Remote Sensing Applications-Society and Environment最新文献

Water hyacinth is one of the most aggressive alien invasive plants, which invades freshwater resources and destroys native biodiversity. The plant proliferates rapidly over a short space of time, forming thick dense layer on the surface of freshwater bodies. Monitoring and management of water hyacinth is essential to protect water resources affected by the presence of this plant. The study assessed the effectiveness of biological agent (Megamelus scutellaris) applied in the Hartbeespoort Dam from pre (2016–2017) and post (2018–2023) biological control to manage water hyacinth spread and proliferation. In achieving this main goal, the study used advanced cloud-computing machine learning techniques and multi date Sentinel-2 Multispectral Instrument (MSI) data to monitor the effectiveness of such biological control. During this analysis, remote sensing data was acquired for two time periods namely: pre-intervention (2016–2017) and post intervention (2018–2023) to establish variation in the spatio-temporal distribution of water hyacinth in the Hartbeespoort Dam using various machine learning techniques (Support Vector Machine (SVM), Classification and Regression Tree (CART), Random Forest (RF) and Naïve Bayes (NB)) in Google Earth Engine cloud computing platform, and assessed the spectral separability of water hyacinth from numerous land cover types, within and around the Hartbeespoort Dam using the Sentinel-2 derived spectral reflectance curves. The results indicated that the extent of water hyacinth area coverage decreased from 15% to below 6% between the period of 2018 and 2021, however, a significant increase was noted between November 2022 and April 2023, after the biological control was introduced. The significant increase observed during the time period of November 2022 and April 2023 can be attributed to nutrient rich water discharging into the dam from the Crocodile River during the time of flooding reported in November 2022. The result further indicate that RF produced the highest overall accuracies ranging between 93.42% and 98.70%. While NB produced the lowest accuracies ranging between 87.76% and 92.08%. These findings underscore the relevance of new generation satellite dataset and machine learning algorithms in monitoring the effectiveness of the biological controls of alien invasive spread provide information regarding alien plant invasion. Therefore, aligning with Sustainable Development Goals (SDG 6) emphasizing on the importance of implementing effective control measures to control invasive species and their impact on water resources thus ensuring the sustainability of freshwater ecosystems and the availability of clean water resources.

Urban areas worldwide are increasingly facing challenges related to land subsidence, a phenomenon exacerbated by uncontrolled groundwater extraction and urban expansion. This research focuses on the Tehran plain, Iran's capital city, where significant subsidence has been observed due to uncontrolled migrations influenced by various economic and political factors. This expansion has increased demand for energy, notably water, leading to irregular water withdrawals from underground sources and, consequently, land subsidence. Monitoring this subsidence, particularly its effects on urban infrastructure, has become a critical challenge. This research first reviewed the existing body of knowledge related to subsidence measurement in the Tehran plain with an emphasis on their findings and limitations and then used radar images to study the subsidence patterns in the Tehran plain from 2016 to the end of 2020. Finally, the results collaborated by optical imagery analysis to find the relationship between surface change detection and spatiotemporal distribution of subsidence. As a result, through processing Sentinel-1A SAR images, consistent vertical displacements (subsidence) were observed, especially in areas heavily reliant on groundwater from wells, with some areas experiencing a rate of more than −20 mm/year. Horizontal displacement, however, was approximately about ±8 mm/year. Also, our results show that the subsidence rate in this plain has decreased in recent years. Therefore, the study integrated multispectral satellite data to clarify this issue and compensate for missing groundwater level data, specifically the Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Moisture Index (NDMI). These datasets were used to monitor changes in vegetation cover distribution and moisture in response to the variations of groundwater depth over time. The results of this research can be beneficial in adequately managing groundwater resource utilization to reduce the potential damage to infrastructure and the environment.

Mangrove ecosystems, although limited in diversity and area compared to tropical forests, provide essential ecological and economic services, such as carbon sequestration and coastal protection. The Sundarbans mangrove forest, shared by Bangladesh and India, is one of the largest mangrove ecosystems in the world and is crucial for biodiversity, economy, and climate regulation. Unfortunately, this ecosystem has been under severe stress over the years, with alarming rates of deforestation leading to habitat loss and a decline in ecosystem services. This study analyzes the spatiotemporal changes in the Sundarbans mangrove forest coverage from 1973 to 2023 using supervised image classification on Landsat images. It also assesses the relationship between the Normalized Difference Vegetation Index and Land Surface Temperature in the Sundarbans using MODIS data which were extracted in Google Earth Engine. It finds that, despite the loss of denser mangrove areas, an improvement in overall vegetation health is visible, which suggests a natural resilience within the Sundarbans mangrove forest. The Land Surface Temperature result shows a weak but statistically significant negative correlation with the Normalized Difference Vegetation Index, indicating that the depletion of the Sundarbans mangrove forest could have an impact on the area’s surface temperature. As such, the study regressed the Normalized Difference Vegetation Index on Land Surface Temperature. The results confirm that although the Normalized Difference Vegetation Index has a statistically significant negative impact on Land Surface Temperature, the Coefficient of Determination is low. This suggests that other factors such as water bodies that intersect with the mangrove forest in the area may play an important role in influencing Land Surface Temperature. The paper reveals a nuanced picture of the Sundarbans’ ecological state, with both declining mangrove densities and signs of vegetation recovery. It highlights the need for comprehensive conservation strategies to mitigate further ecosystem degradation.

The Sinabung volcano on Sumatra Island stands out as one of the most active volcanos, having recorded the highest number of eruptions since it resumed activity in 2010. The eruptive activities have caused significant deformations on the volcano's surface. This research aimed to analyze, cluster, and forecast its deformation patterns based on Sentinel-1 A time series data from 2016 to 2023. The differential interferometry synthetic aperture radar (DInSAR) technique was used to monitor monthly deformations and to create time series data. A forest-based forecast (FBF) model was used to predict the rate of changes in volcano surface inflation from January 2024 to December 2027. The deformation times series patterns were also analyzed and clustered into three regions to reveal areas with similar deformation behaviors. The results indicated that Mount Sinabung's deformation is an overall continuous sporadic phenomenon where random ground inflation and deflation were recorded throughout the area with an average deformation rate ranging from 0.06 to 0.32 cm/month and an overall average of 0.197 cm/month with a standard deviation of 0.96 cm, confirming that the volcano is inflating. The highest single-pixel monthly inflation of 4.62 cm was recorded in 2023, while the highest deflation occurred in 2018 at −4.58 cm. The FBF model predicted a gradual and increasing inflationary pattern at the rate of 0.54 cm/month for 2024–2027, higher than the average of the observed data. The deformation within the lava dome and caldera poses a significant risk and could lead to wall collapses and landslides in the crater dome, potentially triggering explosive eruptions. The outcomes of this research serve as valuable supporting information and offer an early warning of potential volcanic disasters in the future.

Thermal Discharge from power plants in coastal waters may significantly influence the aquatic marine environment. Today, remotely sensing data is considered one of the primary sources to monitor the thermal pollution of power plants. This research quantitatively assesses the accuracy of retrieved Landsat/TIRS Sea Surface Temperature (SST) and effectively uses archival Landsat data to monitor the thermal pollution from Baniyas Thermal Power Plant (TTP) in the Syrian coastal water for 40 years from 1984 to 2023. The results show a strong linear correlation between Landsat/TIRS retrieved and in-situ measured SST values with an RMS error of 0.84 °C, which indicates the high effectiveness of using Landsat data in monitoring thermal pollution. The results also show that the average area affected by thermal pollution was 34 ha, and the thermal pollution level average was 2.9 °C. Thermal pollution changes in the entire period were analyzed according to three phases: formation and growth (1984–1992), stability (1993–2011), and decline (2012–2023). The annual thematic maps of thermal pollution show that the thermal pollution levels gradually decreased from the Baniyas TPP outlet towards open water and did not exceed a distance of 2 km offshore. The operational capacity of Baniyas TPP exhibited an influence on both thermal pollution levels and areas. The thermal pollution spatial pattern was consistent with the surface currents on the eastern coast of the Mediterranean Sea. The methodology produced in this research could be used effectively to monitor thermal pollution using satellite remote sensing data. The thematic maps developed in this study could be used as a basis for sampling to study the effect of thermal pollution levels on aquatic organisms and then develop environmental norms in Syria about the permissible values of thermal pollution.

The Khangchendzonga Biosphere Reserve (KBR) is located in the Eastern Himalayas and serves as a critical habitat for endemic species of flora and fauna, as well as playing a key role in carbon sequestration. The primary aim of this study was to map large-scale deforestation susceptibility zones in the mountainous region of KBR. The study area was divided into three zones: (a) Transition Zone, (b) Core Zone, and (c) Buffer Zone. This study utilized multiple remote sensing datasets acquired through the Google Earth Engine (GEE) platform, including precipitation, temperature, elevation, forest density, distance from rivers, NDVI, NDSI, distance from settlements, settlement density, distance from roads, and land use and land cover data. Additionally, the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods were employed to map deforestation susceptibility. To validate the proposed deforestation susceptibility, Hansen Global Forest Change (HGFC) data from 2001 to 2022 were used. Moreover, deforestation susceptibility was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metrics. Notably, our findings revealed significant declines in tree cover of 1.60%, 1.27%, and 0.89% in the Transition, Core, and Buffer Zones, respectively, during critical years (2009, 2011, 2019, 2020). These periods witnessed substantial deforestation, indicating a deteriorating condition of the reserve's forest cover. Although there were minor discrepancies in the results of the two methods, both highlighted the particular vulnerability of the transition zones in the eastern and southern regions of KBR. The comprehensive methodology employed in this research establishes an advanced spatial data infrastructure that is indispensable for immediate conservation planning and adaptive management strategies. The insights gleaned from this investigation hold substantial promise for guiding future restoration and conservation efforts aimed at enriching biodiversity and fortifying ecosystem services in this critical area.

The increasing risk of Glacial Lake Outburst Floods (GLOFs) in the Eastern Himalaya is exacerbated by climate change-driven glacial ice mass loss, slowdown, and increasing infrastructure projects in the high-altitude regions. To quantify the current risk of potential future disasters we update the inventory of glacial lakes in Sikkim Himalaya, identify the most potentially dangerous glacial lakes (PDGL) and model their peak discharge in different scenarios. The updated glacial lake inventory includes 232 glacial lakes (of >0.01 km²) covering a cumulative area of 22.23 ± 0.10 km². Our GLOF susceptibility mapping of all moraine-dammed glacial lakes using an Analytic Hierarchy Process (AHP) reveals one lake as very high risk, eight as high risk, 22 as medium risk, 56 as low risk, and 18 as very low risk. Further, we apply dam break flood simulations for the seven most dangerous lakes. Results reveal highest peak discharges of 9504 m³ s⁻¹ and 8421 m³ s⁻¹ in extreme case scenarios from the Khanchung and South Lhonak lakes, respectively. The lowest peak discharge of 622 m³ s⁻¹ is estimated in a normal outburst event for Yongdi lake, with every scenario at least 447 m³ s⁻¹ discharge is reaching to Chungthang town. We find that more than 10,000 people face direct threat of GLOF with potential large-scale infrastructure damage (∼1900 settlement, 5 bridges and 2 hydropower plants). The updated glacial lake dataset, GLOF susceptibility mapping, and modeling results demonstrate the urgent need to install an early warning system and control breaching of highly dangerous lakes.

As global warming intensifies, extreme weather has become one of the major challenges threatening the ecological environment. It remains challenging to detect and evaluate the effects of these extreme weather events on ecosystems quickly and accurately. In July 2023, extreme rainfall caused by Super Typhoon Doksuri hit North China, resulting in massive vegetation mortality in the Baiyangdian Wetland. To quickly assess the ecological loss of Baiyangdian wetland, this study obtained cloud-free remote sensing images before and after the rainfall, quantified the eco-environmental quality by RSEI (Remote Sensing-based Ecological Index) with the comparison of WBEI (Water Benefit-based Ecological Index); and then conducted spatial autocorrelation analysis to reveal the spatial heterogeneity of eco-environmental quality in the study area. The results showed that the WBEI decreased from 0.50 to 0.44 and the RSEI decreased from 0.68 to 0.64. The global Moran's Index varies from 0.681 to 0.801, demonstrating a positive correlation in the spatial distribution characteristics of eco-environmental quality. The deterioration of eco-environmental quality due to extreme rainfall was accurately captured and quantified using two remote sensing indices. Additionally, the cluster map of spatial association indicates that the High-High cluster in the sub-area Zaozhadian disappeared after the extreme rainfall, suggesting that the ecological resilience of the wetland returned from farmland was lower than that of the natural wetland in Baiyangdian. This study offers a new perspective on evaluating the impacts of extreme precipitation. By quantifying the response of eco-environmental quality, it provides scientific guidance for wetland ecological conservation efforts.

Crop growth simulation models are often used to estimate crop yield. For most models, this requires crop, water, and soil management information, though this information is often lacking in many regions of the world. Assimilation of Earth observation (EO) data in crop growth models can generate field-level yield estimates over large areas. The use of EO for assimilation often requires a trade-off between spatial and temporal resolution. Spatiotemporal data fusion can provide higher spatial (≤30m) and temporal resolution data to avoid this trade-off. In this study, we evaluated the timing and frequency of EO data assimilation in the Simple Algorithm for Yield Estimation (SAFY) in a persistently cloudy and fragmented agroecosystem of Ethiopia for 2019 and 2020 growing seasons. We used Landsat and MODIS data fusion to obtain frequent and spatially detailed LAI estimates and assimilated at each main maize growth stage to evaluate the effect of timing and frequency of LAI assimilation. The jointing to grain filling stage observations were more important (RMSE = 117 g/m², rRMSE = 16%) than other growth stages to improve yield estimation. Using LAI estimates at key crop growth stages was more influential than the frequency of LAI estimates. Reasonably accurate yield estimation (rRMSE = 20%) was obtained using the pre-peak growth stage LAI observations, suggesting that the method is suitable for in-season yield forecasting. LAI retrieval errors from EO data, particularly at the early and late growth stages, were the source of yield estimation uncertainty. Therefore, assimilation of other EO-derived biophysical variables and improving LAI retrieval accuracy from EO data could further improve crop growth model performance in smallholder agricultural systems.

Many studies have been done to detect smokes from satellite imagery. However, these prior methods are not still effective in detecting various smokes in complex backgrounds. Smokes present challenges in detection due to variations in density, color, lighting, and backgrounds such as clouds, haze, and/or mist, as well as the contextual nature of thin smoke. This paper addresses these challenges by proposing a new segmentation model called VTrUNet which consists of a virtual band construction module to capture spectral patterns and a transformer boosted UNet to capture long range contextual features. The model takes imagery of six bands: red, green, blue, near infrared, and two shortwave infrared bands as input. To show the advantages of the proposed model, the paper presents extensive results for various possible model architectures improving UNet and draws interesting conclusions including that adding more modules to a model does not always lead to a better performance. The paper also compares the proposed model with very recently proposed and related models for smoke segmentation and shows that the proposed model performs the best and makes significant improvements on prediction performances.