Search Results (126)

Urban vegetation shows significant spatial differences due to the combined effects of natural and human factors, yet fine-scale evolutionary patterns and their cross-scale feedback mechanisms remain limited. This study focuses on the Yangtze River Delta (YRD), the top economic area in China. By integrating data from multiple Landsat sensors, we built a high—resolution framework to track vegetation dynamics from 1990 to 2020. It generates annual 30-m Enhanced Vegetation Index (EVI) data and uses a new Vegetation Green—Brown Balance Index (VBI) to measure changes between greening and browning. We combined Mann-Kendall trend analysis with machine—learning based attribution analysis to look into vegetation changes across different city types and urban—rural gradients. Over 30 years, the YRD’s annual EVI increased by 0.015/10 a, with greening areas 3.07 times larger than browning. Spatially, urban centers show strong greening, while peri—urban areas experience remarkable browning. Vegetation changes showed a city-size effect: larger cities had higher browning proportions but stronger urban cores’ greening trends. Cluster analysis finds four main evolution types, showing imbalances in grey—green infrastructure allocation. Vegetation baseline in 1990 is the main factor driving the long-term trend of vegetation greenness, while socioeconomic and climate drivers have different impacts depending on city size and position on the urban—rural continuum. In areas with low urbanization levels, climate factors matter more than human factors. These multi-scale patterns challenge traditional urban greening ideas, highlighting the need for vegetation governance that adapts to specific spatial conditions and city—unique evolution paths. Full article

The rapid expansion of urban areas necessitates effective monitoring systems for sustainable development planning. Time-series change detection algorithms applied to satellite imagery offer promising solutions, but their comparative effectiveness specifically for urban land cover monitoring remains poorly understood. This study aims to systematically evaluate and optimize three widely used algorithms—LandTrendr, CCDC, and BFAST—selected for their proven capabilities in different land cover change contexts and distinct algorithmic approaches. Using Landsat 5/7/8 (TM/ETM+/OLI) time-series data from 2000 to 2020 and a globally distributed dataset of 200 sample locations spanning six continents, we assess these algorithms across multiple spectral bands and parameter settings for land cover change detection in urban areas. Our analysis reveals that CCDC achieves the highest accuracy (78.14% F1 score) when utilizing complete spectral information (bands B1–B7), outperforming both BFAST (74.32% F1 score with NDVI) and LandTrendr (71.29% F1 score with B1). We demonstrated that, contrary to conventional approaches that prioritize vegetation indices, visible light bands—particularly B1 and B2—achieve higher performance across multiple algorithms. For instance, in LandTrendr, B1 yielded an F1 score of 71.29%, whereas NDVI and EVI produced 56.19% and 53.16%, respectively. Similarly, in CCDC, B2 achieved an F1 score of 72.19%, while NDVI and EVI resulted in 68.57% and 65.33%, respectively. Our findings underscore that parameter optimization and band selection significantly impact detection accuracy, with variations up to 30% observed across different configurations. This comprehensive evaluation provides critical methodological guidance for satellite-based urban expansion monitoring and identifies specific optimization strategies to enhance the application of existing algorithms for urban land cover change detection. Full article

An urban heat island (UHI) refers to urban areas that experience higher temperatures due to heat absorption and retention by impervious surfaces compared to the surrounding rural areas. Urban wetlands are crucial in mitigating the UHI effect and improving climate resilience via their cooling effect. This study examines Colombo, Sri Lanka, the RAMSAR-accredited wetland city in South Asia, to assess the cooling effect of urban wetlands based on 2023 dry season data for effective sustainable management. We used Landsat 8 and 9 data to create Land Use/Cover (LUC), Land Surface Temperature (LST), and surface-reflectance-based maps using the Google Earth Engine (GEE). The Enhanced Vegetation Index (EVI), Modified Normalized Difference Water Index (mNDWI), topographic wetness, elevation, slope, and impervious surface percentage were identified as the influencing variables. The results show that urban wetlands in Colombo face tremendous pressure due to rapid urban expansion. The cooling intensity positively correlates with wetland size. The threshold value of efficiency (TVoE) of urban wetlands in Colombo was 1.42 ha. Larger and more connected wetlands showed higher cooling effects. Vegetation- and water-based wetlands play an important role in <10 km urban areas, while more complex shape configuration wetlands provide better cooling effects in urban and peri-urban areas due to edge effects. Urban planners should prioritize protecting wetland areas and ensuring hydrological connectivity and interconnected wetland clusters to maximize the cooling effect and sustain ecosystem services in rapidly urbanizing coastal cities. Full article

Wetland ecosystems are sensitive to climate variation, yet tracking vegetation type and structure changes through time remains a challenge. This study examines how Landsat-derived vegetation indices (NDVI and EVI) correspond with lidar-derived canopy height model (CHM) changes from 2000 to 2018 across the wetland landscape of the Peace–Athabasca Delta (PAD), Canada. By comparing CHM change and NDVI and EVI trends across woody and herbaceous land covers, this study fills a gap in understanding long-term vegetation responses in northern wetlands. Findings show that ~35% of the study area experienced canopy growth, while 2% saw a reduction in height. CHM change revealed 11% ecotonal expansion, where shrub and treed swamps encroached on meadow and marsh areas. NDVI and EVI correlated significantly (p < 0.001) with CHM, particularly in shrub swamps (r² = 0.40, 0.35) and upland forests (NDVI r² = 0.37). However, EVI trends aligned more strongly with canopy expansion, while NDVI captured mature tree height growth and wetland drying, indicated by rising land surface temperatures (LST). These results highlight the contrasting responses of NDVI and EVI—NDVI being more sensitive to moisture-related changes such as wetland drying, and EVI aligning more closely with canopy structural changes—emphasizing the value of combining lidar and satellite indices to monitor wetland ecosystems in a warming climate. Full article

This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N₂O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. These parameters include enhanced vegetation index (EVI), land surface temperature (LST), normalized difference vegetation index (NDVI), albedo, elevation, humidity, population density, precipitation, soil moisture, and wind speed. The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. The study highlights the effectiveness of machine learning in quantifying emission dynamics and underscores the necessity of sustainable land management strategies to mitigate greenhouse gas emissions. By integrating remote sensing and data-driven methodologies, this research contributes to climate change mitigation policies and precision agriculture strategies aimed at balancing food security and environmental sustainability. Full article

Understanding the impacts of land use and land cover changes on ecosystem service values (ESVs) is crucial for effective ecosystem management; however, the intricate relationship between these factors in coal mining regions remains underexplored. In particular, the influence of coal mining activities on these dynamics is insufficiently understood, leaving a gap in the literature that hinders the development of robust management strategies. To address this gap, we investigated the interplay between land use change and the ESV at the interface of Yang Coal Mine No. 2 and the Shanxi Yalinji Guanshan Provincial Nature Reserve in Yangquan City, Shanxi Province. Using Landsat 8 remote sensing data from 2013 to 2021, our approach incorporated analyses using the Google Earth Engine (GEE) platform. We employed a random forest algorithm to classify land use patterns and calculated key indices—including the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), enhanced vegetation index (EVI), and bare soil index (BSI)—which were combined with topographic features. Land use change dynamics were quantified via a transfer matrix, while changes in the ESV were evaluated using the ecosystem sensitivity index and ecological contribution rate. Our results revealed notable fluctuations: forestland increased from 2013 to 2018 before declining sharply from 2019 to 2021; grassland displayed similar variability; and constructed land experienced a continual expansion. Correspondingly, the overall ESV increased by 28.6% from 2013 to 2019, followed by a 19.5% decline in 2020 and 2021, with forest and grassland’s ESVs exhibiting similar trends. These findings demonstrate that land use changes, particularly those that are driven by human activities such as coal mining, have a significant impact on ecosystem service values in mining regions. By unraveling the nuanced relationship between land use dynamics and ESVs, our study not only fills the gap in the literature but also provides valuable insights for developing more effective ecosystem management strategies, ultimately advancing our understanding of ecosystem dynamics in human-impacted landscapes. Full article

Forest fires are an important disturbance that affects ecosystem stability and pose a serious threat to the ecosystem. However, the recovery process of forest ecological quality (EQ) after a fire in plateau mountain areas is not well understood. This study utilizes the Google Earth Engine (GEE) and Landsat data to generate difference indices, including NDVI, NBR, EVI, NDMI, NDWI, SAVI, and BSI. After segmentation using the Simple Non-Iterative Clustering (SNIC) method, the data were input into a random forest (RF) model to accurately extract the burned area. A 2005–2020 remote sensing ecological index (RSEI) time series was constructed, and the recovery of post-fire forest EQ was evaluated through Theil–Sen slope estimation, Mann–Kendall (MK) trend test, stability analysis, and integration with topographic information systems. The study shows that (1) from 2006 to 2020, the post-fire forest EQ improved year by year, with an average annual increase rate of 0.014/a. The recovery process exhibited an overall trend of “decline initially-fluctuating increase-stabilization”, indicating that RSEI can be used to evaluate the post-fire forest EQ in complex plateau mountainous regions. (2) Between 2006 and 2020, the EQ of forests exhibited a significant increasing trend spatially, with 84.32% of the areas showing notable growth in RSEI, while 1.80% of the regions experienced a declining trend. (3) The coefficient of variation (CV) of RSEI in the study area was 0.16 during the period 2006–2020, indicating good overall stability in the process of post-fire forest EQ recovery. (4) Fire has a significant impact on the EQ of forests in low-altitude areas, steep slopes, and sun-facing slopes, and recovery is slow. This study offers scientific evidence for monitoring and assessing the recovery of post-fire forest EQ in plateau mountainous regions and can also inform ecological restoration and management efforts in similar areas. Full article

Monitoring forest health has become essential due to increasing pressures caused by climate change and dust events, particularly in semi-arid regions. This study investigates the impact of dust events on forest vegetation in Paphos forest in Cyprus, which is a semi-arid area prone to frequent dust storms. Using multispectral and radar satellite data from Sentinel-1 and Landsat series, vegetation responses to eight documented dust events between 2015 and 2019 were analysed, employing BFAST (Breaks For Additive Season and Trend) algorithms to detect abrupt changes in vegetation indices and radar backscatter. The outcomes showed that radar data were particularly effective in identifying only the most significant dust events (PM10 > 100 μg/m³, PM2.5 > 30 μg/m³), indicating that SAR (Synthetic Aperture Radar) is more responsive to pronounced dust deposition, where backscatter changes reflect more substantial vegetation stress. Conversely, optical data were sensitive to a wider range of events, capturing responses even at lower dust concentrations (PM10 > 50 μg/m³, PM2.5 > 20 μg/m³) and detecting minor vegetation stress through indices like SAVI, EVI, and AVI. The analysis highlighted that successful detection relies on multiple factors beyond sensor type, such as rainfall timing and imagery availability close to the dust events. This study highlights the importance of an integrated remote sensing approach for effective forest health monitoring in regions prone to dust events. Full article

Remote sensing (RS) is a compulsory component in studying and monitoring ecosystems suffering from the disruption of natural balance, productivity, and degradation. The current study attempted to assess the feasibility of multisource RS for assessing and monitoring mountainous natural grasslands in Armenia. Different spatial resolution RS data (Landsat 8, Sentinel-2, Planet Scope, and multispectral UAV) were used to obtain various vegetation spectral indices: NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI, and the relationships among the indices were assessed via the Spearman correlation method, which showed a significant positive correlation for all cases (p < 0.01). A comparison of all indices showed a significant high correlation between UAV and the Planet Scope imagery. The comparisons of UAV with Sentinel and Landsat data show moderate and low significant correlation (p < 0.01), correspondingly. Also, trend analysis was performed to explore the spatial–temporal changes of these indices using Mann–Kendall statistical tests (MK, MKKH, MKKY, PW, TFPW), which indicated no significant trend. However, Sen’s slope as a second estimator showed a decreasing trend. Generally, it could be proved that, as opensource data, Sentinel-2 seemed to have better alignment, making it a reliable tool for the accurate monitoring of the ecological state of small mountainous grasslands. Full article

Co-management is a promising forest governance strategy that integrates local communities’ traditional rights and forest dependencies while aiming to improve forest cover and ecosystem health. Bangladesh, facing high deforestation rates and limited per capita forest area, has implemented co-management initiatives since 2003 to restore forest cover and support the livelihoods of forest-dependent communities. While the socio-economic impacts of co-management are well studied, its effects on forest cover remain underexplored. This study addresses that gap by using three common spectral vegetation indices (NDVI, EVI, and MSAVI), calculated from Landsat 7 data, to analyze forest cover changes in five major protected areas under co-management. The results indicated that dense forest cover (41–71%) was initially prevalent in these areas, but a significant decline occurred between 2004 and 2015, with slope values ranging from −3.7 to −0.96. In contrast, the non-co-managed control site exhibited a much smaller decline (slope: −0.48 to −0.62) across all indices. Notable increases in agricultural land and forest–agriculture mosaics were also observed in the protected areas under co-management. Global Forest Watch data further confirmed substantial forest cover loss, particularly in CWS (158.77 ha) and SNP (0.49 ha). These findings highlight the need to reassess co-management strategies to address ongoing forest degradation. Full article

Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management. Full article

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 10⁴ km²), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km²) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes. Full article

The persistent increase in forest pest outbreaks requires timely detection methods to monitor the disaster precisely. However, early detection is challenging due to insufficient temporal observation and subtle tree changes. This article proposed a novel framework that collaborates multi-source remote sensing data and uses a change detection algorithm to archive early detection of infestation caused by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) attacks. First, all available Sentinel-2 images with less than 20% cloud cover were utilized. During periods with long intervals (>16 days) between Sentinel-2 images, Landsat-8 images with less than 20% cloud cover were downscaled to a spatial resolution of 10 m using a deep learning algorithm to meet the requirement for a high temporal frequency of clear observations. Second, the spectral index differences between healthy and infested trees were examined to address the challenge of detecting subtle changes in pest attacks. The Enhanced Vegetation Index (EVI) was selected for early defoliation detection. On this basis, the EWMACD (Exponentially Weighted Moving Average Change Detection) algorithm, which is sensitive to subtle changes, was enhanced to improve the capability of detecting early D. tabulaeformis attacks. The assessment showed that the overall accuracy of the change detection (F1 score) reached 0.86 during the early stage and 0.88 during the late stage. The temporal accuracy (Precision) was 84.1% during the early stage. The accuracy significantly improved compared to using a single remote sensing data source. This study presents a new framework capable of monitoring early forest defoliation caused by D. tabulaeformis attacks and offering opportunities for predicting future outbreaks and implementing preventive measures. Full article

Natural resource managers may utilize remotely sensed data to monitor vegetation within their decision-making frameworks for improving habitats. Under binational agreements between the United States and Mexico, seven reaches were targeted for riparian habitat enhancement. Monitoring was carried out using Landsat 8 16-day intervals of the two-band enhanced vegetation index 2 (EVI2) for greenness and actual evapotranspiration (ETa). In-channel water was delivered in 2021 and 2022 at four places in Reach 4. Three reaches (Reaches 4, 5 and 7) showed no discernable difference in EVI2 from reaches that did not receive in-channel water (Reaches 1, 2, 3 and 6). EVI2 in 2021 was higher than 2020 in all reaches except Reach 3, and EVI2 in 2022 was lower than 2021 in all reaches except Reach 7. ET(EVI2) was higher in 2020 than in 2021 and 2022 in all seven reaches; it was highest in Reach 4 (containing restoration sites) in all years. Excluding restoration sites, compared with 2020, unrestored reaches showed that EVI2 minimally increased in 2021 and 2022, while ET(EVI2) minimally decreased despite added water in 2021–2022. Difference maps comparing 2020 (no-flow year) to 2021 and 2022 (in-channel flows) reveal areas in Reaches 5 and 7 where the in-channel flows increased greenness and ET(EVI2). Full article

Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is to find an appropriate VI to analyse the spatio-temporal evolution of olive water stress using LST images and VIs derived from Landsat 5 and 8 satellites in the semi-arid region of southern Peru. For this purpose, VIs (Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2) and Soil Adjusted Vegetation Index (SAVI)) and LST were calculated. The information was processed in Google Earth Engine (GEE) for the period 1985 to 2024, with an interval of every five years for the summer season. The triangle method was applied based on the LST-VIs scatterplot analysis, a tool that establishes wet and dry boundary conditions for the Temperature Vegetation Dryness Index (TVDI). The results indicated a better appreciation of olive orchard water stress over time, with an average of 39% drought (TVDI_NDVI and TVDI_SAVI), 24% severe drought (TVDI_NDVI) and 25% (TVDI_SAVI) of the total area, compared to TVDI_EVI2, which showed 37% drought and 16% severe drought. It is concluded that TVDI_NDVI and TVDI_SAVI provide a better visualisation of the water stress map of the olive crop and offer a range of options to address current and future problems in water resource management in the olive sector in semi-arid areas of southern Peru. Full article