Search Results (3,141)

Under the global goals of carbon peaking and carbon neutrality, China’s northern agro-pastoral ecotone—an ecologically fragile transition zone with drastic land use/cover change (LUCC)—is characterized by a lack of in-depth understanding of its “land use conflict–carbon sink response” mechanism, which is essential for regional land optimization and carbon neutrality. This study quantified the spatiotemporal dynamics of carbon storage in the zone from 2000 to 2020 using the InVEST model and identified key driving factors by combining the XGBoost model (R² = 0.73–0.88) with the SHAP framework. The results showed that regional total carbon storage increased by 30.11 × 10⁶ tons (a net growth of 0.57%), mainly driven by forest carbon sinks (+65.74 × 10⁶ tons, accounting for 218.3% of the total increase), while cropland and grassland underwent continuous carbon loss (−53.87 × 10⁶ tons and −35.80 × 10⁶ tons, respectively). Spatially, this presents a pattern of “high-value agglomeration in the central–southern region and low-value fragmentation at urban–rural edges”. The Normalized Difference Vegetation Index (NDVI) was the primary driver (average SHAP value: 426.15–718.91), with its interacting temperature factor evolving from air temperature (2000) to nighttime surface temperature (2020). This study reveals the coupling mechanism of “vegetation restoration–microenvironment regulation–carbon sink gain” driven by the Grain for Green Program, providing empirical support for land use optimization and carbon neutrality in agro-pastoral areas. Full article

Planned capitals across the Global South frequently experience unplanned land use transitions that contradict their founding visions. Despite six decades of planning and academic inquiry, Islamabad’s research remains fragmented. Environmental studies have documented land use and land cover changes through remote sensing, while governance-oriented analyses have highlighted institutional weaknesses and policy failures. However, these domains rarely intersect, and few studies systematically link spatial transformations with the underlying governance structures and political–economic processes that drive them. Consequently, the existing literature provides valuable but partial explanations for why Islamabad’s planned order unraveled. This study examines Islamabad, conceived in 1960 as a model of order and green balance, where the built-up area expanded by 377 km² (from 88 to 465 km²; +426%) and forest cover declined by 83 km² (−40%) between 1979 and 2019. Using a PRISMA-guided systematic review integrating spatial, governance, and policy data, we synthesized 39 peer-reviewed and gray literature sources to explain why Islamabad’s planned order unraveled. The findings reveal that governance fragmentation between the Capital Development Authority (CDA) and Metropolitan Corporation Islamabad (MCI), combined with elite capture and weak enforcement of the 2020–2040 Master Plan, has produced enduring contradictions between policy intent and urban reality. These conditions mirror those of other planned capitals, such as Brasília and Abuja. Grounded in Pakistan’s institutional context, the study proposes four actionable reforms: (1) regularization frameworks for informal settlements, (2) cross-agency spatial and fiscal coordination, (3) ecological thresholds within zoning by-laws, and (4) participatory master-plan reviews. Islamabad’s experience illustrates how planned capitals can evolve toward inclusive and ecologically resilient futures through governance reform and adaptive planning. Full article

In order to address the social, economic, and environmental challenges arising from urban development, some urban revitalization plans have been proposed. With the implementation of these plans, the spatial pattern of the region has also undergone corresponding changes. Some of the revitalization projects have driven economic growth while accompanied by ecological degradation, while others have achieved coordinated development and protection. This study selected eight urban revitalization cases, based on remote sensing (RS) and geographic information system (GIS), and used the Random Forest (RF) machine learning method to dynamically monitor the spatial changes in the region before and after revitalization through Land Use and Land Cover (LULC) analysis. The research results show that among the eight cases, only the revitalization cases located in Beijing and Swarzędz reflected an increase in water and vegetation areas, while the built-up area decreased. The other six cases located in Nanjing, Kraków, Wągrowiec, Swarzędz, Parczew, and Mosina all reflect the result of built-up areas encroaching water and vegetation areas. Full article

The Tibetan Plateau is a globally critical climate-sensitive and ecologically fragile region. Vegetation phenology serves as a key indicator of ecosystem responses to climate change and simultaneously influences regional carbon cycling, water regulation, and ecological security. However, systematic quantitative assessments of phenological responses under the combined effects of multiple climate factors remain limited. This study integrates multi-source remote sensing data (MODIS MCD12Q2) and ERA5-Land meteorological data from 2001 to 2023, leveraging the Google Earth Engine (GEE) cloud platform to extract key phenological metrics, including the start (SOS) and end (EOS) of the growing season, and growing season length (GSL). Sen’s slope estimation, Mann–Kendall trend tests, and partial correlation analyses were applied to quantify the independent effects and spatial heterogeneity of temperature, precipitation, solar radiation, and evapotranspiration (ET) on GSL. Results indicate that: (1) GSL on the Tibetan Plateau has significantly increased, averaging 0.24 days per year (Sen’s slope +0.183 days/yr, Z = 3.21, p < 0.001; linear regression +0.253 days/yr, decadal trend 2.53 days, p = 0.0007), primarily driven by earlier spring onset (SOS: Sen’s slope −0.183 days/yr, Z = −3.85, p < 0.001), while autumn dormancy (EOS) showed limited delay (Sen’s slope +0.051 days/yr, Z = 0.78, p = 0.435). (2) GSL changes exhibit pronounced spatial heterogeneity and ecosystem-specific responses: southeastern warm–wet regions display the strongest responses, with temperature as the dominant driver (mean partial correlation coefficient 0.62); in high–cold arid regions, warming substantially extends GSL (Z = 3.8, p < 0.001), whereas in warm–wet regions, growth may be constrained by water stress (Z = −2.3, p < 0.05). Grasslands (Z = 3.6, p < 0.001) and urban areas (Z = 3.2, p < 0.01) show the largest GSL extension, while evergreen forests and wetlands remain relatively stable, reflecting both the “climate sentinel” role of sensitive ecosystems and the carbon sequestration value of stable ecosystems. (3) Multi-factor interactions are complex and nonlinear; temperature, precipitation, radiation, and ET interact significantly, and extreme climate events may induce lagged effects, with clear thresholds and spatial dependence. (4) The use of GEE enables large-scale, multi-year, pixel-level GSL analysis, providing high-precision evidence for phenological quantification and critical parameters for carbon cycle modeling, ecosystem service assessment, and adaptive management. Overall, this study systematically reveals the lengthening and asymmetric patterns of GSL on the Tibetan Plateau, elucidates diverse land cover and climate responses, advances understanding of high-altitude ecosystem adaptability and climate resilience, and provides scientific guidance for regional ecological protection, sustainable management, and future phenology prediction. Full article

The biosphere is undergoing critical transformations due to deforestation, agricultural expansion, and logging, which have led to biodiversity loss, degradation of ecosystem services, and climate change. In tropical forests such as the Ecuadorian Amazon, these pressures are especially severe because reductions in forest cover compromise key ecological processes. The purpose of this article is to analyze the relationship between shifting agriculture, food security, and conservation in the Ecuadorian Amazon, with emphasis on the agroforestry system known as the chakra practiced by Kichwa communities. This model integrates crops such as cacao, maize, and cassava with native trees, without chemical inputs, and constitutes a practice that is both culturally significant and environmentally sustainable. Whereas conventional shifting agriculture tends to reduce soil fertility and the forest’s regenerative capacity, chakras maintain important levels of floristic diversity, favor the conservation of endemic species, and provide ecosystem services such as carbon sequestration and nutrient regulation. In this sense, chakras represent a resilient yet context-dependent agroforestry alternative that connects food security and sovereignty, biological conservation, income, Indigenous identity, and climate-change mitigation, although their long-term sustainability remains influenced by market forces, land-use pressure, and policy support in tropical contexts. Full article

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes. Full article

A multiproxy study of a new Pleistocene locality at Ivantzevo, Moscow Region, was conducted to reconstruct paleoenvironments from the Middle Pleistocene to the Last Pleniglacial. Lacustrine deposits and peat accumulated in a wetland within a fluvioglacial depression formed during the Dnieper–Moscow glaciation. Silts and clays were deposited during MIS 7 and the Moscow (Saale) Glaciation (MIS 6), while peat accumulation began in the Mikulino (Eemian) (MIS 5e). The wetland persisted for approximately fifty millennia, until the Middle Valdai (Weichselian). Interglacial peat deposits contain well-preserved pollen and macrofossils, and the recovered fossil insect assemblage is unique for European Russia. Chronology was established using multiple OSL and ²³⁰Th/U dates, combined with pollen-based correlations to type sections north and west of the region. The reconstructed ecosystem dynamics are divided into eleven stages. The transition from the last interglacial to the second stadial of the Valdai involved seven phases: (1) expansion of boreal spruce forest, (2) spread of thermophilic broad-leaved forests with hazel, (3) development of open forest–steppe ecosystems with groves of deciduous trees, (4) re-establishment of forest cover with birch and, later, mixed pine, spruce, and birch forests, (5) emergence of cold steppe combined with shrub-dominated tundra, (6) return of boreal spruce forest, and (7) abrupt replacement of forest by cold steppe and shrub tundra. Climatic reconstructions indicate that these ecosystem dynamics closely corresponded to changes in precipitation and aridity. Full article

Analysis of historical and future land use/land cover (LULC) dynamics using spatiotemporal data is crucial for better management of natural resources and environmental monitoring. This study investigated LULC transformations over a span of 60 years (1984–2044) for the Giba basin in northern Ethiopia. ArcGIS and the Cellular Automata and Artificial Neural Network (CA-ANN) model were used to develop the historical (1984, 2004, 2014, and 2024) and projected future (2034 and 2044) LULC maps of the basin, respectively. The results show that LULC categories experienced shifts from one class to another by 35%, 33%, and 40% in 2004–2014, 2014–2024, and 2004–2024, respectively. During 1984–2024, the largest and smallest percentage of positive changes were observed in settlement (7700%) and shrubs and bushes (25%), which increased from negligible to 78 km² and from 1668 km² to 2082 km², respectively. Furthermore, barren land and forestland showed the largest (−80%) and smallest (−37%) declines, which decreased from 956 km² to 187 km² and from 164 km² to 103 km² during the same period, respectively. Overall, the last 40 years witnessed considerable changes to LULC dynamics in the Giba basin. Cropland, water bodies, and settlements showed a continuously increasing trend throughout the historical study period, while grassland exhibited a continuous decreasing trend. Results of the CA-ANN model showed that the majority of the LULC categories (including water body, forest, bushes and shrubs, grassland, and barren land) will decrease, except for a slight increase of cropland (+6%) and settlements (+16%), which is projected to increase from 2570 km² to 2733 km² and from 78 km² to 91 km², respectively, in the next two decades, from 2024 to 2044. In general, high population increase, changes in government policies, and armed conflicts were found to be the most influential driving factors of LULC changes in the basin. Full article

The soil freeze–thaw process is a dominant disturbance in the seasonally frozen ground and the active layer of permafrost, which plays a crucial role in the surface energy balance, water cycle, and carbon exchange and has a pronounced influence on vegetation phenology. This study proposes a novel density-based Freeze–Thaw Disturbance Index (FTDI) based on the identification of the freeze–thaw disturbance region (FTDR) over the Qinghai–Tibet Plateau (QTP). FTDI is defined as an areal density metric based on geomorphic disturbances, i.e., the proportion of FTDRs within a given region, with higher values indicating greater areal densities of disturbance. As a measure of landform clustering, FTDI complements existing freeze–thaw process indicators and provides a means to assess the geomorphic impacts of climate-driven freeze–thaw changes during permafrost degradation. The main conclusions are as follows: the FTDR results that are identified by the random forest model are reliable and highly consistent with ground observations; the FTDRs cover 8.85% of the total area of the QTP, and mainly in the central and eastern regions, characterized by prolonged freezing durations and the average annual ground temperature (MAGT) is close to 0 °C, making the soil in these regions highly susceptible to warming-induced disturbances. Most of the plateau exhibits low or negligible FTDI values. As a geomorphic indicator, FTDI reflects the impact of potential freeze–thaw dynamic phase changes on the surface. Higher FTDI values indicate a greater likelihood of surface thawing processes triggered by rising temperatures, which impact surface processes. Regions with relatively high FTDI values often contain substantial amounts of organic carbon, and may experience delayed vegetation green-up despite general warming trends. This study introduces the FTDI derived from the FTDR as a novel index, offering fresh insights into the study of freeze–thaw processes in the context of climate change. Full article

Recent advancements in remote sensing and geospatial processing tools have ushered in a new era of mapping and monitoring landscape changes across various scales. This progress is critical for understanding and anticipating the underlying drivers of environmental change. In particular, large-scale Land Use and Land Cover (LULC) mapping has become an indispensable tool for territorial planning and monitoring. This study aims to map and evaluate LULC changes in the El Jadida region of Morocco between 1985 and 2020. Utilizing multispectral Landsat imagery, we applied and compared three supervised machine learning classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NNET). Model performance was assessed using statistical metrics, including overall accuracy, the Kappa coefficient, and the F1 score. The results indicate that the RF algorithm was the most effective, achieving an overall accuracy of 90.3% and a Kappa coefficient of 0.859, outperforming both NNET (81.3%; Kappa = 0.722) and SVM (80.2%; Kappa = 0.703). Analysis of explanatory variables underscored the decisive contribution of the NDWI, NDBI, and SWIR and thermal bands in discriminating land cover classes. The spatio-temporal analysis reveals significant urban expansion, primarily at the expense of agricultural land, while forested areas and water bodies remained relatively stable. This trend highlights the growing influence of anthropogenic pressure on landscape structure and underscores its implications for sustainable resource management and land use planning. The findings demonstrate the high efficacy of machine learning, particularly the RF algorithm, for accurate LULC mapping and change detection in the El Jadida region. This study provides a critical evidence base for regional planners to address the ongoing loss of agricultural land to urban expansion. Full article

Mediterranean relict forests, including Laurisilva and other humid forest refugia, are rare and ecologically distinctive habitats often embedded in fire-prone landscapes. Understanding how these ecosystems respond to disturbance is essential for biodiversity conservation and land management under increasing fire risk. However, the effects of fire on key components of these forests, such as macrofungi, remain poorly understood. Here, we examined how fine-scale spatial heterogeneity in fire severity, topography and vegetation shapes post-fire macrofungal communities in a Laurisilva relict forest in central Portugal. Fire severity reduced mycorrhizal richness while having negligible effects on saprotrophs, leading to shifts in the mycorrhizal-to-saprotrophic richness ratio along severity gradients. A similar shift toward saprotrophs also occurred from low to moderate–high elevations, consistent with more exposed, drier conditions at higher elevations. Aspect, topographic ruggedness, and wetness showed weaker, guild-specific associations with macrofungal richness, while vegetation cover and richness had more limited influence, possibly reflecting the complexity and vulnerability of post-fire plant–fungus interactions. Overall, these results highlight the importance of conserving humid and structurally complex environments to foster post-fire fungal diversity in relict forests. More broadly, our findings suggest that fine-scale environmental heterogeneity may help sustain relict forest resilience under intensifying wildfires and other disturbances associated with land-use and climate change. Full article

The Island of Unguja in Zanzibar (Tanzania) has experienced an accelerated urban development growth since the 1990s due to a rapidly increasing population. These rapid land demands put additional stress on the country’s ability to plan urban centers, cities, and the management of natural resources. The study aimed to determine the impact of urbanization on groundwater availability in the catchment area of the Masingini–Mwanyanya forest reserves from 1992 to 2022. The study used a detection approach to determine the Land Use Land Cover (LULC) changes for three decades, starting from 1992 to 2022. Landsat remote sensed images of 1992, 2002, 2012, and 2022 were used. Additionally, a paired t-test was conducted to determine the significant changes in mean population growth, urbanization, and humidity. The aquifer recharge evolution analysis was conducted using the QGIS software (3.34.8 released version). Obtained results revealed that for these three decades, the forest areas decreased by 14.5% (i.e., from 8.3 km² in 1992 to 7.1 km² in 2022), while built-up area increased from 0 km² in 1992 to 1.7 km² in 2022. Moreover, the evolution of undesirable Land Use Land Cover (LULC) changes, particularly the persistent conversion of forested areas into built-up zones, has been detected. This trend poses a significant threat to the sustainable management of water resources and catchment forest reserves. The study also indicated a decline in the recharge of the coastal aquifer supplying Zanzibar City, which decreased from 15.5 Mm³ to 11.1 Mm³. These findings highlight that the Masingini Forest Reserve is increasingly encroached by rapid urbanization, which is a phenomenon that may jeopardize the availability and sustainability of groundwater resources in the catchment without proper urban planning. Based on these results, the study recommends further research and upscaling of the existing findings, as well as collaboration with relevant authorities to redefine the Masingini–Mwanyanya forest catchment area to ensure the sustainable use of groundwater resources. Full article

Under the context of global climate change and China’s dual carbon strategy (DCS), the impact of land use/land cover change (LULCC) on regional carbon stocks has garnered increasing attention. As a key economic and ecological hub in Southwest China, Chengdu has undergone significant urbanization over the past two decades, and it is necessary to quantitatively assess how shifts in land use affect its carbon stock function. This study integrates multi-period remote sensing data from 2000 to 2020, combining socioeconomic and natural environmental drivers. The PLUS model was employed to simulate land use in 2030 under four scenarios: Natural Development Scenario (NDS), Urban Development Scenario (UDS), Conservation of Cropland Scenario (CPS), and Ecological Protection Scenario (EPS). The InVEST model was then used to calculate changes in carbon stocks and their spatial distribution characteristics. The results indicate the following: (1) From 2000 to 2020, Chengdu’s cropland decreased by 1188.6174 km², while built-up land increased by 1006.5465 km², resulting in a net carbon stock decrease of approximately 3.25 × 10⁶ t, with carbon gains from forest restoration offsetting part of the cropland-to-built-up loss; (2) Under all scenarios, built-up land exhibited an expansion trend, with the UDS showing the most significant increase, reaching 1919.2455 km². In the EPS, the forest increased to 4035.258 km², achieving the largest carbon stock increase of 8.5853 × 10⁶ t. (3) Chengdu’s carbon stock exhibits a spatial distribution pattern characterized by “high in the northwest, low in the center”. High-value areas are concentrated in the ecologically sound Longmen Mountains and Longquan Mountains, while low-value areas are primarily located in urban built-up zones and their peripheries. The study indicates that rationally controlling the expansion of Built-up land, strengthening ecological restoration, and protecting forests can effectively enhance Chengdu’s carbon sink capacity and achieve regional low-carbon and sustainable development. This study aims to address the gap in carbon stock assessments under different development scenarios at the urban scale in Southwest China, and to provide a scientific basis for Chengdu’s regional spatial planning, ecological conservation, low-carbon development, and sustainable land management. Full article

The invasive expansion of Phragmites australis in coastal wetlands, including the Long Point wetland complex in Ontario, has led to significant declines in plant and wildlife diversity, impacting ecosystem functions. Despite ongoing management efforts, the long-term ecological outcomes of Phragmites control remain poorly understood. This study developed a framework to evaluate the long-term efficacy of herbicide treatment by tracking changes in target and non-target plant species and fish habitats in Long Point, Ontario, over an eight-year period (2016–2024). High-resolution satellite imagery from WorldView sensors was classified using a random forest algorithm, achieving over 94% mapping accuracy. Results showed a decrease in Phragmites cover (3–21%) and an increase in fish habitat area (7–58%) within treatment areas. However, some sites also experienced increases in Dead Vegetation (up to 23.6%) and declines in Grass/Herbaceous and Typha (up to 20.5% and 32%, respectively). These findings highlight both the success of Phragmites Best Management Practices and the temporary non-target effects on wetland vegetation. Full article

The urban forest canopy provides critical ecosystem services, including carbon storage and sequestration. Healthy, well-managed trees in an urban setting can provide these services in a way comparable to forests managed for production or as nature preserves. Disturbance events threaten these benefits by reducing canopy cover and biomass. A tornado struck Ruston, Louisiana, on 25 April 2019, resulting in severe canopy damage through a swatch of the city. We used iTree Canopy to obtain estimates of ecosystem services (carbon sequestration, etc.) and converted this to a per-pixel value before interpolating for the study area. Fractional vegetation estimates obtained from spectral unmixing were obtained from pre- and post-tornado images using Sentinel-2 data and applied to weight damage. Pre- and post-tornado assessments revealed that Ruston’s urban forest canopy sequestered 85% of its pre-storm capability, with an estimated decline in social value of approximately $36,000. Assessing disturbance-based landscape changes, and subsequently calculating fractional changes in biomass and corresponding monetary impacts, will increasingly be looked to as ecosystem services and severe weather events are expected to become more commonplace in the future. The methodology employed demonstrates a cost-effective way to assess disturbance impacts in small urban areas, offering a framework to small municipalities to monitor canopy dynamics. Full article