Search Results (5,797)

Single-source remote sensing image (RSI) cannot simultaneously meet high-spatial and high-temporal resolution requirements, failing to provide decision-makers with timely and accurate monitoring data. Spatiotemporal fusion (STF) of multi-source RSIs represents an efficient and convenient means of producing land-cover observations with high-temporal and high-spatial resolutions. However, current STF approaches still suffer from severe prediction distortion under abrupt changes, long-interval temporal variations, and land-cover type transitions, as well as poor robustness against disturbances in prior data. To address these challenges, a temporal-variation-resistant bidirectional convolution-Transformer generative adversarial network (TRB-GAN) for RSI STF, which comprises a temporal-variation-resistant bidirectional convolution-Transformer generator (TRBG) and a multiresolution input convolution-Transformer discriminator (MICTD), is devised to improve the robustness in predicting time-varying information and enhance STF capability. First, the TRBG designs a temporal-variation-resistant bidirectional encoder to capture prior information and arbitrary time-varying local–global features, enhancing prediction robustness and representation capability for time-varying information. Second, the TRBG designs a dual-guided triple-attention fusion decoder (DTAFD), incorporating dual-guided cross convolution-attention fusion and decision attention fusion. DTAFD dynamically calculates correlations among spectral, spatial, and time-varying information to aggregate heterogeneous features and adaptively performs stepwise weighting and integration, effectively mitigating the adverse impacts from heterogeneous imaging mechanisms and significant resolution gaps. Finally, MICTD and deep supervision enable adversarial learning of local–global structures and spectra across resolutions, providing feedback to the TRBG for producing finer images. Ablation and comparative experiments demonstrate the TRB-GAN achieves superior STF performance and stronger robustness to time-varying disturbances for the widely used CIA and LGC datasets. Full article

The Hetao Irrigation District in arid northwestern China presents a significant challenge in balancing agricultural intensification and water conservation, particularly in its terminal lake, Wuliangsu Lake. This study examined how changes in Land Use/Land Cover (LULC) and cropping structures influenced the lake’s water quality. By using remote sensing data for LULC classification and agricultural statistics for crop composition, we analyzed the spatio-temporal variation in LULC and cropping structure in the irrigation district and quantified the associated agricultural non-point source pollution loads (total nitrogen, total phosphorus, and chemical oxygen demand) entering the lake. A calibrated Environmental Fluid Dynamics Code model was applied to evaluate water quality responses to cropping structure optimization. Our findings revealed significant shifts in LULC and cropping structure during the study period, driven by agricultural intensification, ecological restoration policies, urbanization, market forces, and national food security strategies. Concurrently, agricultural non-point source pollution loads into the lake showed a steady declining trend from 2018 to 2023, with total nitrogen (TN) decreasing by 15%, total phosphorus (TP) by 16.9%, and chemical oxygen demand (COD) by 19.4%. Model simulations demonstrated that optimizing the cropping structure, specifically by reducing the area of high-fertilizer crops (sunflower) and expanding low-fertilizer crops (spring wheat) and forage crops for ecological purposes, could further improve lake water quality. Under the intensive adjustment scenario, the inflow loads of TN, TP, and COD decreased by 10%, 11.7%, and 10.9%, respectively, while the corresponding in-lake concentrations decreased by 22.1%, 19.8%, and 18.7%, respectively. TP exhibited the highest sensitivity to such adjustments. By linking cropping structure adjustments with hydrodynamic-water quality modeling, this study provides a quantitative framework for assessing water quality responses in arid irrigated systems, offering a scientific basis for balancing agricultural production and water ecosystem protection in the Hetao district and similar regions. Full article

Climate change poses increasing threats to urban populations, with rising temperatures exacerbating heat-related health risks and reducing outdoor thermal comfort. Although cities occupy only a small fraction of the Earth’s surface, they function as complex ecosystems where human transformations significantly influence environmental quality and public health. This research introduces a trans-scalar methodological framework to assess the impacts of climate change on human well-being in urban areas, integrating qualitative and quantitative approaches. The methodology combines direct field observations, instrumental surveys using infrared thermography, quantitative land cover analysis (Runoff and RIE indices), and predictive microclimatic simulations using ENVI-met 5.8. The framework is applied to two case studies in Italy, the municipalities of Bibbiena and Borgo San Lorenzo, focusing on pedestrian accessibility to community healthcare facilities, known as Community Houses. Results reveal critical environmental vulnerabilities, including surface temperatures reaching up to 60 °C on impervious materials, runoff coefficients exceeding 80%, and Physiological Equivalent Temperature (PET) values exceeding 56 °C during summer peak hours, indicating extreme heat stress conditions. The proposed framework enables the identification of priority intervention areas for climate adaptation strategies, supporting evidence-based urban design that prioritises public health and thermal comfort. The methodology offers replicability for other Mediterranean cities facing similar climate challenges, providing a decision-support tool for planners and public administrations. Full article

Understanding the spatiotemporal dynamics of land-use and cover change (LUCC) and ecosystem service (ES) responses is essential for assessing ecological functions in regional landscapes. However, conventional LUCC simulations often rely on historical trends and inadequately represent the spatially heterogeneous effects of top-down policy constraints. Taking the Henan section of the Yellow River Basin (HYRB) as a case study, we developed a policy-to-rule framework that translated ecological redlines, urban development boundaries, and restoration requirements into explicit spatial constraints and land-use transition rules in the PLUS model. A policy-constrained High-Quality Development Scenario (HQDS) was established, with the Natural Growth Scenario (NGS) as a reference. Five ESs were assessed using InVEST from 1985 to 2050, and the results were integrated with the Minimum Cumulative Resistance (MCR) model and circuit theory to construct an ecological security pattern (ESP). Historical reconstruction of the 2022 land-use pattern achieved an overall accuracy of 90.18% and a Kappa coefficient of 86.39%. The five ESs remained relatively stable overall: water yield, soil conservation, and the sediment-related indicator increased, whereas habitat quality and carbon storage declined slightly. Ecological source areas expanded from 7140.54 km² in 1985 to 12,039.17 km² under the HQDS in 2050, a 68.6% increase. Compared with the NGS, the HQDS increased source areas by 562.42 km² (4.9%), reduced ecological corridors from 26 to 24, and increased their total length from 1068.89 to 1099.61 km. These differences represent the projected, scenario-conditioned consequences of the specified policy constraints and provide quantitative decision support for future ecological management. Full article

Rapid urbanization is reshaping land surface conditions and local thermal environments in fast-growing coastal cities. This study examines how Land Use Land Cover (LULC) transformation influenced seasonal land surface temperature (LST) dynamics in Chattogram City Corporation (CCC), Bangladesh, over 2004–2024. Multi-temporal Landsat imagery was analyzed using a Random Forest classifier, and spectral indices (NDVI, NDBI, NDBaI, MNDWI) were derived to characterize surface biophysical conditions. Built-up land expanded by 27.71 km², largely replacing agricultural and vegetated areas. Summer mean LST rose from 36.08 °C to 36.50 °C, while winter LST rose from 25.25 °C to 26.97 °C. Only the winter warming trend is statistically significant; the summer change falls within the ±1–2 °C retrieval uncertainty of Landsat-derived LST. The summer–winter thermal gap consequently narrowed from 10.83 °C to 9.53 °C, indicating that urbanization-driven warming in this tropical coastal city is disproportionately concentrated in the cool dry season. Partial correlation and multiple regression analyses confirm that built-up intensity (NDBI) is the dominant driver of surface warming, while vegetation (NDVI) exerts a consistent cooling influence. Water bodies showed contrasting seasonal trends, with winter extent declining alongside a slight summer increase. These findings highlight the critical role of vegetation and water bodies in moderating urban heat and provide data-driven insights for climate-responsive planning in rapidly urbanizing coastal cities. Full article

Climate variability and land use/land cover changes (LULCC) can intensify soil erosion. This study evaluated the relative influence of LULCC and climate change on soil water erosion in an Andean watershed in Ecuador. Soil erosion was assessed using the RUSLE model for 1996, 2023, and 2040. Future erosion was predicted for 2040 using projected land use/land cover (LULC) and two climate scenarios under the Shared Socioeconomic Pathways SSP585 (pessimistic) and SSP126 (optimistic). Meanwhile, LULC was projected until 2040 through a Cellular Automata Markov model (CA-Markov). Climate scenarios for 2021–2041 were generated through statistical downscaling of the MPI-ESM-1-2-HR climate model. Results showed that erosion increased by 17.8% from 1996 to 2023; meanwhile, by 2040, it is projected to increase by up to 65.1% under both climate scenarios. A factorial sensitivity analysis revealed that LULCC is the primary driver of erosion, accounting for over 82% of the total variation in erosion, while the contribution of climate change remains secondary. These findings suggest that in tropical mountain zones, loss of vegetation cover has a greater impact on erosion rates than climatic variability, regardless of the climate scenario, confirming that the effect of climate change on soil erosion remains marginal. Full article

The rapid expansion of road construction has significantly contributed to economic development and regional connectivity. However, linear infrastructure such as roads, railways, and utility corridors has also introduced considerable ecological disruptions. Although increasing global attention is being paid to mitigating these effects, most existing research primarily focuses on quantifying and monitoring external environmental changes (e.g., landscape structure or vegetation coverage) while often neglecting internal ecological dynamics such as ecosystem resilience. Previous road-ecology studies have extensively examined road-buffer effects, land-use/land-cover changes, vegetation-index dynamics, and landscape fragmentation. Therefore, the contribution of this study does not lie in proposing an entirely new class of indicators. Rather, it lies in applying a combined external–internal assessment framework to a recently constructed expressway corridor by jointly examining annual land-cover transitions and leaf area index (LAI)-derived temporal variability/resilience indicators across multiple distance buffers and spatial resolutions. This design allows us to compare whether structural land-cover changes and vegetation time-series responses show similar distance–decay patterns around the expressway corridor. The results show that: (1) Land-cover transformation was mainly concentrated within the first 500–1000 m from the expressway, especially for impervious surface expansion and vegetation loss. Multi-indicator distance-gradient analysis showed that land-cover change intensity and LAI-derived variability indicators gradually approached the distal reference condition at approximately 2000 m, which was therefore used as an empirical corridor-analysis boundary rather than a definitive ecological impact threshold. (2) Within the 2000 m buffer zone, forest area increased from 21.866 km² in 2001 to 45.370 km² in 2023, while impervious surface area increased from 3.016 km² to 6.869 km². During the construction and early operation period from 2018 to 2023, impervious surface area increased from 6.268 km² to 6.869 km², indicating localized artificial surface expansion along the expressway corridor. (3) During 2018–2023, the 30 m LAI product showed a 22.3% increase in coefficient of variation (CV), indicating enhanced relative LAI variability. In contrast, temporal autocorrelation (TAC) did not show the consistent increase expected under classical critical slowing down theory, suggesting that TAC-based evidence for resilience decline was weak or inconclusive during this short period. The observed TAC/CV changes were interpreted as critical slowing down (CSD)-related vegetation variability signals, rather than as a distinct or definitive critical slowing down signature. (4) The multi-resolution comparison showed weak pixel-level correspondence between the 30 m and 250 m LAI products, indicating clear scale dependence rather than robust multi-scale consistency. The 250 m data were useful for characterizing long-term regional background trends, whereas the 30 m data were more suitable for detecting localized corridor-scale vegetation variability. Thus, the multi-resolution analysis should be regarded as a scale-sensitivity assessment rather than as direct cross-scale validation. Full article

High-resolution Normalized Difference Vegetation Index (NDVI) data are essential for monitoring fine-scale coastal environmental dynamics, yet persistent cloud cover, rapid geomorphic change, and strong spatial heterogeneity limit the availability of temporally continuous observations. Existing spatiotemporal fusion approaches can partially address these limitations, but many rely primarily on data-driven feature learning and do not explicitly incorporate geographic information, leading to boundary blurring, structural inconsistency, and sensitivity to background noise in complex coastal environments. This study presents a geographically constrained Transformer-based framework for 2 m NDVI spatiotemporal reconstruction in coastal landscapes named Coastal-Prior-Embedded Global–Local Fusion Transformer (Coastal-GLFT). The approach integrates high-resolution Gaofen-6 panchromatic and multispectral imagery with high-frequency wide-field-view observations and auxiliary geographic datasets describing elevation, coastline proximity, and land use/land cover. Geographic priors were incorporated as explicit spatial constraints, while a spatiotemporal gating mechanism and global–local fusion architecture were used to improve the representation of temporal variation and multi-scale spatial structure. The method was evaluated using a multi-temporal dataset for the Yellow River Delta comprising 49 high-resolution scenes and 137 coarse-resolution scenes acquired between 2020 and 2025. Compared with representative physics-based, convolutional neural network, generative adversarial network, and Transformer-based fusion methods, the proposed approach reduced reconstruction error by approximately 5–72%, increased signal fidelity by approximately 1–12%, and improved structural similarity by approximately 2–52%. Compared with the strongest Transformer-based baseline, SwinSTFM, Coastal-GLFT reduced RMSE from 0.0896 to 0.0855, increased PSNR from 36.19 dB to 37.09 dB, and improved SSIM from 0.8551 to 0.8742. Qualitative analysis further demonstrated improved preservation of boundary structure, spatial continuity, and heterogeneous coastal features, including aquaculture ponds, tidal creeks, and fragmented wetlands. These results indicate that integrating geographic constraints with multi-scale Transformer-based reconstruction can improve the fidelity and structural consistency of high-resolution NDVI reconstruction in complex coastal environments. The framework provides a basis for fine-scale coastal vegetation monitoring and land-cover analysis, while future work should assess transferability across diverse coastal systems and improve computational scalability. Full article

Chinese wolfberry (Lycium chinense Miller) is a vital medicinal plant and a key species for ecological restoration. While Species Distribution Models (SDMs) are commonly used to predict habitat suitability, they often rely exclusively on native occurrence data, potentially underestimating a species’ climatic niche breadth under assumptions of niche conservatism. This study aims to evaluate the global climatic tolerance of Chinese wolfberry by integrating occurrence data from both native and introduced ranges. We assess the impact of climate change on its potential range shifts in China and determine whether native-only models underestimate the species’ climatic tolerance. We compiled 984 high-quality global distribution points (536 native, 448 introduced). We compared climatic niche breadth derived from native-only data with that from global data. Using MaxEnt modeling, we projected the global niche model onto China under current conditions and under four future Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585) for the year 2070 to analyze changes in habitat suitability and centroid migration. The study confirms that relying solely on native data significantly underestimates the climatic tolerance of Chinese wolfberry, particularly for thermal variables (e.g., mean temperature of the warmest/coldest month). The global model, driven primarily by the coldness index, annual biotemperature, and humidity index, showed excellent performance (AUC = 0.952). Currently, highly suitable habitats cover approximately 44% of China’s land area. However, future projections indicate a severe decline in habitat quality. While the total suitable area remains relatively stable, the proportion of highly suitable habitat within the total habitat is projected to drop from 62.6% to as low as 19% under high-emission scenarios. The centroid of highly suitable habitats is expected to migrate multidirectionally at an annual rate of 0.97–2.42 km·a⁻¹, while the altitudinal centroid shows a drastic upward shift of up to 7.24 m·a⁻¹ under the SSP585 scenario. Integrating global occurrence data provides a more robust estimate of the ecological tolerance of Chinese wolfberry. Future climate change poses a significant threat to the quality of suitable habitats in China, necessitating adaptive management strategies such as assisted migration to northern refugia and the implementation of climate-smart agricultural practices to sustain the economic and ecological value of this species. Full article

Agrivoltaic (AV) systems represent a promising strategy for integrating renewable energy production and agricultural activity on the same land unit, while contributing to soil water conservation under increasingly frequent drought conditions. This preliminary, single-site case study investigates the effects of a horizontal biaxial tracking AV system on soil water dynamics in a durum wheat field in the Po Valley (Borgo Virgilio, Mantua, Italy) over a full monitoring period, covering the final crop growth stages and the post-harvest bare soil phase (May–December 2024). Monitoring of soil temperature, volumetric water content (VWC), and soil water potential (SWP) was conducted at four depths (15, 30, 45, and 60 cm) at one representative monitoring station per treatment, comparing soil under AV cover (AVC) and in unshaded conditions (UC), located 10 m apart. Paired VWC and SWP measurements were used to derive site-specific soil water characteristic curves (SWCCs) and to calibrate the agro-hydrological model CRITERIA-1D, which was used to estimate available water (AW) in the first 80 cm of depth for both treatments. Measured VWC values were higher in the AVC profile than in the UC profile at all monitored depths throughout the May–September period, with differences persisting, although at lower values through October–December. Estimated AW was consistently higher under AVC than in UC during both the dry and wet periods. Despite higher VWC, the AVC profile showed more negative average SWP values at all depths during summer. This pattern is consistent with the shape of the derived SWCCs and may point to differences in water-retaining capacity between the two profiles, possibly related to structural modifications induced by 13 years of AV system operation. These preliminary findings suggest that AV systems could potentially improve soil water availability in the root zone of rainfed cereal crops and propose the hypothesis that long-term AV cover may act as a driver of changes in soil hydraulic properties, with implications for the sustainability and climate resilience of dryland farming systems. However, given the design of this case study, with only one monitoring point per treatment, the observed differences reflect the specific monitored locations and cannot fully disentangle the AV treatment effect from pre-existing spatial heterogeneity in soil properties. The preliminary results obtained in this study should therefore not be generalised beyond the specific conditions of this case study, and the interpretations proposed here should be treated as unproven hypotheses rather than established conclusions. Further studies with spatial replication and multi-year monitoring are needed to confirm these patterns. Full article

Understanding vegetation dynamics and their driving mechanisms in arid regions is essential for assessing ecosystem resilience under global change. This study investigates the spatiotemporal patterns of vegetation greenness in Xinjiang, China, from 1985 to 2018, with a focus on land-use change and climatic influences. We used long-term Normalized Difference Vegetation Index (NDVI) data combined with climate variables and high-resolution land-use data. Trend analysis, residual analysis, breakpoint detection, and random forest sensitivity analysis were applied to examine vegetation dynamics and their potential driving factors. The results show that Xinjiang experienced a significant greening trend, with a pronounced turning point around 2000, coinciding with the implementation of large-scale ecological restoration projects. During 1985–1999, vegetation changes were closely related to temperature, particularly in mountain regions, while the positive effect of precipitation became more evident after 2000 due to increased vegetation cover and evapotranspiration demand. Land-use transformations, especially barren land to grassland, grassland to cropland, and grassland to forest, were strongly associated with vegetation greening. The baseline residual analysis indicated that a large proportion of vegetation greening after 2000 could not be fully explained by temperature and precipitation alone. Random forest sensitivity analysis further identified human footprint and temperature as important predictors of NDVI trends. These results suggest that human-related land-use and management activities, together with climatic factors, likely played important roles in shaping vegetation recovery in Xinjiang. Breakpoint analysis showed that many vegetation shifts occurred between 1999 and 2003, particularly in regions where ecological restoration projects were concentrated. Overall, our findings indicate that vegetation recovery in Xinjiang was jointly influenced by human-related activities and climate conditions, while this improvement remains constrained by water availability. These insights provide a scientific basis for optimizing ecological restoration strategies in arid and semi-arid regions. Full article

The escalating climate crisis presents a profound challenge to global environmental equilibrium, with rapid urbanization acting as a primary catalyst for land-use transformation. This study investigates the intricate relationship between land use and land cover changes (LULC) and the intensification of the Surface Urban Heat Island (SUHI) effect within the Shillong Planning Area (SPA). By integrating remote sensing data with advanced geospatial modeling and machine learning architectures which include Random Forest (RF), Support Vector Machine (SVM), and XGBoost, the research provides a comprehensive analysis of environmental shifts from 2000 to 2024, with predictive projections extending to 2034 and 2044. The analysis reveals a significant expansion in the built environment, with the Normalized Difference Built-up Index (NDBI) rising from 0.17 to 0.26. This urban growth has come at the expense of ecological health, as evidenced by a decline in the Normalized Difference Vegetation Index (NDVI) from a peak of 0.87 down to 0.74. A strong negative correlation between vegetative density and Land Surface Temperature (LST) underscores the critical role of green infrastructure in regional climate regulation. SUHI projections using the RF model, which achieved an Area Under the Curve (AUC) of 0.868, estimate SUHI values of 6.02 °C for 2034 and 6.66 °C for 2044. Predicted LULC scenarios for 2034 and 2044 suggest continued urban expansion, likely intensifying thermal stress. The application of predictive modeling through machine learning provides a robust framework to inform climate-resilient urban planning and sustainable land management. Full article

Understanding how land-use change affects habitat fragmentation and connectivity is essential for assessing landscape degradation and conservation effectiveness in protected areas globally. It is particularly acute in tropical protected areas where anthropogenic pressures are intensifying. This study investigated long-term landscape dynamics, forest fragmentation, and structural connectivity in the Bena Mulumbu Hunting Domain, a Category VI protected area located in the Miombo woodland region of southeastern Democratic Republic of the Congo. Landsat imagery acquired in 1995, 2005, 2015, and 2025 was classified using the Random Forest algorithm into six land-cover classes (Miombo woodland, savanna, agricultural land, mining areas, built-up/bare land, and water bodies) to quantify land-cover changes over 30 years. Landscape composition was assessed using the percentage of landscape (PLAND), Shannon diversity metrics, and transition analyses. At the same time, fragmentation and structural connectivity of Miombo woodland were evaluated along an edge-to-core gradient (0–2 km, 2–4 km, 4–6 km, and >6 km) using landscape metrics. Results showed that savanna remained the dominant land-cover type throughout the study period. However, the landscape underwent progressive reorganization characterized by recurrent transitions among Miombo woodland, savanna, and agricultural land, leading to increased spatial heterogeneity. Fragmentation analyses revealed significant spatial differences in total core area among zones (Kruskal–Wallis: H = 8.12, p = 0.044); however, after normalization by zone area, no consistent edge-to-core gradient was observed for core habitat proportion, indicating that raw differences primarily reflect zone size rather than a systematic ecological gradient. Despite increasing fragmentation, structural connectivity remained high across the hunting domain. The CONNECT index increased significantly from the edge toward the core zone (p = 0.003), highlighting better-connected forest networks in interior sectors. These findings suggest that the Bena Mulumbu Hunting Domain is experiencing an intermediate stage of landscape transformation, where forest fragmentation is evident but has not yet resulted in widespread connectivity loss. Maintaining existing forest cores and connectivity corridors should therefore be prioritized to prevent further degradation of ecological integrity. These findings challenge the assumption that landscape degradation in protected tropical Miombo woodlands necessarily follows a simple edge-to-core gradient. Full article

Understanding long-term changes in agricultural landscapes is essential for assessing the sustainability of land-use systems and guiding agro-environmental policy. This study analyzes three decades of landscape simplification and natural habitat complexity (1990–2018) across three spatial scales—Europe, Spain, and Extremadura—using CORINE Land Cover data and generalized additive models (GAMs). Four agricultural systems were examined: total agriculture, vineyards, olive groves, and rice fields. For each year and region, 1000 randomly selected 1 km buffers were used to quantify the proportions of focal crops and natural habitat as indicators of landscape simplification and complexity, respectively. Results showed that landscape simplification was widespread and statistically significant across most agricultural systems, particularly in Spanish vineyards (p = 3.02 × 10⁻⁵) and rice fields (p = 3.74 × 10⁻⁵). Temporal analyses indicated declining simplification trends in Europe and Spain (approximately −1.5%), whereas Extremadura exhibited increasing simplification (+1.5%) and a decline in landscape complexity after 2006. Landscape complexity showed fewer significant trends, with Europe and Spain displaying slight increases over time, whereas Extremadura shifted from initial complexity gains to a decline after 2006, indicating pressures from irrigation expansion and crop intensification. Overall, the results reveal marked spatial differences in agricultural landscape trajectories, demonstrating how intensification processes depend on crop type, regional context, and policy frameworks. This multi-scale analysis provides a quantitative basis for designing land-use policies that preserve ecological connectivity, promote diversification, and strengthen the long-term sustainability of agricultural landscapes. Full article

Post-disaster recovery is increasingly understood not as a simple return to pre-event conditions but as a dynamic reorganization of land systems, in which land cover and land use change (LCLUC) provides an operational signature of recovery trajectories. However, most existing assessments reduce recovery to a single dimension—typically vegetation greenness—which can conflate systems that differ fundamentally in their response behavior. This study develops a multi-index Recovery Resilience Index for Land Systems (RRI-LS) within Google Earth Engine and applies it to the catastrophic liquefaction zone of the 2018 Mw 7.5 Palu earthquake (Central Sulawesi, Indonesia). Combining Sentinel-2 spectral indices (NDVI, NDBI, BSI), Dynamic World land-cover labels, and a hybrid Top-of-Atmosphere/Surface-Reflectance baseline to overcome the sparse pre-event archive, we quantify three resilience dimensions—resistance, recovery, and stability—and classify recovery into qualitatively distinct pathways. The hybrid baseline is quantitatively validated: after removing a small systematic offset, the residual discrepancy between TOA- and SR-derived indices is 2.4–4.9 times smaller than the measured disturbance signal. Site-level analysis of the three principal liquefaction hotspots (Balaroa, Petobo, Jono-Oge) and a 1 km grid expansion (n = 962 cells) reveal that disturbance-affected areas did not converge on a single outcome but diverged into bounce-back, transformational, reconstructed (non-vegetated), and degraded pathways; a basin-wide re-run at 250 m (n = 14,157 cells) reproduced the same pathway hierarchy, confirming robustness to grid resolution. Initial disturbance intensity was a poor predictor of long-term recovery (R² = 0.07, p < 0.001), underscoring that recovery is multidimensional and not reducible to a single shock variable. The emergence of a reconstructed, non-vegetated pathway—where bare-soil disturbance is resolved through built surfaces rather than re-greening—demonstrates that vegetation metrics alone are insufficient in human-dominated landscapes. The framework supports a land-system perspective in which recovery is conceptualized as the establishment of new functional equilibria. Full article