Search Results (222)

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Coastal wetlands are critical ecosystems that require effective monitoring to support conservation and restoration efforts. This study evaluates the use of small unmanned aerial systems (sUAS) and multispectral imagery to estimate aboveground biomass (AGB) in tidal marshes, comparing models calibrated with destructive versus non-destructive in situ sampling methods. Imagery was collected over South Carolina’s North Inlet-Winyah Bay National Estuarine Research Reserve, and vegetation indices (VIs) were derived from sUAS imagery to model biomass. Stepwise linear regression was used to develop and validate models based on both sampling approaches. Destructive sampling models, particularly those using the Normalized Difference Vegetation Index (NDVI) and Difference Vegetation Index (DVI), achieved the lowest root mean square error (RMSE) values (as low as 70.91 g/m²), indicating higher predictive accuracy. Non-destructive models, while less accurate (minimum RMSE of 214.86 g/m²), demonstrated higher R² values (0.44 and 0.61), suggesting the potential for broader application with further refinement. These findings highlight the trade-offs between ecological impact and model performance, and support the viability of non-destructive methods for biomass estimation in sensitive wetland environments. Future work should explore machine learning approaches and improved temporal alignment of data collection to enhance model robustness. Full article

Accurate and rapid delineation of wildfire-affected areas is essential in the era of climate-driven increases in fire frequency. This study compares and analyzes four techniques for identifying wildfire-affected areas using Sentinel-2 satellite imagery: (1) calibrated differenced Normalized Burn Ratio (dNBR); (2) differenced NDVI (dNDVI) with empirically defined thresholds (0.04–0.18); (3) supervised SVM classifiers applying linear, polynomial, and RBF kernels; and (4) unsupervised ISODATA clustering. In particular, this study proposes an SVM-based classification method that goes beyond conventional index- and threshold-based approaches by directly using the SWIR, NIR, and RED band values of Sentinel-2 as input variables. It also examines the potential of the ISODATA method, which can rapidly classify affected areas without a training process and further assess burn severity through a two-step clustering procedure. The experimental results showed that SVM was able to effectively identify affected areas using only post-fire imagery, and that ISODATA enabled fast classification and severity analysis without training data. This study performed a wildfire damage analysis through a comparison of various techniques and presents a data-driven framework that can be utilized in future wildfire response and policy-oriented recovery support. Full article

Integrating remote-controlled (RC) helicopters and drones equipped with variable-rate sprayer systems represents a significant advancement in agricultural practices, particularly for the precise management of crop diseases. This study utilizes low-altitude remote sensing platforms to monitor crop growth and disease infestation, proposing advanced technology for managing and monitoring disease outbreaks in cassava fields. The performance of these systems was evaluated using statistical analysis and Geographic Information System (GIS) applications for mapping, with a particular emphasis on the relationship between vegetation indices (NDVI and GNDVI) and the growth stages of cassava. The results indicated that NDVI values obtained from both the RC helicopter and drone systems decreased with increasing altitude. The RC helicopter system exhibited NDVI values ranging from 0.709 to 0.352, while the drone system showed values from 0.726 to 0.361. Based on the relationship between NDVI and GNDVI of cassava plants at different growth stages, the study recommends a variable-rate spray system that utilizes standard instruments to measure chlorophyll levels. Furthermore, the study found that the RC helicopter system effectively measured chlorophyll levels, while the drone system demonstrated superior overall quality. Both systems showed strong correlations between NDVI/GNDVI values and cassava health, which has significant implications for disease management. The image processing algorithms and calibration methods used were deemed acceptable, with drones equipped with variable-rate sprayer systems outperforming RC helicopters in overall quality. These findings support the adoption of advanced remote sensing and spraying technologies in precision agriculture, particularly to enhance the management of cassava crops. Full article

A semi-empirical method for estimating actual evapotranspiration (ETa) based on ancillary and NDVI data, named NDVI-Cws, is currently being refined for improved applicability to wetlands. The investigation, in particular, addresses the case of semi-natural ecosystems where the impact of meteorological water stress (WS) is limited by groundwater resources. To adapt to this situation, the application of the NDVI-Cws method is preceded by a calibration phase based on spatially enhanced Land Surface Analysis Satellite Application Facility (LSA SAF) evapotranspiration products. This calibration is currently performed in the main wetlands of Tuscany (Central Italy) identified in the Ramsar Convention. The calibrated NDVI-Cws version is then applied to all regional Ramsar areas, yielding outputs that are first examined all over Tuscany. Next, the model estimates are quantitatively assessed versus ETa observations taken in a forest and a grassland Ramsar site. The results of these independent tests show the improvement achieved by the calibration phase with respect to the original model version. This supports the potential of the refined NDVI-Cws method to yield reasonably accurate daily ETa estimates for wetlands at a spatial resolution that is mainly dependent on the NDVI data used. Full article

Prior research has shown that for specific periods, vegetation indices from PlanetScope and Sentinel-2 (used as a reference) must be aligned to benefit from the experience of Sentinel-2 and utilize techniques such as data fusion. Even during the worst-case scenario, it is possible through histogram matching to calibrate PlanetScope indices to achieve the same values as Sentinel-2 (useful also for proxy). Based on these findings, the authors examined the effectiveness of linear regression in aligning individual bands prior to computing indices to determine if the bands are shifted differently. The research was conducted on five important bands: Red, Green, Blue, NIR, and RedEdge. These bands allow for the computation of well-known vegetation indices like NDVI and NDRE, and soil indices like Iron Oxide Ratio and Coloration Index. Previous research showed that linear regression is not sufficient by itself to align indices in the worst-case scenario. However, this paper demonstrates its efficiency in achieving accurate band alignment. This finding highlights the importance of considering specific scaling requirements for bands obtained from different satellite sensors, such as PlanetScope and Sentinel-2. Contemporary images acquired by the two sensors during May and July demonstrated different behaviors in their bands; however, linear regression can align the datasets even during the problematic month of May. Full article

The integration of crop models and remote sensing data has become a useful method for monitoring crop growth status and crop yield based on data assimilation. The objective of this study was to use leaf area index (LAI) values and plant nitrogen accumulation (PNA) values generated from spectral indices to calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model using the Monte Carlo Markov Chain (MCMC) technique. The initial management parameters, including sowing date, sowing rate, and nitrogen rate, are recalibrated based on the relationship between the remote sensing state variables and the simulated state variables. This integrated technique was tested on independent datasets acquired from three rice field tests at the experimental site in Deqing, China. The results showed that the data assimilation method achieved the most accurate LAI (R² = 0.939 and RMSE = 0.74) and PNA (R² = 0.926 and RMSE = 7.3 kg/ha) estimations compared with the spectral index method. Average differences (RE, %) between the inverted initialized parameters and the original input parameters for sowing date, seeding rate, and nitrogen amount were 1.33%, 4.75%, and 8.16%, respectively. The estimated yield was in good agreement with the measured yield (R² = 0.79 and RMSE = 661 kg/ha). The average root mean square deviation (RMSD) for the simulated values of yield was 745 kg/ha. Yield uncertainty from data assimilation between crop models and remote sensing was quantified. This study found that data assimilation of crop models and remote sensing data using the MCMC technique could improve the estimation of rice leaf area index (LAI), plant nitrogen accumulation (PNA), and yield. Data assimilation using the MCMC technique improves the prediction of LAI, PNA, and yield by solving the saturation effect of the normalized difference vegetation index (NDVI). This method proposed in this study can provide precise decision-making support for field management and anticipate regional yield fluctuations in advance. Full article

Reed beds, often referred to as dense, nearly monotonous extensive stands of common reed (Phragmites australis), are the most productive vegetation form of inland waters in Central Asia and exhibit great potential for biomass production in such a dryland setting. With its vast delta regions, Kazakhstan has the most extensive reed stands globally, providing a valuable case for studying the potential of reed beds for the bioeconomy. However, accurate and up-to-date figures on available reed biomass remain poorly documented due to data inadequacies in national statistics and challenges in measuring and monitoring it over large and remote areas. To address this gap in knowledge, in this study, the biomass resource characteristics of common reed were estimated for one of the significant reed bed areas of Kazakhstan, the Syr Darya Delta, using ground-truth field-sampled data as the dependent variable and high-resolution Sentinel-2 spectral bands and computed spectral indices as independent variables in multiple Random Forest (RF) regression models. An analysis of the spatially detailed yield map obtained for Phragmites australis-dominated wetlands revealed an area of 58,935 ha under dense non-submerged and submerged reed beds (with a standing biomass of >10.5 t ha⁻¹) and an estimated 1,240,789 tons of reed biomass resources within the Syr Darya Delta wetlands. Our findings indicate that submerged dense reed exhibited the highest biomass at 28.21 t ha⁻¹, followed by dense non-submerged reed at 15.24 t ha⁻¹ and open reed at 4.36 t ha⁻¹. The RF regression models demonstrated robust performance during both calibration and validation phases, as evaluated by statistical accuracy metrics using ten-fold cross-validation. Out of the 48 RF models developed, those utilizing the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) as key predictors yielded the best standing reed biomass estimation results, achieving a predictive accuracy of R² = 0.93, Root Mean Square Error (RMSE) = 2.74 t ha⁻¹ during the calibration, and R² = 0.83, RMSE = 3.71 t ha⁻¹ in the validation, respectively. This study highlights the considerable biomass potential of reed in the region’s wetlands and demonstrates the effectiveness of the RF regression modeling and high-resolution Sentinel-2 data for mapping and quantifying above-ground and above-water biomass of Phragmites australis-dominated wetlands over a large extent. The results provide critical insights for managing and conserving wetland ecosystems and facilitate the sustainable use of Phragmites australis resources in the region. Full article

Accurate estimations of forest total carbon storage are essential for understanding ecosystem functioning and improving forest management. This study investigates how multi-source remote sensing data can be used to provide accurate estimations of diameter at breast height (DBH) at the plot level, enhancing biomass estimations across 39.41 × 10⁴ km². The study is focused on Yunnan Province, China, which is characterized by complex terrain and diverse vegetation. Using ground-based survey data from hundreds of plots for model calibration and validation, the methodology combines multi-source remote sensing data, machine learning algorithms, and statistical analysis to develop models for estimating DBH distribution at regional scales. Decision tree showed the best overall performance. The model effectiveness improved when stratified by climatic zones, highlighting the importance of environmental context. Traditional methods based on the kNDVI index had a mean squared error (MSE) of 2575 t/ha and an R² value of 0.69. In contrast, combining model-estimated DBH values with remote sensing data resulted in a substantially lower MSE of 212 t/ha and a significantly improved R² value of 0.97. The results demonstrate that incorporating DBH not only reduced prediction errors but also improved the model’s ability to explain biomass variability. In addition, climatic region classification further increased model accuracy, suggesting that future efforts should consider environmental zoning. Our analyses indicate that water availability during cool and dry periods in this monsoon-influenced region was especially critical in influencing DBH across different subtropical zones. In summary, the study integrates DBH and high-resolution remote sensing data with advanced algorithms for accurate biomass estimation. The findings suggest that this approach can support regional forest management and contribute to research on carbon balance and ecosystem assessment. Full article

Sensing distance and speed have crucial effects on the data of active and passive sensors, providing valuable information relevant to crop growth monitoring and environmental conditions. The objective of this study was to evaluate the effects of sensing speed and sensor height on the variation in proximal canopy reflectance data to improve rice vegetation monitoring. Data were collected from a rice field using active and passive sensors with calibration procedures including downwelling light sensor (DLS) calibration, field of view (FOV) alignment, and radiometric calibration, which were conducted per official guidelines. The data were collected at six sensor heights (30–130 cm) and speeds (0–0.5 ms^–1). Analyses, including peak signal-to-noise ratio (PSNR) and normalized difference vegetation index (NDVI) calculations and statistical assessments, were conducted to explore the impacts of these parameters on reflectance data variation. PSNR analysis was performed on passive sensor image data to evaluate image data variation under varying data collection conditions. Statistical analysis was conducted to assess the effects of sensor speed and height on the NDVI derived from active and passive sensor data. The PSNR analysis confirmed that there were significant impacts on data variation for passive sensors, with the NIR and G bands showing higher noise sensitivity at increased speeds. The NDVI analysis showed consistent patterns at sensor heights of 70–110 cm and sensing speeds of 0–0.3 ms^–1. Increased sensing speeds (0.4–0.5 ms^–1) introduced motion-related variability, while lower heights (30–50 cm) heightened ground interference. An analysis of variance (ANOVA) indicated significant individual effects of speed and height on four spectral bands, red (R), green (G), blue (B), and near-infrared (NIR), in the passive sensor images, with non-significant interaction effects observed on the red edge (RE) band. The analysis revealed that sensing speed and sensor height influence NDVI reliability, with the configurations of 70–110 cm height and 0.1–0.3 ms^–1 speed ensuring the stability of NDVI measurements. This study notes the importance of optimizing sensor height and sensing speed for precise vegetation index calculations during field data acquisition for agricultural crop monitoring. Full article

Accurate estimation of herbage mass is crucial for managing pastoral livestock systems. The Normalized Difference Vegetation Index (NDVI) from Unmanned Aerial Vehicle (UAV) sensors shows promise for high-resolution estimations of pasture herbage mass, but it is still unknown how this method differs among forage species, seasons, and pasture management practices. A commercial sensor was calibrated to predict herbage mass using NDVI. Additionally, the effect of different forage species, days of regrowth, and nitrogen (N) status on the relationship between NDVI and herbage mass was evaluated. Two pastures of tall wheatgrass (Thinopyrum ponticum) and tall fescue (Festuca arundinacea), divided into 30 and 72 plots, respectively, were assessed during spring and autumn regrowth over two years in Balcarce, Argentina. Doses of 0, 50, and 100 kg N ha⁻¹ were applied to tall wheatgrass, and 0, 50, 100, 200, 400, and 600 kg N ha⁻¹ were applied to tall fescue to create variability in herbage mass and N status. Exponential regression models of herbage mass (y) fitted against NDVI (x) showed an average R² of 0.83 ± 0.04 and a mean absolute error of 170 ± 60 kg DM ha⁻¹. The relationship between NDVI and herbage mass differed (p ≤ 0.05) between species, seasons, and regrowth stage, but was not influenced by N status (p > 0.05). Results suggest that accurate predictions of herbage mass using NDVI measurements by an UAV require frequent model recalibrations to account for observed differences among forage species, days of regrowth, and years. Full article

Under the combined influence of climate change, accelerated urbanization, and inadequate urban flood defense standards, urban pluvial flooding has become an increasingly severe issue. This not only poses significant challenges to social stability and economic development but also makes accurate flood risk assessment crucial for improving urban flood control and drainage capabilities. This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. By constructing a hydrological–hydrodynamic coupled model using the SWMM and LISFLOOD-FP, this study evaluates the drainage capacity of the pipe network and surface inundation characteristics under both historical and design rainfall scenarios. An agent-based model (ABM) is developed to analyze the dynamic risks and vulnerabilities of population and building agents under different rainfall scenarios, capturing macroscopic emergent patterns from individual behavior rules and analyzing them in both time and space dimensions. Additionally, using multi-source remote sensing data, dynamic population vulnerability, and flood hazard processes, a quantitative dynamic flood risk analysis is conducted based on cloud models. The results demonstrated the following: (1) PSO performed best in calibrating the SWMM in the study area, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.93 to 0.69. (2) Drainage system capacity was low, with over 90% of the network exceeding capacity in scenarios with return periods of 1 to 100 years. (3) The vulnerability of people and buildings increased with higher flood intensity and duration. Most affected individuals were located on roads. In Event 6, 11.41% of buildings were at risk after 1440 min; in the 20-year flood event, 26.69% of buildings were at risk after 180 min. (4) Key features influencing vulnerability included the DEM, PND, NDVI, and slope. High-risk areas in the study area expanded from 36.54% at 30 min to 38.05% at 180 min. Full article

Grasslands are key components of land ecosystems, providing valuable ecosystem services and contributing to local carbon sequestration. Australian grasslands, covering approximately 70% of the continent, are vital for agriculture, pasture, and ecosystem services. Ongoing climate change introduces considerable uncertainties about the dynamic responses of different types of grasslands to changes in regional climate and its variation. This study, bringing together high-resolution meteorological data, calibrated long-term satellite NDVI data, and NPP and statistical models, investigated the spatiotemporal variability of NDVI and NPP and their predominant drivers (temperature and soil water content) across Australia’s grassland zones from 1992 to 2021. Results showed a slight, non-significant NDVI increase, primarily driven by improved vegetation in northern savannah grasslands (SGs). Areal average annual NPP values fluctuated annually but with a levelled trend over time, illustrating grassland resilience. NDVI and NPP measures aligned spatially, with values decreasing from the coastal to the inland regions and north to south. Most of the SGs experienced an increase in NDVI and NPP, boosted by abundant soil moisture and warm weather, which promoted vegetation growth and sustained a stable growing biomass in this zone. The increased NDVI and NPP in northern open grasslands (OGs) were linked to wetter conditions, while their decreases in western desert grasslands (DGs) were ascribed to warming and drier weather. Soil water availability was the dominant driver of grassland growth, with NDVI being positively correlated with soil water content but being negatively correlated with temperature across most grasslands. Projections under the SSP126 and SSP370 scenarios using ACCESS-ESM1.5 showed slight NPP increases by 2050 under warmer and wetter conditions, though western and southern grasslands may see declines in vegetation coverage and carbon storage. This study provides insights into the responses of Australian grasslands to climate variability. The results will help to underpin the design of sustainable grassland management strategies and practices under a changing climate for Australia. Full article

Orchards are complex agricultural systems with various characteristics that influence crop evapotranspiration (ET_c), such as variety, tree height, planting density, irrigation methods, and inter-row management. The preservation of biodiversity and improvement of soil fertility have become important goals in modern orchard management. Consequently, the traditional approach to weed control between rows, which relies on herbicides and soil mobilization, has gradually been replaced by the use of permanent living mulch (LM). This study explored the potential of a remote sensing (RS)-assisted method to monitor water use and water productivity in apple orchards with permanent mulch. The experimental data were obtained in the Lis Valley Irrigation District, on the Central Coast of Portugal, where the “Maçã de Alcobaça” (Alcobaça apple) is produced. The methodology was applied over three growing seasons (2019–2021), combining ground observations with RS tools, including drone flights and satellite images. The estimation of ET_a followed a modified version of the Food and Agriculture Organization of the United Nations (FAO) single crop coefficient approach, in which the crop coefficient (K_c) was derived from the normalized difference vegetation index (NDVI) calculated from satellite images and incorporated into a daily soil water balance. The average seasonal ET_a (FAO-56) was 824 ± 14 mm, and the water productivity (WP) was 3.99 ± 0.7 kg m⁻³. Good correlations were found between the K_c’s proposed by FAO and the NDVI evolution in the experimental plot, with an R² of 0.75 for the entire growing season. The results from the derived RS-assisted method were compared to the ET_a values obtained from the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) surface energy balance model, showing a root mean square (RMSE) of ±0.3 mm day⁻¹ and a low bias of 0.6 mm day⁻¹. This study provided insights into mulch management, including cutting intensity, and its role in maintaining the health of the main crop. RS data can be used in this management to adjust cutting schedules, determine K_c, and monitor canopy management practices such as pruning, health monitoring, and irrigation warnings. 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