Search Results (1,303)

The conflict between environmental conservation and agricultural production highlights the need for precise land use and land cover (LULC) mapping to support agro-environmental-related policies. Satellite image time series from the Moderate Resolution Image Spectroradiometer (MODIS) sensor are essential for current LULC mapping efforts. However, most approaches focus on pixel data, and studies exploring object-based spatiotemporal heterogeneity and correlation features in its time series are limited. The objective of this study is to mix the data cube architecture (analysis-ready data—ARD) and the geo-object-oriented time series segmentation via Geographic Object-Based Image Analysis (GEOBIA) to assess its performance in identifying natural vegetation and double-cropping practices over a crop season. The study area was the state of Mato Grosso, Brazil. Results indicate that, by combining GEOBIA and time series analysis (materialized by the multiresolution segmentation algorithm to derive spatiotemporal geo-objects of the MODIS data cube), representative training data collected after a quality control process, and the Support Vector Machine to classify the ARD, the overall accuracy was 0.95 and all users’ and producers’ accuracies were higher than 0.88. By considering the heterogeneity of Mato Grosso’s landscape, the results indicate the potential of the approach to provide accurate mapping. Full article

The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, water management, and ecological services. However, the response mechanisms and driving processes of WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, and land use/cover data, and the methods of Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, the Mann–Kendall Significant Test (M-K test), and Partial Correlation Analysis (PCA) methods. Our study revealed the spatio-temporal trend of WUE and its influencing mechanism in the Yellow River Basin (YRB) and compared the differences in WUE change before and after the implementation of the Returned Farmland to Forestry and Grassland Project in 2000. The results show that (1) the WUE of the YRB showed a significant increase trend at a rate of 0.56 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹ (p < 0.05) from 1982 to 2018. The area showing a significant increase in WUE (47.07%, Slope > 0, p < 0.05) was higher than the area with a significant decrease (14.64%, Slope < 0, p < 0.05). The region of significant increase in WUE in 2000–2018 (45.35%, Slope > 0, p < 0.05) was higher than that of 1982–2000 (8.23%, Slope > 0, p < 0.05), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC·kg⁻¹·H₂O) > Cropland WUE (0.972 gC·kg⁻¹·H₂O) > Grassland WUE (0.805 gC·kg⁻¹·H₂O) under different land cover types. Forest ecosystem WUE has the highest rate of increase (0.79 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹) from 2000 to 2018. Forest ecosystem WUE increased by 0.082 gC·kg⁻¹·H₂O after 2000. (3) precipitation (37.98%, R > 0, p < 0.05) and SM (10.30%, R > 0, p < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 70.39% of the WUE exhibited an increasing trend, which is mainly attributed to the simultaneous increase in GPP and ET, and the rate of increasing GPP is higher than the rate of increasing ET. This study could provide a scientific reference for policy decision-making on the terrestrial carbon cycle and biodiversity conservation. Full article

The integration of biogas and photovoltaic solar energy systems in sanitary landfills represents a promising strategy for sustainable energy generation and efficient urban waste management. This study evaluates the potential for biogas and photovoltaic energy production in two cells of the Municipal Landfill of Chihuahua, Mexico. Using the LandGEM and MMB models (Landfill Gas Emission Model and the Mexican Biogas Model), biogas generation was estimated by considering the composition of the landfill gas and the characteristics of the cover in each cell, revealing notable differences due to their operational periods and waste deposition. Photovoltaic simulations, conducted with the HelioScope software 2020, evaluated spatial configurations and solar radiation data. The generation potential for 2025 was simulated using predictive models, yielding results between 25.48 and 26.08 MW for the biogas–photovoltaic system, depending on the orientation of the panels and the optimization of the coverage. The novelty of this work lies in the combined evaluation of biogas and photovoltaic potential within a single landfill site, integrating advanced modeling tools to optimize system design. By demonstrating the feasibility and benefits of this hybrid system, the study contributes to clean energy solutions, environmental mitigation, and improved waste management strategies. Our findings emphasize the importance of site-specific management practices and predictive modeling to enhance renewable energy production and reduce greenhouse gas emissions, supporting sustainable urban development initiatives. Full article

Agricultural land resources are essential for food production, and thus it is vital to examine the spatiotemporal changes in these resources and their impacts on land suitability to optimize resource allocation. In this study, we investigated the spatial evolution of cropland resources through land use change analysis by utilizing four periods of land use data from 1990 to 2020 in the black soil region of northeast China (BSRNC). Employing niche theory, we developed a cultivability evaluation model tailored to the BSRNC, which was used to assess the impact of the spatial changes in cropland patterns over the past 30 years on land suitability. Our key findings are as follows: (1) Cropland resources have generally tended to expand in the BSRNC, with an increase of 7.16 × 10³ km² in the cultivated area and a northeastward shift in the cropland center by 52.94 km, indicating significant changes in the spatial configuration of the land. (2) The region’s cultivable land resources were substantial, covering 694.06 × 10³ km², or 55.78% of the total area, with notable spatial variability, influenced by the regional climate and topography. (3) The land cultivability has slightly improved, as shown by a 0.10 increase in the cultivability index, but a significant declining trend in the cultivability of cropland was observed after 2000. Our findings provide valuable insights to help accurately assess land productivity in the BSRNC and facilitate the sustainable use and conservation of black soil. Full article

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis. Full article

Global climate fluctuations pose challenges not only to natural environments but also to the conservation and transmission of human cultural and historical heritage. World Heritage Sites are pivotal regions for studying climate change impacts and devising adaptation strategies, with remote sensing technology showcasing significant utility in monitoring these impacts, especially in the Mediterranean region’s diverse and sensitive climate context. Although existing work has begun to explore the role of remote sensing in monitoring the effects of climate change, detailed analysis of the spatial distribution and temporal trends of landscape stability remains limited. Leveraging remote sensing data and its derived products, this study assessed climate change impacts on the Causses and Cévennes Heritage Site, a typical Mediterranean heritage landscape. Specifically, this study utilized remote sensing data to analyze the trends in various climatic factors from 1985 to 2020. The landscape stability model was developed utilizing land cover information and landscape indicators to explore the landscape stability and its distribution features within the study area. Finally, we adopted the Geographical Detector to quantify the extent to which climatic factors influence the landscape stability’s spatial distribution across different periods. The results demonstrated that (1) the climate showed a warming and drying pattern during the study period, with distinct climate characteristics in different zones. (2) The dominance of woodland decreased (area proportion dropped from 76% to 66.5%); transitions primarily occurred among woodland, cropland, shrubland, and grasslands; landscape fragmentation intensified; and development towards diversification and uniformity was observed. (3) Significant spatiotemporal differences in landscape stability within the heritage site were noted, with an overall downward trend. (4) Precipitation had a high contribution rate in factor detection, with the interactive enhancement effects between temperature and precipitation being the most prominent. The present study delivers a thorough examination of how climate change affects the Causses and Cévennes Heritage Landscape, reveals its vulnerabilities, and offers crucial information for sustainable conservation efforts. Moreover, the results offer guidance for the preservation of similar Mediterranean heritage sites and contribute to the advancement and deepening of global heritage conservation initiatives. Full article

The expansion of agricultural frontiers in the Amazon region poses a significant threat to forest conservation and biodiversity persistence. This study focuses on Pará state, Brazil, aiming to identify and characterize the predominant production systems by combining remote sensing data and landscape structure metrics. A rule-based classification tree algorithm is applied to classify hexagonal cells based on land cover, deforestation patterns, and distance from dairy facilities. The results reveal three dominant production systems: Natural Region, Non-Intensive Beef, and Initial Front, with livestock production being prominent. The analysis indicates that there is a correlation between the productive area and deforestation, emphasizing the role of agriculture as a driver of forest loss. Moreover, road networks significantly influence production system spatial distribution, highlighting the importance of infrastructure in land use dynamics. The Shannon diversity index reveals that areas with production systems exhibit greater diversity in land use and land cover classes, reflecting a wider range of modifications. In contrast, natural vegetation areas show lower Shannon values, suggesting that these areas are more intact and are less affected by human activities. These findings underscore the urgent need for sustainable development policies that will mitigate threats to forest resilience and biodiversity in Pará state. Full article

In this study, the global land surface albedo namely GAC43 was retrieved for the years 1979 to 2020 using Advanced Very High Resolution Radiometer (AVHRR) global area coverage (GAC) data onboard National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. We provide a comprehensive retrieval process of the GAC43 albedo, followed by a comprehensive assessment against in situ measurements and three widely used satellite-based albedo products, the third edition of the CM SAF cLoud, Albedo and surface RAdiation (CLARA-A3), the Copernicus Climate Change Service (C3S) albedo product, and MODIS BRDF/albedo product (MCD43). Our quantitative evaluations indicate that GAC43 demonstrates the best stability, with a linear trend of ±0.002 per decade at nearly all pseudo invariant calibration sites (PICS) from 1982 to 2020. In contrast, CLARA-A3 exhibits significant noise before the 2000s due to the limited availability of observations, while C3S shows substantial biases during the same period due to imperfect sensors intercalibrations. Extensive validation at globally distributed homogeneous sites shows that GAC43 has comparable accuracy to C3S, with an overall RMSE of approximately 0.03, but a smaller positive bias of 0.012. Comparatively, MCD43C3 shows the lowest RMSE (~0.023) and minimal bias, while CLARA-A3 displays the highest RMSE (~0.042) and bias (0.02). Furthermore, GAC43, CLARA-A3, and C3S exhibit overestimation in forests, with positive biases exceeding 0.023 and RMSEs of at least 0.028. In contrast, MCD43C3 shows negligible bias and a smaller RMSE of 0.015. For grasslands and shrublands, GAC43 and MCD43C3 demonstrate comparable estimation uncertainties of approximately 0.023, with close positive biases near 0.09, whereas C3S and CLARA-A3 exhibit higher RMSEs and biases exceeding 0.032 and 0.022, respectively. All four albedo products show significant RMSEs around 0.035 over croplands but achieve the highest estimation accuracy better than 0.020 over deserts. It is worth noting that significant biases are typically attributed to insufficient spatial representativeness of the measurement sites. Globally, GAC43 and C3S exhibit similar spatial distribution patterns across most land surface conditions, including an overestimation compared to MCD43C3 and an underestimation compared to CLARA-A3 in forested areas. In addition, GAC43, C3S, and CLARA-A3 estimate higher albedo values than MCD43C3 in low-vegetation regions, such as croplands, grasslands, savannas, and woody savannas. Besides the fact that the new GAC43 product shows the best stability covering the last 40 years, one has to consider the higher proportion of backup inversions before 2000. Overall, GAC43 offers a promising long-term and consistent albedo with good accuracy for future studies such as global climate change, energy balance, and land management policy. Full article

Accurate land cover mapping plays a critical role in enhancing our understanding of Earth’s energy balance, carbon cycle, and ecosystem dynamics. However, existing methods for producing multi-epoch land cover products still heavily depend on manual intervention, limiting their efficiency and scalability. This study introduces an automated approach for multi-epoch land cover mapping using remote sensing imagery and the model migration strategy. Landsat ETM+ and OLI images with a 30 m resolution were utilized as the primary data sources. An automatic training sample extraction method based on prior multi-source land cover products was first utilized. Then, based on the generated training dataset and a random forest classifier, local adaptive land cover classification models of the reference year were developed. Finally, by migrating the classification model to the target epoch, multi-epoch land cover products were generated. Yuli County in Xinjiang and Linxi County in Inner Mongolia were used as test cases. The classification models were first generated in 2020 and then migrated to 2010 to test the effectiveness of automated land cover classification over multiple years. Our mapping results show high accuracy in both regions, with Yuli County achieving 92.52% in 2020 and 88.33% in 2010, and Linxi County achieving 90.28% in 2020 and 85.28% in 2010. These results demonstrate the reliability of our proposed automated land cover mapping strategy. Additionally, the uncertainty analysis of the model migration strategy indicated that land cover types such as water bodies, wetlands, and impervious surfaces, which exhibit significant spectral changes over time, were the least suitable for model migration. Our results can offer valuable insights for medium-resolution, multi-epoch land cover mapping, which could facilitate more efficient and accurate environmental assessments. Full article

Vegetation phenology is an important indicator of climate change and ecosystem productivity. However, the monitoring of vegetation generative phenology through remote sensing techniques does not allow for species-specific retrieval in mixed ecosystems; hence, land surface phenology (LSP) is used instead of traditional plant phenology based on plant organ emergence and development observations. Despite the estimated timing of the LSP parameters being dependent on the vegetation index (VI) used, inadequate attention was paid to the evaluation of the commonly used VIs for LSP of different vegetation covers. We used two years of data from the experimental site in central European peatland, where plots of two peatland vegetation communities are under a climate manipulation experiment. We assessed the accuracy of LSP retrieval by simple remote sensing metrics against LSP derived from gross primary production and canopy chlorophyll content time series. The product of Near-Infrared Reflectance of Vegetation and Photosynthetically Active Radiation (NIRvP) and Green Chromatic Coordinates (GCC) was identified as the best-performing remote sensing metrics for peatland physiological and structural phenology, respectively. Our results suggest that the changes in the physiological phenology due to increased temperature are more prominent than the changes in the structural phenology. This may mean that despite a rather accurate assessment of the structural LSP of peatland by remote sensing, the changes in the functioning of the ecosystem can be underestimated by simple VIs. This ground-based phenological study on peatlands provides the base for more accurate monitoring of interannual variation of carbon sink strength through remote sensing. Full article

With environmental changes, sustaining watershed ecosystem services requires understanding community perceptions and preferences. Integrated approaches considering community perceptions, climate change, and land use cover change are crucial. We address a study gap by combining climate change and land use cover change data with an analysis of community perceptions to evaluate the watershed ecosystem services situation in Nepal’s Khageri Khola Watershed. Data from in-depth stakeholder interviews (n = 16), household perception surveys (n = 440), and participant observations (n = 5) were supplemented by meteorological and land use cover change data. Descriptive analysis, index value calculation, Spearman’s Rho correlation, and chi-square statistics were used to understand linkages between socio-demographics, climate change perceptions, watershed ecosystem services importance, and changes in watershed ecosystem services supply. The Mann–Kendall test, Sen’s slope calculation, and land use cover change analysis considered temperature, precipitation, and land use. Among watershed ecosystem services, communities prioritized drinking water as the most important and biodiversity support as the least important. Watershed ecosystem services exhibited decreasing trends, with soil fertility and productivity notably high (89%) and natural hazard control low (41%). Significant alignment existed between community perceptions and local climate indicators, unlike the incongruity found with land use cover changes, especially regarding water bodies. Socio-demographic factors influenced community perceptions. Policy recommendations include analyzing watershed-level community demand and preferences, integrating community perceptions with climate change and land use cover change data in decision making, engaging communities, equitable sharing of the benefits generated by watershed ecosystem services, and considering socio-demographic and topographic diversity in tailoring management strategies. Full article

This study was conducted in Sawena district, located in the Eastern Bale Zone of Ethiopia, with the aim of analyzing climate variability and identifying adaptation strategies. Secondary data covering the period from 1984 to 2023 were utilized, along with structured and unstructured questionnaires. Primary data were gathered from 350 pastoralist households across six kebeles through a household survey. This study used the Mann–Kendall test, Sen’s slope estimator, the coefficient of variation, descriptive statistics, and a multivariate probit model to analyze climate variability and adaptation strategies. The Mann–Kendall test, Sen’s slope estimator, and coefficient of variation analysis results showed significant rainfall increases in September, October, and November, with high winter variability and an upward autumn trend. Temperature analysis revealed consistent warming, with the greatest increases in September (0.049 °C/year) and summer (0.038 °C/year), and an annual mean rise of 0.034 °C per year, indicating climate shifts affecting pastoralist and agro-pastoral livelihood strategies and water resources that lead the area toward vulnerability. The descriptive results indicated that pastoralist households have adopted various adaptation strategies: 45.1% participate in seasonal livestock migration, 26.3% rely on productive safety net programs, 19% pursue livelihood diversification, and 9.7% engage in agroforestry. Multivariate analysis indicates that education, age, credit access, livestock ownership, asset value, and media exposure influence these strategies. The findings highlight the importance of policies to enhance climate resilience through diversification, sustainable land management, and improved access to resources like credit and markets, alongside strengthened education and targeted extension services. Full article

Indonesia is known for its incredible diversity of insects. Being ectothermic, insects are influenced by environmental factors. The relationship between insect diversity and the environment can be understood using multivariate analysis. The Paseh District in Sumedang Regency has various land uses, including gardens, rice fields, and plantations. Changes in land use due to the construction of the Cisumdawu Toll Road can impact environmental factors, such as soil quality, microclimate, and water availability, which are critical for sustaining diverse insect communities. Similarly, changes in vegetation cover can alter temperature and humidity levels, impacting terrestrial insects adapted to specific climatic conditions. This study aims to gather information on the relationship between insect diversity and environmental factors in different land use types in the Paseh District. A preliminary survey was carried out to record land use types and determine sampling locations. An intensive survey was done to collect and identify flying insect samples, as well as to measure the environmental factors. The results were analyzed using the Shannon–Wiener Diversity Index (H’), Evenness Index (E’), Simpson’s Diversity Index (C), and Canonical Correspondence Analysis (CCA). The study found 115 species of flying insects, with mixed gardens having the highest diversity. The CCA results showed that temperature strongly and positively correlated with insect diversity across all land uses, while wind speed correlated positively with insect diversity in gardens. Altitude correlated negatively with insect diversity in mixed gardens but positively in rice fields. Humidity had a strong positive correlation with insect diversity in other land uses. This research is important for understanding how land use types and environmental factors influence flying insect diversity, which is crucial for conserving biodiversity and maintaining essential ecosystem services such as pollination and pest control. Its impact lies in providing scientific data to guide sustainable land management practices, support agricultural productivity, and inform policies for biodiversity conservation in the Paseh District and similar regions. Full article

Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions. Full article

Soil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates soil salinization, driven by factors such as soil conditions, land use/land cover changes, and water deficits, over extensive spatial and temporal scales. Continuous monitoring of areas at risk of salinization plays a critical role in supporting effective land management and enhancing agricultural production. For these purposes, this work aims to propose a spatiotemporal method for monitoring soil salinization using spectral indices derived from Earth observation data. The proposed approach was tested in the Zaghouan Region in northeastern Tunisia, a region where soils are characterized by alarming levels of salinization. To address this concern, remote sensing techniques were applied for the analysis of satellite imagery generated from Landsat 5, Landsat 8, and Landsat 9 missions. A comprehensive field survey complemented this approach, involving the collection of 229 geo-referenced soil samples. These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R² of 0.67 and an RMSE of 0.12 dS/m. By 2023, 42.3% of the study area was classified as strongly or very strongly saline, indicating a significant increase in salinity over time. This rise in salinity corresponds to notable land use and land cover (LULC) changes, as 55.9% of the study area experienced LULC shifts between 2000 and 2023. A decline in vegetation cover coincided with increasing salinity, showing an inverse relationship between these factors. Additionally, the results highlight the complex interplay among these variables demonstrating that soil salinity levels are significantly impacted by climate change indicators, with a negative correlation between precipitation and salinity (r = −0.85, p < 0.001). Recognizing the interconnections between soil salinity, LULC changes, and climate variables is essential for developing comprehensive strategies, such as targeted irrigation practices and land suitability assessments. Earth observation and remote sensing play a critical role in enabling more sustainable and effective soil management in response to both human activities and climate-induced changes. Full article