Search Results (1,124)

Mapping banana and peach palm in heterogeneous landscapes remains challenging due to spatial heterogeneity, spectral similarities between crops and native vegetation, and persistent cloud cover. This study focused on the municipality of Jacupiranga, located within the Ribeira Valley region and surrounded by the Atlantic Forest, which is home to one of Brazil’s largest remaining continuous forest areas. More than 99% of Jacupiranga’s agricultural output in the 21st century came from bananas (Musa spp.) and peach palms (Bactris gasipaes), underscoring the importance of perennial crops to the local economy and traditional communities. Using a time series of vegetation indices from Sentinel-2 imagery combined with field and remote data, we used a hierarchical classification method to map where these two crops are cultivated. The Random Forest classifier fed with 10 m resolution images enabled the detection of intricate agricultural mosaics that are typical of family farming systems and improved class separability between perennial and non-perennial crops and banana and peach palm. These results show how combining geographic information systems, data analysis, and remote sensing can improve digital agriculture, rural management, and sustainable agricultural development in socio-environmentally important areas. Full article

Drought-induced forest mortality threatens biodiversity globally, particularly in arid, and semi-arid woodlands. The continual development of remote sensing approaches enables enhanced monitoring of forest health. Herein, we investigate the ability of a limited ground-truthed canopy dieback dataset and satellite image derived Normalised Difference Vegetation Index (NDVI) to make inferences about forest health as temporal and spatial extent from its collection increases. We used ground-truthed observations of relative canopy mortality from the Pinus edulis-Juniperus osteosperma woodlands of southeastern Utah, United States of America, collected after the 2017–2018 drought, and PlanetScope satellite imagery. Through assessing different modelling approaches, we found that NDVI is significantly associated with sitewide mean canopy dieback, with beta regression being the most optimal modelling framework due to the bounded nature of the variable relative canopy dieback. Model performance was further improved by incorporating the proportion of J. osteosperma as an interaction term, matching the reports of species-specific differential dieback. A time-series analysis revealed that NDVI retained its predictive power for our whole testing period; four years after the initial ground-truthing, thus enabling retrospective inference of defoliation and regreening. A spatial random forest model trained on our ground-truthed observations accurately predicted dieback across the broader landscape. These findings demonstrate that modest field campaigns combined with high-resolution satellite data can generate reliable, scalable insights into forest health, offering a cost-effective method for monitoring drought-impacted ecosystems under climate change. Full article

This paper presents a comprehensive methodology for assessing the resilience of landscapes to human impact in western Kazakhstan. The approach developed is based on integrating remote sensing data (MODIS, SMAP, NDVI and NDSI), the results of field surveys, and multi-criteria analysis methods in a GIS environment. The assessment covered over 50 landscape types and subtypes using ten key indicators reflecting climatic, geomorphological, soil, hydrological, and biotic characteristics. These indicators were normalised, aggregated and summarised to create an integral index of landscape resilience, which allowed four resilience classes to be identified, ranging from highly vulnerable to relatively resilient. The spatial analysis revealed that over 60% of the region’s territory is classified as high-vulnerability, predominantly within semi-desert and desert zones, which are vulnerable to climatic risks, degradation of vegetation cover and human activity. Verification of the results based on remote monitoring data for the period 2000–2024 and field observations confirmed the reliability of the developed methodology. The results obtained allow the identification of areas prioritised for environmental monitoring, restoration and sustainable land use in arid climate conditions. A plan of measures for regulation and restoration of ecosystems and spatial planning tools are proposed. The obtained data can be used for the development of regional environmental policy and sustainable land use strategies. Full article

High-resolution remote sensing imagery offers detailed spatial and semantic insights into the Earth’s surface, yet its classification remains hindered by the limited availability of labeled data, primarily due to the substantial expense and time required for manual annotation. To overcome this challenge, we propose a hybrid semi-supervised tri-training framework that integrates traditional classification methods with a lightweight convolutional neural network. By combining heterogeneous learners with complementary strengths, the framework iteratively assigns pseudo-labels to unlabeled samples and collaboratively refines model performance in a co-training manner. Additionally, a landscape-metric-guided relearning module is introduced to incorporate spatial configuration and land cover composition, further enhancing the framework’s representational capacity and classification robustness. Experiments were conducted on four high-resolution multispectral datasets (QuickBird (QB), WorldView-2 (WV-2), GeoEye-1 (GE-1), and ZY-3) covering diverse land-cover types and spatial resolutions. The results demonstrate that the proposed method surpasses state-of-the-art baselines by 1.5–10% while generating more spatially coherent classification maps. Full article

A GIS-based Plant Coefficient Method (PCM), termed the Plant Coefficient Method Tool (PCMT), is presented and validated through this research. It is designed for sustainable irrigation management within arid urban environments, exemplified by Riyadh, Saudi Arabia. The study integrates remote sensing data, including Landsat 8 satellite imagery, vegetation indices (NDVI, LAI), and climatic parameters to estimate daily and seasonal plant water demand for diverse landscape species. Results demonstrate that plant-specific coefficients (${\mathrm{K}}_{\mathrm{p}\mathrm{l}}$) fluctuate seasonally, ranging from 0.1 to 1.4, with average water demand (${\mathrm{E}\mathrm{T}}_{\mathrm{p}\mathrm{l}}$) reaching up to 25 L per square meter during the summer months and decreasing to around 6 L in winter. It may be found by good management based on PCMT that average daily projected ${\mathrm{E}\mathrm{T}}_{\mathrm{p}\mathrm{l}}$ rates can be lowered to as low as 3 mm/day, resulting in a significant decrease in water needs, by around 70% to 50%, when compared to higher categories. Validation across three sites (urban trees, date palms, and turf grass), showed strong correlations (R² > 0.8) between satellite-derived vegetation indices and modeled water needs. The volumetric water demand estimates closely aligned with actual irrigation practices, albeit with some over- and under-irrigation episodes. Spatial analysis indicated that high-demand zones predominantly occur in summer, emphasizing the necessity of adaptive irrigation scheduling. Overall, the PCMT presents a scalable, accurate tool for optimizing water use, supporting sustainable landscape management aligned with Saudi Arabia’s green initiatives. Full article

As critical components of regional ecological networks, the protection and development of ecological corridors (ECs) are essential for enhancing ecosystem stability. To promote the effective protection of ECs, this study develops an integrated framework—comprising ecological corridor identification, land use simulation, and construction cost assessment—to evaluate the cost of EC construction in Nanchang under multiple future land-use scenarios. High-resolution, multi-temporal remote sensing data were used to simulate land-use patterns for 2035 under three scenarios—ecological protection (EP), natural development (ND), and urban expansion (UE)—with the PLUS model. ECs were extracted using the Minimum Cumulative Resistance (MCR) model, and construction costs were quantitatively estimated by overlaying simulated land-use maps with corridor networks while incorporating land adjustment and compensation standards. The results show that: (1) 23 ECs (564.01 km in length, 997.93 km² in area) were identified in Nanchang, with higher corridor density in the northern and southeastern regions. (2) By 2035, the overall land-use structure in Nanchang is projected to remain broadly similar across the three scenarios, though differences will exist in the magnitude of change for individual land-use categories. (3) Cropland dominates the EC landscape (>60%) across all scenarios, while construction land accounts for 6.95%, 7.71%, and 8.39% under the EP, ND, and UE scenarios, respectively. (4) Estimated construction costs are 233.707, 262.354, and 288.897 billion RMB yuan under the EP, ND, and UE scenarios, respectively. Significant spatial variation in costs is observed, and the EP scenario does not consistently yield the lowest costs across administrative units. Additionally, this study proposes a refined zoning strategy for corridor management in Nanchang. The findings offer valuable insights for urban ecological planning and provide a scientific basis for mitigating regional ecological risks while promoting sustainable development in urbanized regions. Full article

Karst landscapes, characterized by ecological constraints such as thin soil layers, severe rock desertification, and fragile habitats, require a clear understanding of the mechanisms regulating carbon storage and the impacts of ecological restoration measures. However, current research lacks detailed insights into the specific effects of ecological restoration measures. This study integrates multi-source remote sensing data and adjusts InVEST model parameters to systematically reveal the spatiotemporal evolution of carbon storage and its driving mechanisms in typical karst plateau regions of southwest China under ecological restoration measures. The results indicate: (1) From 2000 to 2020, the carbon stock in the study area increased by 6.09% overall. However, from 2020 to 2025, due to the rapid conversion of forest land into building land and grassland, the carbon stock decreased sharply by 7.69%. (2) Severe rock desertification constrains carbon stock, and afforestation provides significantly higher long-term carbon sink benefits. (3) The spatial heterogeneity of carbon storage is primarily influenced by the combined effects of natural factors (rock desertification, elevation, NDVI) and human factors (POP). Based on the research findings, it is recommended that measures to promote close forests be prioritized in karst regions to protect and restore forest ecosystems. At the same time, local habitat improvement and the establishment of ecological compensation mechanisms should be implemented, and the expansion of building land should be strictly controlled to enhance the stability of ecosystems and their carbon sink functions. These research findings provide a solid scientific basis for enhancing and precisely regulating the carbon sink capacity of fragile karst ecosystems, and are of great significance for formulating scientifically sound and reasonable ecological protection policies. Full article

Background: The incorporation of biosensors into orthopedic implants, particularly tibial components in total knee arthroplasty (TKA), marks a new era in personalized joint replacement. These smart systems aim to provide real-time physiological and mechanical data, enabling dynamic postoperative monitoring and enhanced surgical precision. Objective: This narrative review synthesizes the current landscape of electrochemical biosensor-embedded tibial implants in TKA, exploring technical mechanisms, clinical applications, challenges, and future directions for translation into clinical practice. Methods: A comprehensive literature review was conducted across PubMed and Google Scholar. Articles were thematically categorized into technology design, integration strategies, preclinical and clinical evidence, regulatory frameworks, ethical considerations, and strategic recommendations. Findings were synthesized narratively and organized to support forward-looking system design. Results: Smart tibial implants have demonstrated feasibility in both bench and early clinical settings. Key advances include pressure-sensing intraoperative tools, inertial measurement units for remote gait tracking, and chemical biosensors for infection surveillance. However, the field remains limited by biological encapsulation, signal degradation, regulatory uncertainty, and data privacy challenges. Interdisciplinary design, standardized testing, translational funding, and ethical oversight are essential to scaling these innovations. Conclusions: Biosensor-enabled tibial components represent a promising convergence of orthopedics, electronics, and data science. By addressing the technological, biological, regulatory, and ethical gaps outlined herein, this field can transition from prototype to widespread clinical reality—offering new precision in arthroplasty care. Full article

Urban rivers, as crucial components of ecosystems, serve multiple functions, including flood control, drainage, and landscape services. However, with the acceleration of urbanization, factors such as industrial wastewater discharge, domestic sewage leakage, and surface runoff pollution have led to increasingly severe degradation of water quality in urban rivers. Unmanned aerial vehicle (UAV) remote sensing technology, with its sub-meter spatial resolution and operational flexibility, demonstrates significant advantages in the detailed monitoring of complex urban water systems. This study proposes an Ensemble Self-Attention Enhanced Mixture Density Network (ESA-MDN), which integrate an ensemble learning framework with a mixture density network and incorporates a self-attention mechanism for feature enhancement. This approach better captures the nonlinear relationships between water quality parameters and remote sensing features, achieving high-precision modeling of water quality parameter distributions. The resulting spatiotemporal distribution maps provide valuable support for pollution source identification and management decision making. The model successfully retrieved five water quality parameters, Chl-a, TSS, COD, TP, and DO, and validation metrics such as R², RMSE, MAE, MSE, MAPE, bias, and slope were utilized. Key metrics for the ESA-MDN test set were as follows: Chl-a (R² = 0.98, RMSE = 0.31), TSS (R² = 0.93, RMSE = 0.27), COD (R² = 0.93, RMSE = 0.39), TP (R² = 0.99, RMSE = 0.02), and DO (R² = 0.88, RMSE = 0.1). The results indicated that ESA-MDN can effectively extract water quality parameters from multispectral remote sensing data, with the generated spatiotemporal water quality distribution maps providing crucial support for pollution source identification and emergency response decision making. Full article

Soil condition represents a critical factor for ensuring sustainable agricultural development and food security. In this study, we examined the content of key soil properties and their patterns using an interpretable machine learning framework in combination with remote sensing data (Sentinel-2A) across several land use types in northwestern Russia. The analyzed soil properties in 64 samples included soil organic carbon (Corg), total nitrogen (N), mobile phosphorus (Pmob), total phosphorus (Ptot), and mobile potassium (Kmob) sampled across three land use types: cropland, hayfield, and forest. For machine learning interpretability, model-agnostic methods were utilized, including permutation and SHapley Additive exPlanations (SHAP) with spatial visualization. Our results revealed the highest concentrations of Corg (6.1 ± 4.3%), Kmob (78.3 ± 42.1%), and N (31.2 ± 14.5 mg/100 g) in forested areas, while both types of phosphorus (Ptot and Pmob) peaked in croplands (0.075 ± 0.024 and 0.023 ± 0.015%, respectively). The lowest values of Corg were observed in hayfields, and the lowest values of Kmob and N in croplands. Model validation demonstrated that Corg and N were predicted most accurately (R² = 0.53 and 0.55, respectively), where SWIR bands from Sentinel-2A satellite imagery were key predictors. The generated soil property maps and spatial SHAP values clearly showed distinct patterns correlated with land use types due to distinct biogeochemical processes across landscapes. Our findings demonstrate how land management practices fundamentally alter soil parameters, creating diagnostic spectral signatures that can be captured through interpretable machine learning and remote sensing. Full article

Assessing the impact of land use and land cover (LULC) on water quality (WQ) is central to land-based environmental research. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this study analyses recent trends using LULC maps to assess stream WQ within buffers, focusing on papers published between 2020 and 2024. It identifies relevant remote sensing practices for LULC mapping, landscape metrics, WQ physicochemical parameters, statistical techniques for correlating LULC and WQ, and conventions for configuring buffers. Materials include Scopus, Web of Science, and Atlas.ti, which support both qualitative data analysis and Conversational Artificial Intelligence (CAI) tasks via its integration with OpenAI’s large language models. The methodology highlights creating a bibliographic database, coding, CAI, and validating prompts. Official maps and visual or digital interpretations of optical imagery provided inputs for LULC. Classifiers from earlier generations have shaped LULC cartography. The most employed WQ parameters were phosphorus, total nitrogen, and pH. The three most referenced landscape metrics were the Largest Patch Index, Patch Density, and Landscape Shape Index. The literature mainly relied on Redundancy Analysis, Principal Component Analysis, and alternative correlation approaches. Buffer configurations varied in size. CAI facilitated an agile systematic review; however, it encountered challenges related to a phenomenon known as hallucination, which hampers its optimal performance. Full article

Arctic warming is expected to alter permafrost landscapes and shift tundra ecosystems from greenhouse gas sinks to sources. We quantified plant biomass and necromass, carbon stocks, and microbial activity across five Low-Arctic tundra sites in the Yana–Indigirka Lowland (Chokurdakh, NE Siberia) during the 2024 growing season. Above- and below-ground plant biomass was measured by harvest adjacent to 50 × 50 m permanent plots; total C and N were determined by dry combustion on an elemental analyzer. Total organic carbon (TOC) stocks were calculated by horizon from TOC (%), bulk density, and thickness. Microbial basal respiration (BR), substrate-induced respiration (SIR), microbial biomass C (MBC), and the metabolic quotient (qCO₂) were assessed in litter/organic (O), peat (T), and mineral gley horizons. Mean above-ground biomass was 15.8 ± 1.5 t ha⁻¹; total living biomass averaged 43.1 ± 1.6 t ha⁻¹. Below-ground biomass exceeded above-ground by 1.73×. Carbon in above-ground, below-ground, and necromass pools averaged 7.8, 12.2, and 12.5 t C ha⁻¹, respectively. Surface organic horizons dominated ecosystem C storage: litter–peat stocks ranged from 234 to 449 t C ha⁻¹, whereas 0–30 cm mineral layers held 18–50 t C ha⁻¹; total (surface + 0–30 cm) stocks spanned 258–511 t C ha⁻¹ among sites. Key contributors to biomass and C storage were deciduous shrubs (Salix pulchra, Betula nana), bryophytes (notably Aulacomnium palustre), and the graminoids (Eriophorum vaginatum). BR and MBC were highest in O and T horizons (BR up to 21.9 μg C g⁻¹ h⁻¹; MBC up to 70,628 μg C g⁻¹) and declined sharply in mineral soil; qCO₂ decreased from O to mineral horizons, indicating more efficient C use at depth. These in situ data show that Low-Arctic tundra C stocks are concentrated in surface organic layers while microbial communities remain responsive to warming, implying high sensitivity of carbon turnover to thaw and hydrologic change. The dataset supports model parameterization and remote sensing of shrub–tussock tundra carbon dynamics. Full article

Accurate classification of tree species is crucial for forest management and biodiversity conservation. Remote sensing technology offers a unique capability for classifying and mapping trees across large areas; however, the accuracy of extracting and identifying individual trees remains challenging due to the limitations of available imagery and phenological variations. This study presents a novel integrated machine learning (ML) and Geographic Object-Based Image Analysis (GEOBIA) framework to enhance tree species classification in a botanical garden using multi-temporal unmanned aerial vehicle (UAV) imagery. High-resolution UAV imagery (2.3 cm/pixel) was acquired across four different seasons (summer, autumn, winter, and early spring) to incorporate the phenological changes. Spectral, textural, geometrical, and canopy height features were extracted using GEOBIA and then evaluated with four ML models (Random Forest (RF), Extra Trees (ET), eXtreme gradient boost (XGBoost), and Support Vector Machine (SVM)). Multi-temporal data significantly outperformed single-date imagery, with RF achieving the highest overall accuracy (86%, F1-score 0.85, kappa 0.83) compared to 57–75% for single-date classifications. Canopy height and textural features were dominant for species identification, indicating the importance of structural variations. Despite the limitations of moderate sample size and a controlled botanical garden setting, this approach offers a robust framework for forest and urban landscape managers as well as remote sensing professionals, by optimizing UAV-based strategies for precise tree species identification and mapping to support urban and natural forest conservation. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

In regional traditional landscape studies where continuous literature and physical relics are scarce, image-based materials serve as a crucial medium for reconstructing historical spatial structures. This study focuses on the sandbar polder landscapes in the Yangzhong area, located in the lower Yangtze River. By integrating historical maps, military cartographic surveys, CORONA satellite imagery, and modern remote sensing data, this study developed a multi-source image interpretation framework to reconstruct the traditional dike–water–field–settlement spatial structure. Employing image recognition and morphological analysis, the study extracted features such as dikes, water systems, and settlements, revealing their adaptation mechanisms to microtopography and associated ecological functions, including multi-level irrigation and drainage, hydrological buffering, and flood prevention. The results demonstrate that traditional sandbar polder landscapes exhibit a high degree of experiential adaptation, and their spatial organization offers valuable insights for future green infrastructure planning. The study confirms the applicability of image-based interpretation methods for historical landscape reconstruction and provides a practical path for the activation and translation of traditional landscape units in contemporary urban–rural governance. Full article