Search Results (188)

Coastal flooding driven by sea-level rise and land subsidence poses severe risks to low-lying communities. This study evaluates the causes and impacts of coastal sinking in Sayung, Demak, Central Java, using multi-temporal Landsat imagery (1977, 2024), tidal gauge data, and GPS measurements. A set of spectral indices—Normalized Difference Vegetation Index (NDVI), Weighted Modified Normalized Difference Water Index (WMNDWI), Land Surface Water Index (LSWI), and Normalized Difference Built-up Index (NDBI)—were calculated and integrated as input features for a Random Forest machine learning model to detect and classify environmental changes. Results indicated an average land subsidence rate of approximately 6 cm/year ± 0.8 cm/year, validated against InSAR-based measurements, and a classification accuracy of 91% (RMSE of 0.8 cm/year). A substantial decline in vegetation indices was observed, reflecting the conversion of agricultural land into built-up areas and water bodies. Extensive flooding and shoreline retreat were documented, with high-risk zones concentrated along densely developed coastlines. These findings highlight the urgent need for integrated management strategies, including stricter groundwater regulation, continuous remote-sensing-based monitoring, and large-scale mangrove restoration, to safeguard ecological functions and enhance the socio-economic resilience of coastal communities in the face of accelerating climate change impacts. Full article

As an important part of the urban ecological environment, urban green space plays a crucial and irreplaceable role in improving air quality, promoting sustainable development, and enhancing residents’ quality of life. This study takes Beijing’s urban green space as the research object. Based on Landsat series satellite remote sensing images, the land use distribution of Beijing is obtained through supervised classification. Combined with data such as PM_2.5 concentration and wind speed, the dry deposition efficiency of PM_2.5 is quantitatively analyzed. The results show that: (1) Beijing’s urban green space has significant advantages in PM_2.5 dry deposition. In terms of dry deposition flux, the order of annual average deposition of different land types is: forest land > farm land > grassland > impervious surface > water body = unutilized land. Among them, forest land has the best dry deposition effect, with an annual average dry deposition of 1.13 g/m², which is 188.41 times that of impervious surface; cultivated land and grassland are 0.22 g/m² and 0.19 g/m² respectively, which are 37.13 times and 32.34 times that of impervious surface. (2) From 2000 to 2020, the PM_2.5 removal rate of green space continued to rise, but the reduction amount showed a trend of first increasing and then decreasing. There are significant seasonal differences. The reduction amount is the highest in autumn (reaching 449.90 tons in October), followed by summer, spring, and winter (the lowest in August, at 190.27 tons). (3) In terms of spatial distribution, the high-value areas of dry deposition are concentrated in the suburbs, showing a “southwest-northeast” axial distribution, while the low-value areas are mainly located in the outer suburbs, reflecting the imbalance of green space layout and the regional differences in PM_2.5 reduction. Combined with the current situation of green space in Beijing, the study puts forward targeted optimization suggestions, providing theoretical support and scientific basis for the construction of Beijing as a “garden city”. Full article

Large-scale vegetation loss induced by surface coal mining constitutes a critical driver of regional ecological degradation. However, the applicability of existing change detection methodologies based on remote sensing within complex mining areas under diverse climatic conditions remains systematically unverified. To address this gap and reveal nationwide disturbance patterns, this study systematically evaluates the performance of two algorithms—Continuous Change Detection and Classification (CCDC) and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr)—in identifying vegetation loss across three major climatic zones of China (the humid, semi-humid, and semi-arid zones). Based on the optimal algorithm, the vegetation loss year and loss magnitude across all of China’s surface coal mining areas from 1990 to 2020 were accurately identified, enabling the reconstruction of the comprehensive, nationwide spatio-temporal pattern of mining-induced vegetation loss over the past 30 years. The results show that: (1) CCDC demonstrated superior stability and significantly higher accuracy (OA = 0.82) than LandTrendr (OA = 0.31) in identifying loss years across all zones. (2) The cumulative vegetation loss area reached 1429.68 km², with semi-arid zones accounting for 86.76%. Temporal analysis revealed a continuous expansion of the loss area from 2003 to 2013, followed by a distinct inflection point and decline during 2014–2016 attributable to policy-driven regulations. (3) Further analysis revealed significant variations in the average magnitude of loss across different climatic zones, namely semi-arid (0.11), semi-humid (0.21), and humid (0.25). These findings underscore the imperative for region-specific restoration strategies to ensure effective conservation outcomes. This study provides a systematic quantification and analysis of long-term, nationwide evolution patterns and regional differentiation characteristics of vegetation loss induced by surface coal mining in China, offering critical support for sustainable development decision-making in balancing energy development and ecological conservation. Full article

In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article

Using Landsat series imagery and the deep learning model CITNet, this study conducted high-accuracy classification and spatiotemporal change analysis of wetlands on Chongming Island from 2000–2020 and explored the driving mechanisms by integrating climatic and anthropogenic factors. The results demonstrate that the total wetland area decreased by approximately 125.5 km² over the two decades. Among natural wetlands, tidal mudflats and shallow seawater zones continuously shrank, while herbaceous marshes exhibited a “decline recovery” trajectory. Artificial wetlands expanded before 2005 but contracted significantly thereafter, mainly due to aquaculture pond reduction. Wetland transformation was dominated by wetland-to-non-wetland conversions, peaking during 2005–2010. Driving factor analysis revealed a “human pressure dominated, climate modulated” pattern: nighttime light index (NTL) and GDP demonstrated strong negative correlations with wetland extent, while minimum temperature and the Palmer Drought Severity Index (PDSI) promoted herbaceous marsh expansion and accelerated artificial wetland contraction, respectively. The findings indicate that wetland changes on Chongming Island result from the combined effects of policy, economic growth, and ecological processes. Sustainable management should focus on restricting urban expansion in ecologically sensitive zones, optimizing water resource allocation under drought conditions, and incorporating climate adaptation and invasive species control into restoration programs to maintain both the extent and ecological quality of wetlands. Full article

Coffee demand continues to rise, while producing countries face increasing challenges and yield losses due to climate change. In response, farmers are adopting agricultural practices capable of boosting productivity. However, these practices increase intercrop variability, making coffee mapping more challenging. In this study, a novel approach is proposed to identify coffee cultivation considering four phenological stages: planting (PL), producing (PR), skeleton pruning (SK), and renovation with stumping (ST). A hierarchical classification framework was designed to isolate coffee pixels and identify their respective stages in one of Brazil’s most important coffee-producing regions. A dense time series of multispectral bands, spectral indices, and texture metrics derived from Harmonized Landsat Sentinel-2 (HLS) imagery, with an average revisit time of ~3 days, was employed. This data was combined with an ensemble learning approach based on decision-tree algorithms, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The results achieved unprecedented sensitivity and specificity for coffee plantation detection with RF, consistently exceeding 95%. The classification of coffee phenological stages showed balanced accuracies of 77% (ST) and from 93% to 95% for the other classes. These findings are promising and provide a scalable framework to monitor climate-resilient coffee management practices. Full article

Mangroves in Sri Lanka provide critical ecosystem services, yet they have undergone significant changes due to anthropogenic and natural drivers. This study presents the first national-scale assessment of mangrove dynamics in Sri Lanka using remote sensing techniques. A total of 4670 Landsat images from Landsat 5, 7, 8, and 9 were selected to detect mangrove distribution, changes in extent, and structure and stability patterns from 1987 to 2022. A Random Forest classification model was applied to elucidate the spatial changes in mangrove distribution in Sri Lanka. Using national-scale data enhanced mapping accuracy by incorporating region-specific spectral and ecological characteristics. The average overall accuracy of the maps was over 96.29%. The total extent of mangroves in 2022 was 16,615 ha, representing 0.25% of the total land of Sri Lanka. The results further indicate that, at the national scale, mangrove extent increased from 1989 to 2022, with a net gain of 1988 ha (13.6%), suggesting a sustained and continuous recovery of mangroves. Provincial-wise assessments reveal that the Eastern and Northern Provinces showed the largest mangrove extents in Sri Lanka. In contrast, the Colombo, Gampaha, and Kalutara districts in the Western Province showed persistent declines. The top mangrove spatial structure and stability districts were Jaffna, Trincomalee, and Gampaha, while the most degraded mangrove districts were Batticaloa, Colombo, and Kalutara. This study offers critical insights into sustainable mangrove management, policy implementation, and climate resilience strategies in Sri Lanka. Full article

Avocado orchards are among the most profitable and fastest-growing commodity crops in Mexico, especially in the area known as the “Avocado Belt”. Several efforts have been made to monitor their expansion; however, there is currently no method that can be easily updated to track this expansion. The main objective of this study was to monitor the expansion of avocado orchards from 1993 to 2024, using the Continuous Change Detection and Classification (CCDC) algorithm and Landsat 5, 7, 8, and 9 imagery. Presence/absence maps of avocado orchards corresponding to 1 January of each year were used to perform a trajectory analysis, identifying eight possible change trajectories. Finally, maps from 2020 to 2023 were verified using reference data and very-high-resolution images. The maps showed a level of agreement = 0.97, while the intersection over union for the avocado orchard class was 0.62. The main results indicate that the area occupied by avocado orchards more than tripled from 1993 to 2024, from 64,304.28 ha to 200,938.32 ha, with the highest expansion occurring between 2014 and 2024. The trajectory analysis confirmed that land conversion to avocado orchards is generally permanent and happens only once (i.e., gain without alternation). The method proved to be a robust approach for monitoring avocado orchard expansion and could be an attractive alternative for regularly updating this information. Full article

Cotton is a globally strategic crop that plays a crucial role in sustaining national economies and livelihoods. To address the challenges of accurate cotton field extraction in the complex planting environments of Xinjiang’s Alaer reclamation area, a cotton field identification model was developed that integrates multi-source satellite remote sensing data with machine learning methods. Using imagery from Sentinel-2, GF-1, and Landsat 8, we performed feature fusion using principal component, Gram–Schmidt (GS), and neural network techniques. Analyses of spectral, vegetation, and texture features revealed that the GS-fused blue bands of Sentinel-2 and Landsat 8 exhibited optimal performance, with a mean value of 16,725, a standard deviation of 2290, and an information entropy of 8.55. These metrics improved by 10,529, 168, and 0.28, respectively, compared with the original Landsat 8 data. In comparative classification experiments, the endmember-based random forest classifier (RFC) achieved the best traditional classification performance, with a kappa value of 0.963 and an overall accuracy (OA) of 97.22% based on 250 samples, resulting in a cotton-field extraction error of 38.58 km². By enhancing the deep learning model, we proposed a U-Net architecture that incorporated a Convolutional Block Attention Module and Atrous Spatial Pyramid Pooling. Using the GS-fused blue band data, the model achieved significantly improved accuracy, with a kappa coefficient of 0.988 and an OA of 98.56%. This advancement reduced the area estimation error to 25.42 km², representing a 34.1% decrease compared with that of the RFC. Based on the optimal model, we constructed a digital map of continuous cotton cropping from 2021 to 2023, which revealed a consistent decline in cotton acreage within the reclaimed areas. This finding underscores the effectiveness of crop rotation policies in mitigating the adverse effects of large-scale monoculture practices. This study confirms that the synergistic integration of multi-source satellite feature fusion and deep learning significantly improves crop identification accuracy, providing reliable technical support for agricultural policy formulation and sustainable farmland management. Full article

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

Angelica sinensis, a highly valued Chinese herb renowned for its medicinal and nutritional properties, occupies a distinctive position in montane agriculture. The remote sensing monitoring of grain crops and their driving factors has been extensively studied, yet research on medicinal cash crops, particularly Angelica sinensis, remains limited. This study employed Landsat imagery and a two-step supervised classification method to map Angelica sinensis cultivation areas in southern Gansu Province while also assessing and projecting climate change impacts on its spatial distribution and yield based on the MaxEnt model and CMIP6 models. The results revealed a pronounced upward altitudinal shift in Angelica sinensis cultivation between 1990 and 2020, with the proportion of cultivation areas above 2400 m increasing from 28.75% to 67.80%. Climate factors explained 59.07% of the spatial distribution of Angelica sinensis, with precipitation, temperature, and altitude identified as the key environmental factors influencing its spatial distribution, yield, and growth. Projections for 2020 to 2060 indicate that Angelica sinensis cultivation areas will continue to shift to higher altitudes, accompanied by overall declines in both suitable area and yield. Under the SSP5-8.5 scenario, nearly all suitable areas are expected to be confined to altitudes above 2400 m by 2060, with 41.46% occurring above 2800 m. By 2060, the yield is expected to decrease to 361–421 kg/mu (down 20–31% from 2020) while the suitable area is projected to shrink to 0.98–1.80 million mu (40–60% smaller than 2040) under different scenarios. This study provides new insights into the protection and sustainable management of Angelica sinensis under changing climatic conditions, offering a scientific basis for the sustainable utilization of this valuable medicinal plant. Full article

This study focuses on assessing the physical growth of cities and the land-cover changes resulting from it, which play a crucial role in understanding the environmental impacts and managing phenomena such as the Daytime Urban Surface Heat Island Intensity (DSUHII). Predicting the trends of these changes for the future provides valuable insights for urban planning and mitigating thermal effects in arid environments. This research aims to evaluate the spatial and temporal changes in the intensity of urban surface heat islands in cities under different climatic conditions, resulting from land-cover changes in the past, and to predict future trends. For this purpose, Landsat satellite data products, including Surface Reflectance with a 30-m resolution and Land Surface Temperature (LST) originally at a 100 (120)-meter resolution for Landsat 8 (Landsat 5) (resampled to 30 m for compatibility), along with a database of underlying criteria affecting urban growth, were used to analyze land-cover and LST changes. The land-cover classification was carried out using the Support Vector Machine (SVM) algorithm, and its accuracy was assessed. Spatial and temporal changes in LST and land-cover classes were quantified using cross-tabulation models and subtraction operators. Subsequently, the impact of land-cover changes on LST in different climates was analyzed, and the trends of land-cover and DUSHII changes were simulated for the future using the CA–Markov model. The results showed that in the humid climate (Babol and Rasht), built-up areas increased by over 100% from 1990 to 2023 and are projected to grow further by 2055, while green spaces significantly decreased. In the cold–dry climate (Mashhad), urban development increased dramatically, and green spaces nearly halved. In the hot–dry climate (Yazd and Kerman), built-up areas tripled, and the reduction of green spaces will continue. Additionally, in cities with hot and dry climates, a significant area of barren land was converted into built-up areas, and this trend is predicted to continue in the future. DSUHII in Babol increased from 2.5 °C in 1990 to 5.4 °C in 2023 and is projected to rise to 7.8 °C by 2055. In Rasht, this value increased from 2.9 °C to 5.5 °C, and is expected to reach 7.6 °C. In Mashhad, the DSUHII was negative, decreasing from −1.1 °C in 1990 to −1.5 °C in 2023, and is projected to decline to −1.9 °C by 2055. In Yazd, DSUHII also remained negative, decreasing from −2.5 °C in 1990 to −3.3 °C in 2023, with an expected drop to −6.4 °C by 2055. Similarly, in Kerman, the intensity of DSUHII decreased from −2.8 °C to −5.1 °C, and it is expected to reach −7.1 °C by 2055. Overall, the conclusions highlight that in humid climates, DSUHII has significantly increased, while green spaces have decreased. In moderate, cold, and dry climates, a gradual reduction in DSUHII is observed. In the hot–dry climate, the most substantial decrease in DSUHII is evident, indicating the varying impacts of land-cover changes on DSUHII across these regions. Full article

Water is an essential necessity for maintaining the life cycle on Earth. These resources are continuously changing because of human activities and climate-related factors. Hence, adherence to effective water management and consistent water policy is vital for the optimal utilization of water resources. Water resource monitoring can be achieved by precisely delineating the borders of water surfaces and quantifying the variations in their areas. Since Lake Van is the largest lake in Turkey, the largest alkaline lake in the world, and the fourth largest terminal lake in the world, it is very important to determine the changes in water surface boundaries and water surface areas. In this context, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automatic Water Extraction Index (AWEI) were calculated from Landsat-8 satellite images of 2014, 2017, 2020 and 2023 in June, July, and August using the Google Earth Engine (GEE) platform. Water pixels were separated from other details using the Canny edge detection algorithm based on the calculated indices. The Otsu thresholding method was employed to determine water surfaces, as it is the most favored technique for calculating NDWI, AWEI, and MNDWI indices from Landsat 8 images. Utilizing the Canny edge detection algorithm and Otsu threshold detection approaches yielded favorable outcomes in accurately identifying water surfaces. The AWEI demonstrated superior performance compared to the NDWI and MNDWI across all three measures. When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector Machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. In 2017, the highest producer accuracy, user accuracy, overall accuracy, kappa, and f score evaluation criteria were all 100% with the SVM algorithm and AWEI. In 2020, the SVM algorithm and NDWI produced the highest evaluation criteria values of 100% for producer accuracy, user accuracy, overall accuracy, kappa, and f score. In 2023, using the SVM and CART algorithms as well as the AWEI, the highest evaluation criteria values for producer accuracy, user accuracy, overall accuracy, kappa, and f score were 100%. This study is a case study demonstrating the successful application of machine learning with Canny edge detection and the Otsu water surfaces thresholding method. Full article

Lomami National Park, located in the Democratic Republic of the Congo (DR Congo), is renowned for the integrity of its forest ecosystems, safeguarded by the absence of agricultural activities and limited road access. However, these ecosystems remain under-researched, particularly in terms of forest cover dynamics. This research gap poses a significant challenge to establishing rigorous monitoring systems, which are essential for ensuring the long-term preservation of these valuable ecosystems. This study utilized Google Earth Engine to preprocess Landsat images from 2008, 2016, and 2024, employing techniques such as atmospheric correction and cloud masking. Random Forest classification was applied to analyze land cover changes, using training datasets curated through ground-truthing and region-of-interest selection. The classification accuracy was evaluated using metrics such as overall accuracy, producer’s accuracy, and user’s accuracy. To assess landscape configuration, metrics such as class area, patch number, largest patch index, disturbance index, aggregation index, and edge density were calculated, distinguishing between the park’s core and peripheral zones. Spatial transformation processes were analyzed using a decision tree approach. The results revealed a striking contrast in forest cover stability between Lomami National Park and its surrounding periphery. Within the park, forest cover has been preserved and even showed a modest increase, rising from 92.60% in 2008 to 92.75% in 2024. In contrast, the peripheral zone experienced a significant decline in forest cover, decreasing from 79.32% to 70.48% during the same period. This stability within the park extends beyond maintaining forested areas; it includes preserving and enhancing the spatial structure of forest ecosystems. For example, edge density, a key indicator of forest edge compactness, remained stable in the park, fluctuating between 8 m/ha and 9 m/ha. Conversely, edge density in the peripheral zone exceeded 35 m/ha, indicating that forest edges within the park are considerably more cohesive and intact than those in the surrounding areas. The spatial transformation processes also underscored these contrasting dynamics. In the park, the primary process was the aggregation of primary forest patches, reflecting a trend toward continuous and connected forest landscapes. By contrast, the peripheral zone exhibited dissection, indicating fragmentation and the breakdown of forest patches. These findings highlight the park’s critical role in maintaining both the extent and structural integrity of forest ecosystems, setting it apart from the more degraded periphery. They underscore the resilience of forest ecosystems in the face of limited anthropogenic pressures and the crucial importance of effective land management and rigorous conservation strategies in addressing the challenges posed by urbanization and rural expansion. Additionally, the results emphasize that well-adapted conservation measures, combined with specific demographic and socio-economic conditions, can play a pivotal role in achieving long-term forest preservation and ecological stability. Full article

Impervious areas are one of the important indicators for evaluating the urbanization process, while surface temperature is one of the reference factors for evaluating the urban environment. In order to investigate whether the spatial distribution of an impervious surface has any influence on urban surface temperature, Xuzhou City was selected as the study area, and the impervious surface information was extracted based on the maximum likelihood classification method for Xuzhou City for the period of 2013–2022, and surface temperature inversion was performed using Landsat 8 remote sensing imagery and nighttime lighting data. In order to reduce the confusion between bare soil and impervious surfaces, the study area was divided into built-up and non-built-up areas for the selection of impervious and pervious surface samples using nighttime lighting data, and, finally, the maximum likelihood classification method was used to realize the extraction of impervious surfaces. The experimental results show that, by extracting the impervious surface of Xuzhou City, the impervious surface of Xuzhou City continued to increase from 2013 to 2022, in which the growth rate was faster in 2014–2016 and 2019–2021, and slower in 2017–2018 and 2021–2022, after performing surface temperature inversion as well as temperature grading. The results of impervious surface extraction and surface temperature inversion were subjected to overlay analysis and linear regression analysis. It was found that most of the impervious surface area is in high-temperature areas, and the density of the impervious surface is proportional to the surface temperature in the impervious surface and its surrounding area. Therefore, it can be concluded that the expansion of impervious surfaces is one of the reasons for the increase in urban surface temperature. Full article