Search Results (9,006)

Unmanned Aerial Vehicles (UAVs) are widely used in applications such as land assessment, surveillance, and rescue operations, where they are often required to cover multiple disjoint regions. Coverage Path Planning (CPP) aims to determine optimal paths for UAVs to cover these areas. While CPP for single regions has been extensively studied, multi-region CPP with multiple UAVs remains underexplored. Existing methods typically focus on minimizing path length, but often neglect the nonlinear variations in energy consumption during flight, limiting their practical applicability. This paper addresses the multi-UAV, multi-region CPP as a variant of the Vehicle Routing Problem (VRP) with energy estimation. We propose an approach that optimizes UAV flight speeds to minimize energy consumption, supported by an accurate energy estimation algorithm. In addition, a heuristic algorithm is developed to balance the distribution of tasks among UAVs, considering both the scanning and transit times. Experiments using real-world data from the Changsha urban area demonstrate that our approach outperforms state-of-the-art methods in computational efficiency and energy savings, highlighting its potential for practical UAV deployment. Full article

National parks play a vital role in safeguarding natural scenery, maintaining ecological integrity, and preserving cultural heritage, while simultaneously offering valuable opportunities for recreation and education. Among the diverse resources provided by national parks, visual landscape resources hold particular significance due to their capacity to inspire, educate, and enhance aesthetic appreciation. However, assessing and managing these resources remain challenging, as they span both the physical attributes of the landscape and the human visual perception process. This study aims to develop a theoretical and practical framework for evaluating the “landscape visual affordance” of national parks. Grounded in ecological psychology’s affordance theory, the proposed approach integrates physical affordance and sensory affordance, encompassing both the objective physical attributes of the landscape and the subjective processes of human perception. Drawing on a multi-dimensional set of indicators, the research quantifies physical features—such as topography, land use, vegetation cover, and landscape structure—as well as sensory dimensions, including visibility, visual prominence, and viewing frequency. These elements are synthesized into a landscape visibility assessment model built upon the affordance theory framework. The results demonstrate that landscape visual affordance effectively identifies landscape patches with varying degrees of visual quality and importance within national parks and other protected areas. By providing robust support for management decisions—such as zoned protection, optimizing recreational facilities, and evaluating visitor carrying capacity—this model offers new insights and practical guidance for the sustainable planning and management of landscapes in national parks and other ecologically critical regions. Full article

The rapid expansion of built-up areas (BUAs) requires effective spatial and temporal monitoring, being a crucial practice for urban land use planning, resource allocation, and environmental studies, and spectral indices (SIs) can provide efficiency and reliability in automating the process of BUAs extraction. This paper explores the use of nine spectral indices and sixteen thresholding methods for the automatic mapping of BUAs using Landsat 8 imagery from a semi-arid climate in Morocco during spring and summer. These indices are the Normalized Difference Built-Up Index (NDBI), the Vis-red-NIR Built-Up Index (VrNIR-BI), the Perpendicular Impervious Surface Index (PISI), the Combinational Biophysical Composition Index (CBCI), the Normalized Built-up Area Index (NBAI), the Built-Up Index (BUI), the Enhanced Normalized Difference Impervious Surfaces Index (ENDISI) and the Built-up Land Features Extraction Index (BLFEI). Results show that BLFEI, SWIRED, and BUI maintain high separability between built-up and each of the other land cover types across both seasons, as evaluated via the Spectral Discrimination Index (SDI). The lowest SDI values for all three indices were observed for bare soil against BUAs, with BLFEI recording 1.21 in the wet season and 1.05 in the dry season, SWIRED yielding 1.22 and 1.08, and BUI showing 1.21 and 1.08, demonstrating their robustness in distinguishing BUAs from other land covers under varying phenological and soil moisture conditions. These indices reached overall accuracies of $93.97\%$, $93.39\%$ and $92.81\%$, respectively, in wet conditions, and $91.57\%$, $89.17\%$ and $89.67\%$, respectively, in dry conditions. The assessment of thresholding methods reveals that the Minimum method resulted in the highest accuracies for these indices in wet conditions, where bimodal medium peaked histograms were observed, whereas the use of Li, Huang, Shanbhag, Otsu, K-means, or IsoData was found to be the most effective under dry conditions, where more peaked histograms were observed. Full article

Accurate and robust information on land cover changes in urban and coastal areas is essential for effective urban land management, ecosystem monitoring, and urban planning. This paper details the methodology and results of a pixel-level classification and change detection analysis, leveraging 1945 Royal Air Force (RAF) aerial imagery and 2011 Very High-Resolution (VHR) multispectral WorldView-2 satellite imagery from the broader area of Patras, Greece. Our attention is mainly focused on the changes in the coastline from the city of Patras to the northeast direction and the two major rivers, Charadros and Selemnos. The methodology involves preprocessing steps such as registration, denoising, and resolution adjustments to ensure computational feasibility for both coastal and riverbed change detection procedures while maintaining critical spatial features. For change detection at coastal areas over time, the Normalized Difference Water Index (NDWI) was applied to the new imagery to mask out the sea from the coastline and manually archive imagery from 1945. To determine the differences in the coastline between 1945 and 2011, we perform image differencing by subtracting the 1945 image from the 2011 image. This highlights the areas where changes have occurred over time. To conduct riverbed change detection, feature extraction using the Gray-Level Co-occurrence Matrix (GLCM) was applied to capture spatial characteristics. A Support Vector Machine (SVM) classification model was trained to distinguish river pixels from non-river pixels, enabling the identification of changes in riverbeds and achieving 92.6% and 92.5% accuracy for new and old imagery, respectively. Post-classification processing included classification maps to enhance the visualization of the detected changes. This approach highlights the potential of combining historical and modern imagery with supervised machine learning methods to effectively assess coastal erosion and riverbed alterations. Full article

The precise spatially explicit data on land cover and land use changes is one of the essential variables for enhancing the quantification of greenhouse gas emissions and removals, which is relevant for meeting the goal of the European economy and society to become climate-neutral by 2050. The accuracy of the machine learning models trained on remote-sensed data suffers from a lack of reliable training datasets and they are often site-specific. Therefore, in this study, we proposed a method that integrates the bi-temporal analysis of the combination of spectral indices that detects the potential changes, which then serve as reference data for the Random Forest classifier. In addition, we examined the transferability of the pre-trained model over time, which is an important aspect from the operational point of view and may significantly reduce the time required for the preparation of reliable and accurate training data. Two types of vegetation losses were identified: woody coverage converted to non-woody vegetation, and vegetated areas converted to sealed surfaces or bare soil. The vegetation losses were detected annually over the period 2018–2021 with an overall accuracy (OA) above 0.97 and a Kappa coefficient of 0.95 for all time intervals in the study regions in Poland and Norway. Additionally, the pre-trained model’s temporal transferability revealed an improvement of the OA by 5 percentage points and the macroF1-Score value by 12 percentage points compared to the original model. Full article

Recently, Time Series Remote Sensing Images (TSRSIs) have been proven to be a significant resource for land use/land cover (LULC) mapping. Deep learning methods perform well in managing and processing temporal dependencies and have shown remarkable advancements within this domain. Although deep learning methods have exhibited outstanding performance in classifying TSRSIs, they rely on enough labeled time series samples for effective training. Labeling data with a wide geographical range and a long time span is highly time-consuming and labor-intensive. Active learning (AL) is a promising method of selecting the most informative data for labeling to save human labeling efforts. It has been widely applied in the remote sensing community, except for the classification of TSRSIs. The main challenge of AL in TSRSI classification is dealing with the internal temporal dependencies within TSRSIs and evaluating the informativeness of unlabeled time series data. In this paper, we propose a data-driven active deep learning framework for TSRSI classification to address the problem of limited labeled time series samples. First, a temporal classifier for TSRSI classification tasks is designed. Next, we propose an effective active learning method to select informative time series samples for labeling, which considers representativeness and uncertainty. For representativeness, we use the K-shape method to cluster time series data. For uncertainty, we construct an auxiliary deep network to evaluate the uncertainty of unlabeled data. The features with rich temporal information in the classifier’s middle-hidden layers will be fed into the auxiliary deep network. Then, we define a new loss function with the aim of improving the deep model’s performance. Finally, the proposed method in this paper was verified on two TSRSI datasets. The results demonstrate a significant advantage of our method over other approaches to TSRSI. On the MUDS dataset, when the initial number of samples was 100 after our method selected and labeled 2000 samples, an accuracy improvement of 4.92% was achieved. On the DynamicEarthNet dataset, when the initial number of samples was 1000 after our method selected and labeled 2000 samples, an accuracy improvement of 7.81% was attained. On the PASTIS dataset, when the initial number of samples was 1000 after our method selected and labeled 2000 samples, an accuracy improvement of 4.89% was achieved. Our code is available in Data Availability Statement. Full article

Drylands cover a vast area, and biodiversity conservation in these regions represents a major challenge. A bibliometric study of published research highlighted several key aspects, including publication types, research fields, years of publication, contributing countries, institutions, languages, journals, publishers, authors, and frequently used keywords. The analysis also included plants related to biodiversity conservation in arid areas, animals related to biodiversity conservation in arid areas, and causes of biodiversity decline in arid regions, effects of biodiversity loss in these regions, and restoration methods aimed at improving biodiversity conservation in arid areas. A total of 947 publications were identified, starting from 1994, authored by researchers from 99 countries, primarily from Australia, the USA, China, Spain, and South Africa, and published in 345 journals, with the most prominent being Journal of Arid Environments, Biodiversity and Conservation, and Biological Conservation. The most commonly appearing keywords included biodiversity, conservation, diversity, vegetation, and patterns, with recent years showing an increased use of terms related to the causes and effects of aridification: climate change, land use, and ecosystem services. The causes of biodiversity loss in drylands are primarily linked to human activities and climatic changes, while the effects impact the entire ecosystem. Methods to improve biodiversity include traditional agroforestry systems, tree plantations and other plant species, grazing management, and other approaches. Combined actions among stakeholders and ecologically appropriate nature-based solutions are also recommended. Improvements in conservation biodiversity in arid areas are very important also for achieving the sustainability goals in these areas. However, numerous aspects of this topic remain to be studied in greater detail. Full article

Urbanization in Jaipur, India, has led to a 42% decline in green cover over the past two decades, exacerbating urban heat, air pollution, groundwater depletion, and reduced livability. Green Infrastructure (GI) offers a sustainable solution, but effective implementation requires robust, data-driven strategies. This study employs geospatial technologies—GIS, remote sensing, and Multi-Criteria Decision Analysis (MCDA)—to develop a suitability model for Urban Green Infrastructure (UGI) planning. Using an entropy-based weighting approach, the model integrates environmental factors, including the Normalized Difference Vegetation Index (NDVI), which fell by 18% between 2000 and 2020; Land Surface Temperature (LST), which increased by 1.8 °C; soil moisture; precipitation; slope; and land use/land cover (LULC). Proximity to water bodies was found to be a critical determinant of suitability, whereas land surface temperature and soil moisture played significant roles in determining UGI feasibility. The results were validated using NDVI trends and comparative analysis with prior studies so as to ensure accuracy and robustness. The suitability analysis reveals that 35% of Jaipur’s urban area, particularly peri-urban regions and river corridors, is highly suitable for UGI interventions, thereby presenting significant opportunities for urban cooling, flood mitigation, and enhanced ecosystem services. These findings align with India’s National Urban Policy Framework (2018) and the UN Sustainable Development Goal 11, supporting climate resilience and sustainable urban development. This geospatial approach provides a scalable methodology for integrating green spaces into urban planning frameworks across rapidly urbanizing cities. Full article

Understanding the impacts of land use/land cover (LULC) changes on ecological processes is essential for addressing biodiversity loss, habitat fragmentation, and climate change. This study analyzes the effects of LULC changes on habitat quality and landscape connectivity in İzmir, Turkey’s third-largest city, using the Integrated Valuation of Ecosystem Services and Trade-offs Habitat Quality (InVEST HQ) model, Conefor 2.6 connectivity analysis, and Circuitscape 4.0 resistance-based modeling. This study relies on Coordination of Information on the Environment (CORINE) Land Cover data from 1990 to 2018. Findings indicate that artificial surfaces increased by 82.5% (from 19,418 ha in 1990 to 35,443 ha in 2018), primarily replacing agricultural land (11,721 ha converted). Despite this expansion, high quality habitat areas remained relatively stable, though habitat fragmentation intensified, with the number of patches rising from 469 in 1990 to 606 in 2018, and the average patch size decreasing from 394.31 ha to 297.39 ha. Connectivity analysis highlighted Mount Nif and the Urla–Çeşme–Karaburun Peninsula as critical ecological corridors. However, resistance to movement increased, reducing the likelihood of connectivity-supporting corridors. These findings emphasize the importance of integrating spatial modeling approaches into urban planning and conservation strategies to mitigate future habitat loss and fragmentation. Full article

Global ecosystems, particularly in biodiversity-rich tropical rainforests, are increasingly under pressure from human activities. As socio-economic development continues and populations steadily grow, the effective planning of areas surrounding national parks has become a global challenge. This study, based on remote sensing data and utilizing landscape ecology tools, such as ArcGIS 10.8, GeoDa 1.20, and Fragstats 4.2, combines spatial statistical methods, trend analysis, and the Hurst index to conduct a long-term analysis and forecast future trends in vegetation ecological quality indicators (VEQI) and landscape pattern changes within and around the Hainan Tropical Rainforest National Park. VEQI changes across various buffer zones were also assessed. Our results show that both arable and built-up land increased, especially from 2002 to 2022. Arable land decreased from 5566.8 km² to 4796.8 km², then increased to 5904.6 km²; built-up land expanded from 163.97 km² to 314.59 km², reflecting urbanization. Spatiotemporal analysis revealed that 42.54% of the study area experienced significant VEQI changes, with a 24.05% increase (mainly in the northwest) and an 18.49% decrease (mainly in the southeast). The VEQI improvements were consistent across all buffer zones, with the most significant growth in the 7.5 km zone. Landscape indices indicated high fragmentation in coastal areas, while inland areas remained stable, reflecting the tension between conservation and urbanization. These findings provide a theoretical basis for future ecological development and buffer zone policies in the park. Full article

With the rapid acceleration of global urbanization, the impact of land use/cover change (LUCC) on the environment and ecosystems has become increasingly prominent, particularly in terms of air quality, which has emerged as a significant issue demanding attention. Focusing on the coastal city of Lianyungang, the spatiotemporal dynamics of land use/cover changes were explored by utilizing land use dynamic degree and land use transfer matrix methods. By integrating a comprehensive historical dataset, multiple linear regression analysis was used to analyze the driving mechanism of land use conversion and to explore the effect of LUCC on the variations in PM₁₀ levels. The results showed an overall decreasing trend in PM₁₀ levels over the 24-year period from 2000 to 2023, with distinct seasonal fluctuations, showing higher concentrations in winter and lower concentrations in summer. The impact of land use on PM₁₀ variations can be categorized into three stages: initial (2000–2006), transitional (2007–2013), and deepening development (2014–2022). Notably, during the third stage, with the involvement of policy interventions and industrial upgrading, a strong negative correlation of −0.97 was identified between urban land expansion and the decrease in PM₁₀ levels. The correlation between LUCC and PM₁₀ levels was insignificant over shorter periods, but the analysis of data from 2000 to 2022 revealed a significant positive correlation of 0.77, emphasizing the importance of adopting a long-term perspective to accurately assess the impact of LUCC on air quality. This research provides valuable insights into the implications of LUCC on air quality during urbanization and establishes a scientific foundation for developing air pollution management strategies in Lianyungang and similar regions. Full article

Understanding how climate policies impact forest carbon sequestration is crucial for optimizing mitigation strategies. This study evaluated forest carbon sequestration in China from 2020 to 2060 under three climate scenarios: SSP1-2.6 (high mitigation), SSP3-7.0 (limited mitigation), and SSP5-8.5 (no mitigation). We integrated the land-use harmonization (LUH2) and patch-generating land-use simulation (PLUS) models to project forest cover change, and the Lund–Potsdam–Jena managed land (LPJmL) model to simulate carbon dynamics. The results showed stronger mitigation efforts led to higher sequestration, with annual rates of 0.49, 0.48, and 0.20 Pg yr⁻¹ across the scenarios. SSP1-2.6 achieved the highest carbon density (17.75 kg m⁻²) and sequestration (56.95 Pg), driven by the greatest increases in the carbon density of existing forests (+41.56%) and soil carbon (+39.94%). SSP3-7.0, despite the highest forest cover (34.74%), had a lower carbon density (17.19 kg m⁻²) and sequestration (56.84 Pg). SSP5-8.5 recorded the lowest forest cover (27.12%) and sequestration (45.62 Pg). Increasing carbon density, rather than expanding forest area, could be more effective for carbon sequestration in China. The carbon density and annual sequestration in existing forests were 2.36 and 2.89 times higher than in new forests. We recommend prioritizing SSP1-2.6 to maximize sequestration, focusing on protecting southwest forests and soil carbon. Full article

Accurate, cost-efficient vegetation mapping is critical for managing afforestation projects, particularly in resource-limited areas. This study used a consumer-grade RGB unmanned aerial vehicle (UAV) to evaluate the optimal spatial and temporal resolutions (leaf-off and leaf-on) for precise, economically viable tree species mapping. This study conducted in 2024 in Kasho, Bannu district, Pakistan, using UAV missions at multiple altitudes captured high-resolution RGB imagery (2, 4, and 6 cm) across three sampling plots. A Support Vector Machine (SVM) classifier with 5-fold cross-validation was assessed using accuracy, Shannon entropy, and cost–benefit analyses. The results showed that the 6 cm resolution achieved a reliable accuracy (R² = 0.92–0.98) with broader coverage (12.3–22.2 hectares), while the 2 cm and 4 cm resolutions offered higher accuracy (R² = 0.96–0.99) but limited coverage (4.8–14.2 hectares). The 6 cm resolution also yielded the highest benefit–cost ratio (BCR: 0.011–0.015), balancing cost-efficiency and accuracy. This study demonstrates the potential of consumer-grade UAVs for affordable, high-precision tree species mapping, while also accounting for other land cover types such as bare earth and water, supporting budget-constrained afforestation efforts. Full article

Agriculture in the Indian Sundarbans deltaic region primarily depends on a rice-based monocropping system during the rainy season, with the subsequent season often remaining fallow. To mitigate this issue, a series of experiments using zero tillage and straw mulching (ZTSM) potato cultivation were conducted over eight consecutive years (2017–2024) across various islands in the Sundarbans Delta, West Bengal, aimed to intensify the cropping system and ensure the betterment of the land use pattern using climate-smart agricultural practices. In the initial two years, the experiments concentrated on assessing different potato cultivars and nutrient dosages under zero tillage and paddy straw mulching conditions. During the subsequent years, the focus shifted to field demonstrations under diverse climatic conditions. The research included the application of different macronutrients and growth regulators, in combination with different depths of straw mulching. In the final years of the study, the intervention was dedicated solely to the horizontal expansion of cultivated land. These initiatives aimed to enhance agricultural productivity and sustainable land use in the polders, promoting climate-resilient farming practices. From the sets of experiments, we standardized the sustainable nutrient management strategies and selection of appropriate potato cultivars vis-à-vis depth of straw mulching and, finally, the overall best agronomic practices for the region. The adoption of the ZTSM potato cultivation system demonstrated considerable success, as evidenced by the remarkable increase in the number of farmers employing this sustainable agricultural practice. The number of farmers practicing zero tillage potato cultivation surged from 23 in the initial year to over 1100, covering an area of more than 15 ha, highlighting the effectiveness of the technology. The analysis of the estimated adoption also showed that more than 90% adoption is likely to be achieved within a decade. This potential expansion underscores the benefits of the ZTSM potato cultivation system in improving soil health, conserving water, and reducing labour and costs. As more farmers recognize the advantages of zero tillage potato mulching, this approach is poised to play a pivotal role in sustainable agriculture, enhancing productivity while promoting environmental stewardship. Full article

Land-Cover and Land-Use Change (LCLUC) is a dynamic process affected by the combination and mutual interaction of climatic and socioeconomic drivers. Field studies and surveys, which are typically time- and resource-consuming, have been employed by researchers to better understand LCLUC drivers. However, remotely sensed data may provide the same trustworthy outcomes with less time and expense. This study aimed to assess the relationship between LCLUC and changes in socioeconomic and climatic factors in the Dallas-Fort Worth (DFW) metropolitan area, Texas, USA, between 2000 and 2020. The LCLU, socioeconomic, and climatic data were obtained from the National Land Cover Database of Multi-Resolution Land Characteristics Consortium, NASA’s Socioeconomic Data and Applications Center (SEDAC), and the global climate and weather data website (WorldClim), respectively. Change detection calculated from these data was used to analyze spatial and statistical relationships between LCLUC and changes in socioeconomic and climatic factors. Results showed that LCLUC was significantly predicted by population change, housing and transportation, household and disability change, socioeconomic status change, monthly average minimum temperature change, and monthly mean precipitation change. While socioeconomic factors played a predominant role in driving LCLUC in this study, the influence of climatic factors should not be overlooked, particularly in regions where climate sensitivity is more pronounced, such as arid or transitional zones. These findings highlight the importance of considering regional variability when assessing LCLUC drivers. Full article