutions to deal with this phenomenon. Accordingly, this study aimed to identify SDSs and delineate their drivers using remote sensing data and machine learning (ML) algorithms in a hotspot area in the Lower Mesopotamian floodplain in southern Iraq. To model SDSs, a total of 15 environmental features based on remote sensing data such as topographic, climatic, land use/cover, and soil properties were considered as dust drivers and fed into the four well-known ML algorithms, including linear discriminant analysis (LDA), logistic model tree (LMT), extreme gradient boosting (XGB)-Linear, and XGB-Tree-based. Dust emission hotspots were identified by visual interpretation of sub-daily MODIS-Terra/Aqua true color composite imagery (2000–2021) to train (70%) and validate (30%) ML algorithms. Considering the variability of the spatial-temporal patterns of SDSs as a result of changes in dust drivers, the modeling process was carried out in four periods, including 2000–2004, 2005–2007, 2008–2012, and 2013–2021. Our results show that dust events in the study area occur most frequently in April, June, July, and August. Overall, all ML algorithms performed well and provided reliable results for identifying SDSs. However, the XGB-Linear provided the most reliable results with an average area under curve (AUC) of 0.79 for the study periods. Precipitation was determined as the most important dust driver. The SDS maps produced can be used as a basis for the development of rehabilitation plans in the study area to mitigate the adverse effects of dust storms.

Machine learning, a subset of artificial intelligence, has emerged as a powerful tool for generating new knowledge from observations. In the realm of geographic information systems (GIS), machine learning techniques have become essential for spatial analysis tasks. Satellite image classification methods offer valuable decision-making support, particularly in land-use planning and identifying asbestos cement roofs, which pose significant health risks. In Colombia, where asbestos has been used for decades, the detection and management of installed asbestos is critical. This study evaluates the effectiveness of the RoofClassify plugin, a machine learning-based GIS tool, in detecting asbestos cement roofs in Sibaté, Colombia. By employing high-resolution satellite imagery, the study assesses the plugin's accuracy and performance. Results indicate that RoofClassify demonstrates promising capabilities in detecting asbestos cement roofs, achieving an overall accuracy score of 69.73%. This shows potential for identifying areas with the presence of asbestos and informing decision-makers. However, false positives remain a challenge, necessitating further on-site verification. The study underscores the importance of cautious interpretation of classification results and the need for tailored approaches to address specific contextual factors. Overall, RoofClassify presents a valuable tool for identifying asbestos cement roofs, aiding in asbestos management strategies.

The purpose of this article is to present the scope and the dynamics of the environmental changes unfolded in the vicinity of Mtendeli refugee camp. It presents a new method, which combines geospatial analysis of high-resolution Earth observation data (Sentinel-1&2) with ground-based observations and input from local experts. Time series classifications of annual land use/land cover in the surroundings of the camp is developed from remote data. Subsequently main transitions and trends are quantitatively achieved. This is a first study which, not only treats the land transition process in a comprehensive manner, but also tracks the changes and their main drivers on an annual scale over the lifetime of the camp (2016–2021) and the post-closure situation in 2022. Most importantly, thanks to the involvement of social studies, it unfolds the socio-economical drivers of those changes. Drawing upon a random forest algorithm and available databases, we achieve overall classification accuracies of 83.5% (2020) and 82.0% (2022). Our findings indicate an ongoing expansion of cropland between 2016 and 2021, to the detriment of natural vegetation classes. The impact of environmental restoration programs implemented in the former camp area is visible by 2022. The proposed method can be used to identify areas of environmental risk and thus support decisions linked with sustainable development and land management.

Although climate change is impacting various aspects of our environment, it is important to note that the overall risk to trees remains low, especially in urban areas like Hong Kong where the benefits of trees to society are significant. The trees planted in an urban setting are isolated and have several limiting factors including, excessive run-off, urban pollution, physical damage and limited root growth, which sometimes lead for tree failure incidents. The conventional on-site tree health assessment method is time consuming thus, requiring a remote sensing based method to effectively and routinely monitor the health status of urban trees. In this study several types of remote sensing datasets have been exploited to assess the health status of more than 700 Old and Valuable Trees (OVTs) and Stone Wall Trees (SWTs) around Hong Kong. These datasets include the data from Terrestrial LiDAR (Light Detection and Ranging) Surveys (TLS), Handheld Laser Scanner (HLS), Airborne LiDAR Surveys (ALS) and airborne multispectral data. For validation purpose, the in situ tree parameters data was also obtained from the Tree Management Office (TMO) of the Greening, Landscape & Tree Management Section (GLTMS) under the Development Bureau of the Hong Kong SAR Government. The results have indicated that over the period of four years (2017–2020) there has been a decline in the health of some target trees which can be attributed to the increased infestation rate in trees and severe weather conditions. The usage of LiDAR data has supported the fact that different tree structural forms can effectively be extracted and can help making informed decisions on the precise health conditions of urban trees.

Mapping hydrothermal alteration zones associated with porphyry copper deposits (PCDs) is crucial for identifying new exploration targets on a regional scale. Hydrothermal alteration indicator layers play a fundamental role in recognizing potential areas for PCDs, highlighting the need for precise delineation of these zones and their integration with geochemical and geological data to reduce uncertainty in mapping porphyry copper prospectivity. This study focuses on the Pariz district within the Urmia-Dokhtar Metallogenic Belt (UDMB) in southern Iran, a region known for its significant porphyry copper mineralization. First, logical operator algorithms (LOA) were applied to ASTER remote sensing data to map and distinguish argillic and phyllic alteration zones associated with PCDs. Subsequently, propylitic alteration zones associated with chlorite-epidote and propylitic alteration associated with calcite were also delineated, as were silica-rich hydrothermal alteration zones. Five evidence layers corresponding to these geologic features were generated and weighted with logistic functions, independent of expert judgment and without consideration of the spatial distribution of known mineral occurrences (KMOs). In addition, two layers of information were developed, including multivariate geochemical signatures and proximity to intrusive rocks. The geochemical analysis identified two significant factors associated with porphyry copper mineralization: Factor-I (Zn, Pb, Cu, Sn, B) and Factor-II (Mo, Cu). These factors contributed to a multivariate geochemical signature in addition to the alteration layers derived from remote sensing. Evaluation using prediction-area (P-A) plots and Normalized density index (ND) confirmed the effectiveness of all seven layers for mineral prospectivity mapping (MPM). Geometric average (GA), data-driven index overlay (IO), and deep autoencoder neural network (DEA) integrated these layers, with IO showing superior performance in identifying high potential zones, as indicated by higher prediction rates compared to other methods. Therefore, IO proves to be the most efficient approach for mapping the regional porphyry copper minerals in the Pariz district of the UDMB.

The Hyperspectral Thermal Emission Spectrometer (HyTES) offers high spatial and spectral resolution thermal infrared (TIR) airborne measurements, which are crucial for deriving land surface temperature and emissivity (LST&E). These measurements have wide-ranging applications, particularly in understanding water stress and plant water use. One critical application of TIR satellite-sensor systems is the estimation of evapotranspiration (ET), which can be derived from LST. ET is essential for modeling water fluxes from the land surface, and various algorithms leverage LST as a key boundary condition for this purpose. In this study, we apply an ET algorithm to HyTES LST data for the first time, using an analytical surface energy balance model, the Surface Temperature Initiated Closure (STIC) version 1.3. We provide an overview of the STIC model, detailing its application to HyTES data, including the integration of ancillary datasets. We demonstrate the practicality of this approach by presenting ET and LST calculations for HyTES flightlines from three field campaigns conducted in 2019, 2021, and 2023. To validate our results, we compare the derived ET and LST against available in situ measurements, including eddy covariance-derived latent heat flux and radiometer-derived LST. While this study focuses on HyTES data, the same methodology is applicable to any instantaneous LST dataset. Advancing TIR mapping of ET is crucial for applications in agriculture, water management and for understanding the evolving water cycle.

Phenology is a reliable indicator of vegetation condition and ecological changes in the environment. Plant Spectral Phenology (PSP) offers the potential for the development of automated, rapid, and wide-area vegetation monitoring systems. The spectral characteristics of plants (vegetation) are employed as metrics of PSP, which can be sensed both proximally and remotely. A key objective is to undertake a comparative analysis of the results of PSP versus those of phenology based on visual observations. The resolution of this issue is of paramount importance for the coordination of phenological studies at diverse levels (ground, surface, and remote), thus ensuring the continuity of phenological studies conducted prior to the advent of PSP. This issue is particularly pronounced in the case of evergreen conifers. The present study focuses on four evergreen conifers: Thuja occidentalis, Platycladus orientalis, Pinus sylvestris and P. nigra subsp. pallasiana. Hyperspectral imaging was performed under laboratory conditions using a Cubert UHD-185 hyperspectral camera. Concomitantly, phenological observations were conducted. The spectral time series yielded 79 chlorophyll-sensitive and carotenoid-sensitive Vegetation Indices (VIs), which were then used to construct double logistic functions. A significant proportion of the VIs exhibited a high degree of correctness with regard to the aforementioned functions, as indicated by the value of R² exceeding 0.7. The values of the principal stages of seasonal development of evergreen conifers, namely the Start of Season (SOS), End of Season (EOS), Position of Peak value (POP) and Length of Season (LOS), were calculated using double logistic functions. These stages were matched to the phenological phases of development of the experimental plants. The values of SOS, EOS, POP and LOS varied significantly depending on the VIs used as a metric as well as the evergreen conifers. The lowest variability by metrics is observed in SOS, while the maximum is observed in EOS and POP. The results obtained may be of importance for the choice of criterion for the comparison of PSP with phenology based on visual observations and the most suitable VIs for these purposes.

Sea Surface Temperature (SST) maps are necessary for managing marine resources in a climate change context, but are lacking for most of the 598 world's atolls. We assessed the feasibility of using the Landsat-8 (L8) satellite to infer SST maps for four French Polynesia atolls of aquaculture interest in Tuamotu Archipelago, namely Takaroa, Raroia, Tatakoto, and Reao. Specifically, we (1) used sensors to measure in situ the range of spatial temperature differences recorded in these four atoll lagoons; (2) calibrated and assessed the performances of SST algorithms to estimate lagoon temperature from L8 signals; (3) generated temperature maps for the lagoons and compared spatial patterns of temperature obtained from these maps with patterns highlighted by in situ sensors. Good agreements between satellite and in situ temperature data were obtained, with better results achieved when using an atoll-by-atoll optimization (average bias = −0.26 °C; RMSE = 0.55 °C). However, we also show that the range of temperature inside atoll lagoons is low, and of the same order of magnitude than RMSE achieved with SST algorithms. Because of the L8 overpass time (∼9 a.m.) and the revisit time (16 days), L8 SST could not capture the entire range of spatial differences measured in situ in the four lagoons, but could capture spatial gradients and fronts better than with few in situ sensors. Considering the achieved accuracies and the actual temperature differences at the four study sites, we discuss the usefulness of L8 derived SST maps to assist fishery and aquaculture management in atoll lagoons, as well as the possible generalization to other lagoons.

An increase in the frequency and severity of forest insect pest and disease (FIPD) outbreaks has drastically affected the health and functioning of many forest stands worldwide. This has led to an increased demand for enhanced monitoring techniques with the capabilities to identify individually infected trees before FIPD outbreaks have an opportunity to spread. In this regard, remote sensing has emerged as an indespensible tool with the capacity to map outbreaks at an individual tree level. As FIPD outbreaks have intensified, and with the advancement of monitoring capabilities, there has been a surge of interest within this field. In response to this rapid growth of interest, this review provides a comprehensive assessment of the recent advancements, challenges, and future prospects of the use of remote sensing in mapping FIPD at a tree-level. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, we conducted a systematic review encompassing 87 studies published from 2000 to May 2023. Specifically, we examined various aspects, including taxonomic characteristics, sensor types, and the analytical methods applied. Our findings revealed a signficant increase in research activity in the last few years, with the majority of these studies conducted in Asia, North America, and Europe. The most extensively studied insect pest was the Bark beetle (Ips typographus), whilst Pine wilt disease was found to be the most researched disease. Unmanned aerial vehicles and hyperspectral sensors were favoured by researchers for the majority of monitoring tasks. In terms of analytical methods, random forest (84%), artificial neural network (83%), and convolutional neural networks (93%) were found to have produced the highest levels of model accuracy. Lastly, this review underscores the indispensable role of remote sensing in facilitating the monitoring of FIPD, and identifies specific limitations and potential research gaps that need to be addressed within the field.