Search Results (767)

Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring the global carbon cycle. However, accurate estimation of forest AGB remains a significant challenge, especially when integrating multi-source remote sensing data, and the effects of different feature combinations for AGB estimation results are unclear. In this study, we proposed a method for estimating forest AGB by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), and the Advanced Land Observing Satellite digital elevation model (ALOS DEM), and field survey data. The continuous tree height (TH) feature was derived using GF-7 stereo imagery and the ALOS DEM. Spectral features were extracted from S1 and S2, and topographic features were extracted from the ALOS DEM. Using these features, 15 feature combinations were constructed. The recursive feature elimination (RFE) method was used to optimize each feature combination, which was then input into the extreme gradient boosting (XGBoost) model for AGB estimation. Different combinations of features used to estimate forest AGB were compared. The best model was selected for mapping AGB distribution at 30 m resolution. The outcomes showed that the forest AGB model was composed of 13 features, including TH, topographic, and spectral features extracted from S1 and S2 data. This model achieved the best prediction performance, with a determination coefficient (R²) of 0.71 and a root mean square error (RMSE) of 18.11 Mg/ha. TH was found to be the most important predictive feature, followed by S2 optical features, topographic features, and S1 radar features. Full article

Lakes are critical resources that support the ecological balance and provide essential services for human and environmental well-being. However, their quality is being increasingly threatened by both natural and anthropogenic processes. This study aimed to assess the water quality and the presence of heavy metals in 15 lakes in the Vidarbha and Marathwada regions of Maharashtra, India. To understand the extent of pollution and its sources, the physico-chemical parameters were analyzed which included pH, turbidity, total hardness, orthophosphate, residual free chlorine, chloride, fluoride, and nitrate, as well as heavy metals such as iron, lead, zinc, copper, arsenic, chromium, manganese, cadmium, and nickel. The results revealed significant pollution in several lakes, with the Lonar Lake showing a pH value of 12, exceeding the Bureau of Indian Standards’ (BIS) limit. The Lonar Lake also showed elevated levels of fluoride having a value of 2 mg/L, nitrate showing a value of 45 mg/L, and orthophosphate showing a concentration up to 2 mg/L. The Rishi Lake had higher concentrations of nickel having a value of 0.2 mg/L and manganese having a value of 0.7 mg/L, crossing permissible BIS limits. The Rishi Lake and the Salim Ali Lake exhibited higher copper levels than other lakes. Cadmium was detected in most of the lakes ranging from values of 0.1 mg/L to 0.4 mg/L, exceeding BIS limits. The highest turbidity levels were observed in Rishi Lake and Salim Ali Lake at 25 NTU. The total hardness value observed in the Kharpudi Lake was 400 mg/L, which is highest among all the lakes under study. The spatial analysis, which utilized remote sensing and GIS techniques, including Sentinel-2 multispectral imagery for land use and land cover mapping and Digital Elevation Model (DEM) for watershed delineation, provided insights into the topography and drainage patterns affecting these lakes. The findings emphasize the urgent need for targeted management strategies to mitigate pollution and protect these vital freshwater ecosystems, with broader implications for public health and ecological sustainability in regions reliant on these water resources. Full article

Bare soil (BS) identification through satellite remote sensing can potentially play a critical role in understanding and managing soil properties essential for climate regulation and ecosystem services. From 191 papers, this review synthesises advancements in BS detection methodologies, such as threshold masking and classification algorithms, while highlighting persistent challenges such as spectral confusion and inconsistent validation practices. The analysis reveals an increasing reliance on satellite data for applications such as digital soil mapping, land use monitoring, and environmental impact mapping. While multispectral sensors like Landsat and Sentinel dominate current methodologies, limitations remain in distinguishing BS from spectrally similar surfaces, such as crop residues and urban areas. This review emphasises the critical need for robust validation practices to ensure reliable estimates. By integrating technological advancements with improved methodologies, the potential for accurate, large-scale BS detection can significantly contribute to combating land degradation and supporting global food security and climate resilience efforts. Full article

The Albufera of Valencia (Spain) is a representative case of pressure on water quality, which caused the hypertrophic state of the lake to completely change the ecosystem that once featured crystal clear waters. PerúSAT-1 is the first Peruvian remote sensing satellite developed for natural disaster monitoring. Its high spatial resolution makes it an ideal sensor for capturing highly detailed products, which are useful for a variety of applications. The ability to change its acquisition geometry allows for an increase in revisit time. The main objective of this study is to assess the potential of PerúSAT-1′s multispectral images to develop multi-parameter algorithms to evaluate the ecological state of the Albufera lagoon. During five field campaigns, samples were taken, and measurements of ecological indicators (chlorophyll-a, Secchi disk depth, total suspended matter, and its organic-inorganic fraction) were made. All possible combinations of two bands were obtained and subsequently correlated with the biophysical variables by fitting a linear regression between the field data and the band combinations. The equations for estimating all the water variables result in the following R² values: 0.76 for chlorophyll-a (NRMSE: 16%), 0.75 for Secchi disk depth (NRMSE: 15%), 0.84 for total suspended matter (NRMSE: 11%), 0.76 for the inorganic fraction (NRMSE: 15%), and 0.87 for the organic fraction (NRMSE: 9%). Finally, the equations were applied to the Albufera lagoon images to obtain thematic maps for all variables. Full article

Uncrewed aerial vehicles (UAVs) have transformed remote sensing, offering unparalleled flexibility and spatial resolution across diverse applications. Many of these applications rely on mapping flights using snapshot imaging sensors for creating 3D models of the area or for generating orthomosaics from RGB, multispectral, hyperspectral, or thermal cameras. Based on a literature review, this paper provides comprehensive guidelines and best practices for executing such mapping flights. It addresses critical aspects of flight preparation and flight execution. Key considerations in flight preparation covered include sensor selection, flight height and GSD, flight speed, overlap settings, flight pattern, direction, and viewing angle; considerations in flight execution include on-site preparations (GCPs, camera settings, sensor calibration, and reference targets) as well as on-site conditions (weather conditions, time of the flights) to take into account. In all these steps, high-resolution and high-quality data acquisition needs to be balanced with feasibility constraints such as flight time, data volume, and post-flight processing time. For reflectance and thermal measurements, BRDF issues also influence the correct setting. The formulated guidelines are based on literature consensus. However, the paper also identifies knowledge gaps for mapping flight settings, particularly in viewing angle pattern, flight direction, and thermal imaging in general. The guidelines aim to advance the harmonization of UAV mapping practices, promoting reproducibility and enhanced data quality across diverse applications. Full article

Accurately determining the distribution and quantity of maize straw types is of great significance for evaluating the effectiveness of conservation tillage, precisely estimating straw resources, and predicting the risk of straw burning. The widespread adoption of conservation tillage technology has greatly increased the diversity and complexity of maize straw coverage in fields after harvest. To improve the precision and effectiveness of remote sensing recognition for maize straw types, a novel method was proposed. This method utilized unmanned aerial vehicle (UAV) multispectral imagery, integrated the Stacking Enhanced Straw Index (SESI) introduced in this study, and combined height, shape, and spectral characteristics to improve recognition accuracy. Using the original five-band multispectral imagery, a new nine-band image of the study area was constructed by integrating the calculated SESI, Canopy Height Model (CHM), Product Near-Infrared Straw Index (PNISI), and Normalized Difference Vegetation Index (NDVI) through band combination. An object-oriented classification method, utilizing a “two-step segmentation with multiple algorithms” strategy, was employed to integrate height, shape, and spectral features, enabling rapid and accurate mapping of maize straw types. The results showed that height information obtained from the CHM and spectral information derived from SESI were essential for accurately classifying maize straw types. Compared to traditional methods that relied solely on spectral information for recognition of maize straw types, the proposed approach achieved a significant improvement in overall classification accuracy, increasing it by 8.95% to reach 95.46%, with a kappa coefficient of 0.94. The remote sensing recognition methods and findings for maize straw types presented in this study can offer valuable information and technical support to agricultural departments, environmental protection agencies, and related enterprises. Full article

Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring is of paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function (SRF) and computer algorithms, to overcome the limitations of traditional methods. First, the equivalent remote sensing reflectance of Sentinel-2A was simulated by combining UAV hyperspectral images with ground experimental data. Then, using grey relational analysis (GRA) and the maximum information coefficient (MIC) algorithm, we explored the complex relationship between the vegetation indices (VIs) and LCC, and further selected feature variables. Meanwhile, we utilized three spectral indices (DSI, NDSI, RSI) to identify sensitive band combinations for LCC and further analyzed the response relationship of the original bands to LCC. On this basis, we selected three nonlinear machine learning models (XGBoost, RFR, SVR) and one multiple linear regression model (PLSR) to construct the LCC inversion model, and we chose the optimal model to generate spatial distribution maps of maize LCC at the regional scale. The results indicate that there is a significant nonlinear correlation between the VIs and LCC, with the XGBoost, RFR, and SVR models outperforming the PLSR model. Among them, the XGBoost_MIC model achieved the best LCC inversion results during the tasseling stage (VT) of maize growth. In the UAV hyperspectral data, the model achieved an R² = 0.962 and an RMSE = 5.590 mg/m² in the training set, and an R² = 0.582 and an RMSE = 6.019 mg/m² in the test set. For the Sentinel-2A-simulated spectral data, the training set had an R² = 0.923 and an RMSE = 8.097 mg/m², while the test set showed an R² = 0.837 and an RMSE = 3.250 mg/m², which indicates an improvement in test set accuracy. On a regional scale, the LCC inversion model also yielded good results (train R² = 0.76, test R² = 0.88, RMSE = 18.83 mg/m²). In conclusion, the method proposed in this study not only significantly improves the accuracy of traditional methods but also, with its outstanding versatility, can achieve rapid, non-destructive, and precise crop growth monitoring in different regions and for various crop types, demonstrating broad application prospects and significant practical value in precision agriculture. Full article

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R²) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R² = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R² = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future. Full article

Plant phenology is an important driver of inter-annual variability in peatland carbon uptake. However, the use of traditional phenology datasets (e.g., manual surveys, satellite remote sensing) to quantify this link is hampered by their limited spatial and temporal coverage. This study examined the use of phenology cameras (phenocams) and uncrewed aerial vehicles (UAVs) for monitoring phenology in a Scottish temperate peatland. Data were collected at the site over multiple growing seasons using a UAV platform fitted with a multispectral Parrot Sequoia camera. We found that greenness indices calculated using data from both platforms were in strong agreement with each other, and exhibited strong correlations with rates of gross primary production (GPP) at the site. Greenness maps generated with the UAV data were combined with fine-scale vegetation classifications, and highlighted the variable sensitivity of different plant species to dry spells over the study period. While a lack of suitable weather conditions for surveying limited the UAV data temporally, the phenocam provided a near-continuous record of phenology. The latter revealed substantial temporal variability in the relationship between canopy greenness and peatland GPP, which although strong over the growing season as a whole (r_s = 0.88, p < 0.01), was statistically insignificant during the peak growing season. Full article

Unmanned aerial vehicles (UAVs) equipped with multi-sensor remote sensing technologies provide an efficient approach for mapping spatial and temporal variations in vegetation traits, enabling advancements in precision monitoring and modeling. This study’s objective was to analyze UAV multiple sensors’ performance in monitoring winter wheat chlorophyll content (SPAD), plant nitrogen accumulation (PNA), and N nutrition index (NNI). A two-year field experiment with five N fertilizer treatments was carried out. The color indices (CIs, from RGB sensors), vegetation indices (VIs, from multispectral sensors), and temperature indices (TIs, from thermal sensors) were derived from the collected images. XGBoost (extreme gradient boosting) was applied to develop the models, using 2021 data for training and 2022 data for testing. The excess green minus excess red index, red green ratio index, and hue (from CIs), and green normalized difference vegetation index, normalized difference red-edge index, and normalized difference vegetation index (from VIs), showed high correlations with three N indicators. At the pre-heading stage, the best performing CIs correlated better than the VIs; this was reversed in the post-heading stage. CIs outperformed VIs in SPAD (CIs: R²(coefficient of determination) = 0.66, VIs: R² = 0.61), PNA (CIs: R² = 0.68, VIs: R² = 0.64), and NNI (CIs: R² = 0.64, VIs: R² = 0.60) in the pre-heading stage, whereas VI-based models achieved slightly higher accuracies in post-heading and all stages compared to CIs. Models built with CIs + VIs significantly improved the models’ performance compared to single-sensor models. Adding TIs to CIs and CIs + VIs further improved the models’ performance slightly, especially at the post-heading stage, resulting in the best model performance with three sensors. These findings highlight the effectiveness of UAV systems in estimating wheat N and establish a framework for integrating RGB, multispectral, and thermal sensors to enhance model accuracy in precision vegetation monitoring. Full article

In the past few years, deep learning has achieved remarkable advancements in the area of image compression. Remote sensing image compression networks focus on enhancing the similarity between the input and reconstructed images, effectively reducing the storage and bandwidth requirements for high-resolution remote sensing images. As the network’s effective receptive field (ERF) expands, it can capture more feature information across the remote sensing images, thereby reducing spatial redundancy and improving compression efficiency. However, the majority of these learned image compression (LIC) techniques are primarily CNN-based and transformer-based, often failing to balance the global ERF and computational complexity optimally. To alleviate this issue, we propose a learned remote sensing image compression network with visual state space model named VMIC to achieve a better trade-off between computational complexity and performance. Specifically, instead of stacking small convolution kernels or heavy self-attention mechanisms, we employ a 2D-bidirectional selective scan mechanism. Every element within the feature map aggregates data from multiple spatial positions, establishing a globally effective receptive field with linear computational complexity. We extend it to an omni-selective scan for the global-spatial correlations within our Channel and Global Context Entropy Model (CGCM), enabling the integration of spatial and channel priors to minimize redundancy across slices. Experimental results demonstrate that the proposed method achieves superior trade-off between rate-distortion performance and complexity. Furthermore, in comparison to traditional codecs and learned image compression algorithms, our model achieves BD-rate reductions of −4.48%, −9.80% over the state-of-the-art VTM on the AID and NWPU VHR-10 datasets, respectively, as well as −6.73% and −7.93% on the panchromatic and multispectral images of the WorldView-3 remote sensing dataset. Full article

Chlorophyll content is an essential parameter for evaluating the growth condition of winter wheat, and its accurate monitoring through remote sensing is of great significance for early warnings about winter wheat growth. In order to investigate unmanned aerial vehicle (UAV) multispectral technology’s capability to estimate the chlorophyll content of winter wheat, this study proposes a method for estimating the relative canopy chlorophyll content (RCCC) of winter wheat based on UAV multispectral images. Concretely, an M350RTK UAV with an MS600 Pro multispectral camera was utilized to collect data, immediately followed by ground chlorophyll measurements with a Dualex handheld instrument. Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. The results provided the following indications: (1) Red-edge vegetation indices (RIs) and TIs were key to estimating RCCC. Univariate regression models were tolerable during the flowering and filling stages, while the superior multivariate models, incorporating multiple features, revealed more complex relationships, improving R² by 0.35% to 69.55% over the optimal univariate models. (2) The RF model showed notable performance in both univariate and multivariate regressions, with the RF model incorporating RIS and TIS during the flowering stage achieving the best results (R²_train = 0.93, RMSE_train = 1.36, RPD_train = 3.74, R²_test = 0.79, RMSE_test = 3.01, RPD_test = 2.20). With more variables, BPNN, KELM, and CNN models effectively leveraged neural network advantages, improving training performance. (3) Compared to using single-feature indices for RCCC estimation, the combination of vegetation indices and texture indices increased from 0.16% to 40.70% in the R² values of some models. Integrating UAV multispectral spectral and texture data allows effective RCCC estimation for winter wheat, aiding wheatland management, though further work is needed to extend the applicability of the developed estimation models. Full article

Variations in vegetation indices derived from multispectral images and digital terrain models from satellite imagery have been successfully used for reclamation and hazard management in former mining areas. However, low spatial resolution and the lack of sufficiently detailed information on surface morphology have restricted such studies to large sites. This study investigates the application of small, unmanned aerial vehicles (UAVs) equipped with multispectral sensors for land cover classification and vegetation monitoring. The application of UAVs bridges the gap between large-scale satellite remote sensing techniques and terrestrial surveys. Photogrammetric terrain models and orthoimages (RGB and multispectral) obtained from repeated mapping flights between November 2023 and May 2024 were combined with an ALS-based reference terrain model for object-based image classification. The collected data enabled differentiation between natural forests and areas affected by former mining activities, as well as the identification of variations in vegetation density and growth rates on former mining areas. The results confirm that small UAVs provide a versatile and efficient platform for classifying and monitoring mining areas and forested landslides. Full article

This study employs multi-source remote sensing information and machine learning methods to comprehensively assess the geological background, structural features, alteration anomalies, and spectral characteristics of the Malkansu Manganese Ore Belt in Xinjiang. Manganese mineralization is predicted, and areas with high mineralization potential are delineated. The results of the feature factor weight analysis indicate that structural density and lithological characteristics contribute most significantly to manganese mineralization. Notably, linear structures are aligned with the direction of the manganese belt, and areas exhibiting high controlling structural density are closely associated with the locations of mineral deposits, suggesting that structure plays a crucial role in manganese production in this region. The Area Under the Curve (AUC) values for the Random Forest (RF), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGBoost) models were 0.975, 0.983, and 0.916, respectively, indicating that all three models achieved a high level of performance and interpretability. Among these, the NB model demonstrated the highest performance. By algebraically overlaying the predictions from these three machine learning models, a comprehensive mineralization favorability map was generated, identifying 11 prospective mineralization zones. The performance metrics of the machine learning models validate their robustness, while regional tectonics and stratigraphic lithology provide valuable characteristic factors for this approach. This study integrates multi-source remote sensing information with machine learning methods to enhance the effectiveness of manganese prediction, thereby offering new research perspectives for manganese forecasting in the Malkansu Manganese Ore Belt. Full article

Rapeseed mapping is crucial for refined agricultural management and food security. However, existing remote sensing-based methods for rapeseed mapping in Southwest China are severely limited by insufficient training samples and persistent cloud cover. To address the above challenges, this study presents an automatic rapeseed mapping framework that integrates multi-source remote sensing data fusion, automated sample generation, and deep learning models. The framework was applied in Santai County, Sichuan Province, Southwest China, which has typical topographical and climatic characteristics. First, MODIS and Landsat data were used to fill the gaps in Sentinel-2 imagery, creating time-series images through the object-level processing version of the spatial and temporal adaptive reflectance fusion model (OL-STARFM). In addition, a novel spectral phenology approach was developed to automatically generate training samples, which were then input into the improved TS-ConvNeXt ECAPA-TDNN (NeXt-TDNN) deep learning model for accurate rapeseed mapping. The results demonstrated that the OL-STARFM approach was effective in rapeseed mapping. The proposed automated sample generation method proved effective in producing reliable rapeseed samples, achieving a low Dynamic Time Warping (DTW) distance (<0.81) when compared to field samples. The NeXt-TDNN model showed an overall accuracy (OA) of 90.12% and a mean Intersection over Union (mIoU) of 81.96% in Santai County, outperforming other models such as random forest, XGBoost, and UNet-LSTM. These results highlight the effectiveness of the proposed automatic rapeseed mapping framework in accurately identifying rapeseed. This framework offers a valuable reference for monitoring other crops in similar environments. Full article