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Bartelt G, You J, Hondzo M. 2024. Remote cyanobacteria detection by multispectral drone imagery. Lake Reserv Manage. 40:236–247. Cyanobacteria play a crucial role in the ecological services of aquatic environments. Remote detection of cyanobacteria in water using satellite-based sensor images has been proven effective in monitoring eutrophication and harmful algal blooms. Satellite-based sensors are good at tracking large blooms in oceans and lakes, but not in small bodies of water. This study seeks to use remote-sensing techniques on images obtained from a multispectral camera mounted on an unmanned aerial system (UAS). We investigated a small freshwater lake, Brownie Lake, in Minneapolis, Minnesota, using the UAS. We compared the collected imagery to the measurements of chlorophyll and phycocyanin concentrations. Cyanobacterial chlorophyll a (Chl-a) concentrations and multispectral UAS data showed good agreement (r2 = 0.54) in this study. Chl-a concentration strongly correlated with the presence of the near-infrared band at 840 nm and the red band at 668 nm. The most correlated spectral band combinations were the normalized difference vegetative index (NDVI) and 2 band algorithm (2BDA). Our research demonstrates the usefulness of UAS technologies in water quality monitoring. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with multispectral cameras offer great potential for applications in precision agriculture. A critical challenge that limits the deployment of this technology is the varying ambient illumination caused by cloud movement. Rapidly changing solar irradiance primarily affects the radiometric calibration process, resulting in reflectance distortion and heterogeneity in the final generated orthomosaic. In this study, we optimized the radiometric block adjustment (RBA) method, which corrects for changing illumination by comparing adjacent images and from incidental observations of reference panels to produce accurate and uniform reflectance orthomosaics regardless of variable illumination. The radiometric accuracy and uniformity of the generated orthomosaic could be enhanced by improving the weights of the information from the reference panels and by reducing the number of tie points between adjacent images. Furthermore, especially for crop monitoring, we proposed the RBA-Plant method, which extracts tie points solely from vegetation areas, to further improve the accuracy and homogeneity of the orthomosaic for the vegetation areas. To validate the effectiveness of the optimization techniques and the proposed RBA-Plant method, visual and quantitative assessments were conducted on a UAV-image dataset collected under fluctuating solar irradiance conditions. The results demonstrated that the optimized RBA and RBA-Plant methods outperformed the current empirical line method (ELM) and sensor-corrected approaches, showing significant improvements in both radiometric accuracy and homogeneity. Specifically, the average root mean square error (RMSE) decreased from 0.084 acquired by the ELM to 0.047, and the average coefficient of variation (CV) decreased from 24% (ELM) to 10.6%. Furthermore, the orthomosaic generated by the RBA-Plant method achieved the lowest RMSE and CV values, 0.039 and 6.8%, respectively, indicating the highest accuracy and best uniformity. In summary, although UAVs typically incorporate lighting sensors for illumination correction, this research offers different methods for improving uniformity and obtaining more accurate reflectance values from orthomosaics. [ABSTRACT FROM AUTHOR]

Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light detection and ranging (LiDAR) techniques, with deep learning (DL) algorithms for land cover classification can help to address this problem. Therefore, we propose an image-based land cover classification methodology based on fusing multispectral and airborne LiDAR data by adopting CNN-based semantic segmentation in a semi-arid Mediterranean area of northeastern Aegean, Greece. The methodology consists of three stages: (i) data pre-processing, (ii) semantic segmentation, and (iii) accuracy assessment. The multispectral bands were stacked with the calculated Normalized Difference Vegetation Index (NDVI) and the LiDAR-based attributes height, intensity, and number of returns converted into two-dimensional (2D) images. Then, a hyper-parameter analysis was performed to investigate the impact on the classification accuracy and training time of the U-Net architecture by varying the input tile size and the patch size for prediction, including the learning rate and algorithm optimizer. Finally, comparative experiments were conducted by altering the input data type to test our hypothesis, and the CNN model performance was analyzed by using accuracy assessment metrics and visually comparing the segmentation maps. The findings of this investigation showed that fusing multispectral and LiDAR data improves the classification accuracy of the U-Net, as it yielded the highest overall accuracy of 79.34% and a kappa coefficient of 0.6966, compared to using multispectral (OA: 76.03%; K: 0.6538) or LiDAR (OA: 37.79%; K: 0.0840) data separately. Although some confusion still exists among the seven land cover classes observed, the U-Net delivered a detailed and quite accurate segmentation map. [ABSTRACT FROM AUTHOR]

Coloration is crucial for ambush mesopredators' survival as they depend on it for camouflage to hunt and avoid predation. Furthermore, coloration is especially important in ectotherms as it is linked to two essential functions for survival: thermoregulation and crypsis. In a context of trade‐offs between thermoregulation and crypsis, the comprehension of how ectotherms employ color to address conflicting demands of thermoregulation and crypsis is limited. This study investigated background matching and thermoregulation in western diamond‐backed rattlesnakes (Crotalus atrox). Rattlesnakes were predicted to better match selected microhabitats in terms of color, luminance, and pattern than random microhabitats within their home range, and body temperature was predicted to influence their body coloration with higher temperatures favoring lighter colors. Pictures of 14 radiotracked western diamond‐backed rattlesnakes were taken in situ with a full spectrum camera (UV/VIS) and body temperature was recovered from internal temperature‐datalogging radiotransmitters. Crotalus atrox matched the color, luminance, and pattern of the background better than a randomly selected background, thus enhancing background matching. Additionally, rattlesnake coloration varied independently of temperature indicating that rattlesnakes are behaviorally modifying crypsis regardless of thermoregulation. [ABSTRACT FROM AUTHOR]

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian “Valley of the Kings” (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves.

Spatio-temporal dynamics of surface water reservoirs at regional and global scales remain poorly understood. Using satellite remote sensing provides a unique opportunity to address this problem. This study aims to (1) quantify the extent and volume of surface water and (2) compare our approach with other datasets. We utilized MODIS-based spectral indices to monitor temporal variations in inundation extent. By interpolating water levels across the surface water extent, we generated water level maps and quantified surface water volume from 2000 to 2022. Evaluation against ICESat-2 data involved 64 comparisons, with approximately 58% showing an R2 value greater than or equal to 0.5, and 38% were higher than or equal to 0.7. Compared to the flooding model, our method aligns more closely with ICESat-2 data, contrary to the flooding model which tends to overestimate water levels and, consequently, water storage. [ABSTRACT FROM AUTHOR]

Remote sensing is a unique and cost-effective tool that provides information about the nitrogen status of plants in a non-destructive way. The objective of this study is to evaluate the effectiveness of aerial multispectral imagery captured by UAV for estimating corn nitrogen (N) and chlorophyll (Chl) content at different growth stages. The study used a fully randomized experimental design with four treatments of nitrogen fertilizer (0, 50%, 100%, and 150%). Ten plants were randomly selected in each plot at the phenological stages of 8 leaves (V8) and tasseling growth stages (VT) for sampling. Leaf samples were taken to measure total nitrogen (N) and chlorophyll (Chl) content. Mathematical models were created using vegetation indices extracted from aerial multispectral images to estimate the amount of nitrogen and chlorophyll. The models were evaluated using the leave-one-out cross-validation method. The results showed that there is a significant positive relationship between the leaf dry weight (LDW), the Chl and N content with the amount of nitrogen fertilizer used. So, the results indicated that the REIP index is suitable for estimating chlorophyll content in both the V8 (R² of 0.997) and VT (R² of 0.980) growth stages. Additionally, the REIP index was found to be an appropriate index for estimating N content in both growth stages (R² of 0.980). It can be concluded that aerial multispectral remote sensing technology is a reliable method for estimating corn nitrogen and chlorophyll content. [ABSTRACT FROM AUTHOR]

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian "Valley of the Kings" (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) is often employed to estimate suspended sediment concentration (SSC) in rivers, and the availability of hyperspectral imagery enhances the effectiveness of RS-based water quality monitoring due to its high spectral resolution. Yet, the necessity of hyperspectral imagery for SSC estimation in rivers has not been fully validated. This study thus compares the performance of hyperspectral RS with that of multispectral RS by conducting field-scale experiments in shallow rivers. In the field experiments, we measured radiance from a water body mixed with suspended sediments using a drone-mounted hyperspectral sensor, with the sediment and riverbed types considered as controlling factors. We retrieved the SSC from UAV imagery using an optimal band ratio analysis, which successfully estimated SSC distributions in the sand bed conditions with both multispectral and hyperspectral data. In the vegetated bed conditions, meanwhile, the prediction accuracy decreased significantly due to the temporally varying bottom reflectance associated with the random movement of vegetation caused by near-bed turbulence. This is because temporally inhomogeneous bottom reflectance distorts the relationship between the SSC and total reflectance. Nevertheless, the hyperspectral imaging exhibited better prediction accuracy than the multispectral imaging, effectively extracting optimal spectral bands sensitive to back-scattered reflectance from sediments while constraining the bottom reflectance caused by the vegetation-covered bed. [ABSTRACT FROM AUTHOR]

This study examines the application of unsupervised classification techniques in the mapping of forest vegetation, aiming to align vegetation cover with the Braun-Blanquet classification system through remote sensing. By leveraging Landsat 8 and 9 satellite imagery and advanced clustering algorithms, specifically the Weka X-Means, this research addresses the challenge of minimizing researcher subjectivity in vegetation mapping. The methodology incorporates a two-step clustering approach to accurately classify forest communities, utilizing a comprehensive set of vegetation indices to distinguish between different types of forest ecosystems. The validation of the classification model relied on a detailed analysis of over 17,000 relevés from the "Flora" database, ensuring a high degree of accuracy in matching satellite-derived vegetation classes with field observations. The study's findings reveal the successful identification of 44 forest community types that was aggregated into seven classes of Braun-Blanquet classification system, demonstrating the efficacy of unsupervised classification in generating reliable vegetation maps. This work not only contributes to the advancement of remote sensing applications in ecological research, but also provides a valuable tool for natural resource management and conservation planning. The integration of unsupervised classification with the Braun-Blanquet system presents a novel approach to vegetation mapping, offering insights into ecological characteristics, and can be good starter point for sequestration potential of forest communities' assessment in the Republic of Tatarstan. [ABSTRACT FROM AUTHOR]

Despite significant progress has been made towards crop yield prediction with remote sensing, there exist knowledge gaps on (1) the impacts of temporal resolution of imaging frequencies on yield prediction, (2) transferability of the models among different genotypes and test sites, and (3) translation of these research developments to crop breeding that benefit farmers. Existing research predominantly provides an on-site perspective, frequently missing the complexities of real-world applications. The objectives of this paper are to investigate the transferability and generalization capabilities of yield prediction models for crop breeding across test sites located in North and South Americas. Toward that goal, we tested different machine learning techniques including Random Forest Regressor (RF), Gradient Boosting Regression (GB), and Deep Neural Networks (DNN) for soybean yield prediction with experiments conducted in different climate and growth conditions. A novel transfer learning approach was proposed for genotype selection and categorizing soybean yield for screening high-yield varieties. Furthermore, we studied the effect of temporal resolution on yield prediction, focusing on the critical development stages and optimal aerial survey frequencies for precise yield prediction using large 31,404 sample data. Results demonstrated that the combined dataset of Argentina and United States representing different climate regimes provided the highest performance with an R2 of 0.76 using RF and GB algorithms. The classification approach was proven to be most useful for crop breeding as demonstrated by accurately identifying the high-yielding genotypes. Increasing temporal sampling of key phenological stages significantly improved yield prediction. Although transfer learning yielded promising outcomes across trials within Argentina the efficacy of transferring models from Argentina to the United States was limited, attributed to significant seasonal and climate variations. This study pioneered the use of transfer learning for model adaptability in real-world breeding scenarios, training and transferring models within the South and North Americas, providing actionable insights and strategies for the breeding community, aiming to facilitate improved decision-making for agricultural productivity. [ABSTRACT FROM AUTHOR]

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi'an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers. [ABSTRACT FROM AUTHOR]

This paper introduces a dataset of aerial imagery captured during the 2022 cocoa growing season in the central-western region of Côte d'Ivoire. The images were acquired using a multispectral camera mounted on a DJI Phantom 4 unmanned aerial vehicle (UAV). The agricultural land surveyed encompasses 10 different types of cocoa-based agroforestry systems, each ranging from 2.6 ha to 8.3 ha, totaling 7638 images and covering 30 ha. The UAV mission was conducted at an altitude of 80 m, with a side overlap of 70 % and a front overlap of 80 %. This configuration achieved ground sampling distances (GSD) ranging from 4.2 to 4.6 cm providing high-resolution detailed imagery of those lands.These high-resolution RGB and multispectral images can be used to characterize the structural complexity of the systems as well as the abundance, and the health of the trees in these cocoa-based systems. It can be a valuable resource for researchers in the fields of ecology, agriculture, and environmental monitoring. The dataset supports a wide range of applications, from precision agriculture to sustainable cocoa land use management, making it a pivotal tool for enhancing agricultural practices and ecosystem management in Ivorian regions facing environmental and economic challenges.

In water monitoring, it is always a hot issue to exploring a method to improve the accuracy of remote sensing inversion. To this end, based on the actual investigation of the surrounding environment and water quality characteristics of the Fuyang river, this study has established five distinct water functional zones: reservoir (RS), village river (VR), industrial river (IR), artificial lake (AL), and urban river (UR), which are considered as subdivided datasets. Meanwhile, considering that lakes and reservoirs typically contain relatively stationary or slow-moving water bodies, while river channels are characterized by moving water bodies, three multi-type datasets have been constructed for comparison. These are based on the morphological and flow characteristics of different regions within the Fuyang river and include river channel (R), lake (L), and whole basin (W). Based on multi-spectral images of unmanned aerial vehicle (UAV) and measured water quality data, ten types of inversion models were to invert six kinds of water quality parameters, and compare and analyze the performance of the optimal inversion model of the corresponding data sets. The results show that the multivariate regression model is superior to the univariate regression model, and the mean coefficient of determination (R2) of the subdivided data set is 0.936, and the root mean square error (RMSE) and mean absolute error (MAE) appear decrease in different degrees compared with the W with 3.573 and 2.662, respectively. The mean ratio performance to interquartile (RPIQ) of the corresponding model in UR and VR are 3.963 and 2.748, respectively. Extremely Randomized Trees (ERT) model is more suitable for the inversion of multi-type data sets lacking obvious water quality characteristics and with complex components, while Categorical Boosting (CatBoost) model is more suitable for the inversion of subdivided data sets with obvious water quality characteristics. The current method has practical guiding significance for improving the application level of water monitoring technology in ecological environment protection and urban water resources protection.

IntroductionEstimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level.MethodsThis study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the 'Independence' cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level.ResultsThe deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation.DiscussionThis study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability.

The Remote Sensing (RS) field continuously grapples with the challenge of transforming satellite data into actionable information. This ongoing issue results in an ever-growing accumulation of unlabeled data, complicating interpretation efforts. The situation becomes even more challenging when satellite data must be used immediately to identify the effects of a natural hazard. Self-supervised learning (SSL) offers a promising approach for learning image representations without labeled data. Once trained, an SSL model can address various tasks with significantly reduced requirements for labeled data. Despite advancements in SSL models, particularly those using contrastive learning methods like MoCo, SimCLR, and SwAV, their potential remains largely unexplored in the context of instance segmentation and semantic segmentation of satellite imagery. This study integrates SwAV within an auto-encoder framework to detect landslides using deca-metric resolution multi-spectral images from the globally-distributed large-scale landslide4sense (L4S) 2022 benchmark dataset, employing only 1% and 10% of the labeled data. Our proposed SSL auto-encoder model features two modules: SwAV, which assigns features to prototype vectors to generate encoder codes, and ResNets, serving as the decoder for the downstream task. With just 1% of labeled data, our SSL model performs comparably to ten state-of-the-art deep learning segmentation models that utilize 100% of the labeled data in a fully supervised manner. With 10% of labeled data, our SSL model outperforms all ten fully supervised counterparts trained with 100% of the labeled data.

Exploiting machine learning techniques to automatically classify multispectral remote sensing imagery plays a significant role in deriving changes on the Earth’s surface. However, the computation power required to manage large Earth observation data and apply sophisticated machine learning models for this analysis purpose has become an intractable bottleneck. Leveraging quantum computing provides a possibility to tackle this challenge in the future. This article focuses on land cover classification by analyzing Sentinel-2 images with quantum computing. Two hybrid quantum-classical deep learning frameworks are proposed. Both models exploit quantum computing to extract features efficiently from multispectral images and classical computing for final classification. As proof of concept, numerical simulation results on the LCZ42 dataset through the TensorFlow Quantum platform verify our models' validity. The experiments indicate that our models can extract features more effectively compared with their classical counterparts, specifically, the convolutional neural network (CNN) model. Our models demonstrated improvements, with an average test accuracy increase of 4.5% and 3.3%, respectively, in comparison to the CNN model. In addition, our proposed models exhibit better transferability and robustness than CNN models.

Managing sloping terrains worldwide presents a significant challenge due to the lack of structured management practices, particularly when integrating various fruit trees in irregular arrangements. This study addresses the complexity arising from mixed cultivation by proposing a solution utilizing Unmanned Aerial Vehicle (UAV) imagery for tree species recognition and fruit tree classification. Our approach involves equipping UAVs with multispectral and optical cameras to capture imagery over experimental sloping terrain. The collected data undergoes processing for classifying different types of fruit trees, roads, and buildings through Orthophoto images. Convolutional Neural Networks (CNN) are employed for image recognition in challenging hillside terrains, with deep neural network methods, specifically VGG-16, VGG-19, and ResNet-50, being applied and compared. VGG-16 achieved significant accuracy in multispectral imagery analysis. Subsequently, various image fusion techniques, including Brovey, Hue- Saturation-Value, Principal Components Analysis (PCA), and Gram-Schmidt, were explored, with PCA demonstrating superior performance. The study revealed that image fusion, particularly with near-infrared or red-edge bands, significantly enhanced prediction accuracy compared to standalone multispectral imagery. The combination of visible-band fused imagery with additional spectral bands yielded the highest accuracy, improving overall prediction accuracy from 0.76 to 0.92. This research provides valuable insights applicable to diverse regions grappling with challenges in managing sloping terrains and mixed fruit tree cultivation.

Modern geoinformation technologies, such as remote sensing satellite missions and classification methods, are becoming increasingly prominent in land cover classification. Due to the emergence of high spatial resolution missions with improved temporal and spectral resolutions, such as Worldview-3, this approach enabled new possibilities in land management. To provide an in-depth analysis of such possibilities, this study reviews methods of land cover classification using WorldView-3 satellite imagery. With 29 different spectral channels and a spatial resolution of 1.2 m, Worldview-3 multispectral satellite images represent the most modern currently publicly available commercial multispectral images. The classification of multispectral images is performed to facilitate the identification and recognition of objects in the images. Analyzed classification methods are: supervised (semi-automatic) classification methods, unsupervised (automatic) classification methods, and object-based classification methods. In order to increase the accuracy in land cover studies, it was determined as necessary to develop automatic methods that rely on a combination of controlled and uncontrolled classification methods. This approach enables the automatic determination of samples for conducting supervised classifications of interest for land management.

Managing total suspended solids (TSS) in inland water is crucial for maintaining water quality and aquatic ecosystems. To obtain spatial data, the conventional method of point sampling TSS is a time- and labor-intensive task. Thus, recent efforts have focused on using satellite imagery to estimate TSS. Previous studies have used empirical method and machine learning model with multispectral satellite bands; however, these are limited to specific study areas and the amount of data is insufficient. This study aimed to build a generalized estimating model that can generally estimate the spatial and temporal variations in TSS concentrations in an extensive region by integrating the Water Quality Monitoring System with Sentinel-2 satellite observations using deep learning algorithms. This would provide comprehensive coverage of continental terrain, enabling consistent acquisition of satellite data and computation of the TSS across four major rivers in South Korea. Deep learning algorithms can generally estimate TSS concentrations over large areas. We found that the convolutional neural network (CNN) model was more accurate than traditional regression and other models, with a Nash-Sutcliffe efficiency (NSE) of 0.758. These findings indicate that it is possible to estimate TSS concentrations in ungauged areas, allowing the acquisition of extensive and continuous spatiotemporal datasets for organic micropollutants. This would be of great assistance in monitoring micropollutants in the four major rivers in South Korea.

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R²=0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R²=0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops. [ABSTRACT FROM AUTHOR]

Modern geoinformation technologies, such as remote sensing satellite missions and classification methods, are becoming increasingly prominent in land cover classification. Due to the emergence of high spatial resolution missions with improved temporal and spectral resolutions, such as Worldview-3, this approach enabled new possibilities in land management. To provide an in-depth analysis of such possibilities, this study reviews methods of land cover classification using WorldView-3 satellite imagery. With 29 different spectral channels and a spatial resolution of 1.2 m, Worldview-3 multispectral satellite images represent the most modern currently publicly available commercial multispectral images. The classification of multispectral images is performed to facilitate the identification and recognition of objects in the images. Analyzed classification methods are: supervised (semi-automatic) classification methods, unsupervised (automatic) classification methods, and object-based classification methods. In order to increase the accuracy in land cover studies, it was determined as necessary to develop automatic methods that rely on a combination of controlled and uncontrolled classification methods. This approach enables the automatic determination of samples for conducting supervised classifications of interest for land management. [ABSTRACT FROM AUTHOR]

Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. To address these challenges and increase food security, input cost reductions and yield optimization can be accomplished using yield precision maps created by machine learning models; however, without considering the spatial structure of the data, the precision map's accuracy evaluation assessment risks being over-optimistic, which may encourage poor decision making that can lead to negative economic impacts (e.g., lowered crop yields). In fact, most machine learning research involving spatial data, including the unmanned aerial vehicle (UAV) imagery-based yield prediction literature, ignore spatial structure and likely obtain over-optimistic results. The present work is a UAV imagery-based corn yield prediction study that analyzed the effects of image spatial and spectral resolution, image acquisition date, and model evaluation scheme on model performance. We used various spatial generalization evaluation methods, including spatial cross-validation (CV), to (a) identify over-optimistic models that overfit to the spatial structure found inside datasets and (b) estimate true model generalization performance. We compared and ranked the prediction power of 55 vegetation indices (VIs) and five spectral bands over a growing season. We gathered yield data and UAV-based multispectral (MS) and red-green-blue (RGB) imagery from a Canadian smart farm and trained random forest (RF) and linear regression (LR) models using 10-fold CV and spatial CV approaches. We found that imagery from the middle of the growing season produced the best results. RF and LR generally performed best with high and low spatial resolution data, respectively. MS imagery led to generally better performance than RGB imagery. Some of the best-performing VIs were simple ratio index(near-infrared and red-edge), normalized difference red-edge index, and normalized green index. We found that 10-fold CV coupled with spatial CV could be used to identify over-optimistic yield prediction models. When using high spatial resolution MS imagery, RF and LR obtained 0.81 and 0.56 correlation coefficient (CC), respectively, when using 10-fold CV, and obtained 0.39 and 0.41, respectively, when using a k-means-based spatial CV approach. Furthermore, when using only location features, RF and LR obtained an average CC of 1.00 and 0.49, respectively. This suggested that LR had better spatial generalizability than RF, and that RF was likely being over-optimistic and was overfitting to the spatial structure of the data. [ABSTRACT FROM AUTHOR]

Crop yield prediction is important for farmers to conduct proper field management and make marketing decisions. Yield prediction models built on single-type and same-year data may not reflect the holistic effect of environment and management on crop development. This study aimed to quantify cotton yield variation due to soil texture and weather conditions using the multiple-year unmanned aerial vehicle (UAV) imagery and deep learning techniques. UAV images were collected about once a month to quantify cotton growth at two irrigated cotton fields in three years (2017–2019). Soil apparent electrical conductivity (ECa) of the fields was measured and calibrated to quantify soil texture and soil water holding capacity using eleven soil features. Convolutional neural networks (CNNs) were used to process and analyse the soil features in seven different depths. Similarly, a second CNN was used to analyse the six weather parameters derived from the historical data of a nearby weather station. A gated recurrent unit (GRU) network was used to predict cotton yield using the processed soil data, weather data, and UAV-based image features (e.g., NDVI) of different months. Results show that the GRU model trained with data in two years could predict the cotton yield in a third year with prediction errors of mean average error from 247 (8.9%) to 384 kg ha−1 (13.7%). The study indicates that the developed CNN and GRU networks have the potential to predict crop yield of years other than the ones used for training the models. [ABSTRACT FROM AUTHOR]

Anomalously intensive gas seeps on the sea surface due to damage to the Nord Stream 1 and Nord Stream 2 subsea gas pipelines in the Baltic Sea have been analyzed using optical (Kanopus-V and Sentinel-2B) and radar (Sentinel-1A) satellite images. Positive contrasts of NRCS (up to 7.5 dB) registered by Sentinel-1A radar and of spectral reflectance (up to 0.73 units) registered by Kanopus-V and Sentinel-2B optical sensors have been detected in the area of the gas seeps. Specific features of the seeps were revealed on the sea surface in the form of dome-shaped swells, fountains, vortices, foam, wave breaking, disturbance of the surface wave structure, and wind shadows. Taking into consideration the volume and density of gas located in the damaged gas pipelines, it was shown that the total volume of methane emission was no more than ~0.51 Tg, i.e., less than 0.1% of the annual global methane emissions into the atmosphere. [ABSTRACT FROM AUTHOR]

Nonmetallic minerals like granite and limestone have calcite and biotitic ingredients as their major part which exhibit wonderful absorption features in the visible and short wave range of the electromagnetic spectrum. This research puts emphasis on delineating granite and limestone deposits of the Mardan district through the latest multispectral Landsat‐9 and Sentinel‐2 sensors of which the latter provided 94% mapping accuracy in delineating granites (biotitic bearing minerals) and limestone (calcite‐bearing minerals). The Image processing techniques of minimum noise fraction, which is double cascaded principal components analysis and pixel purity index algorithms proved helpful to bring significant improvements in classification results and in the reduction of noise and data size. The outcomes of the research study show that supervised machine learning algorithms are impactful to map such minerals provided that the data must be well organized and limited in size. The results achieved were verified through validation steps like, (a) Independent reference data of high‐resolution Google Earth maps and (b) Ground survey reports. Arc GIS 10.2 and Envi 5.3 software suite were used to create (a) ground truth points at random for accuracy assessment (b) portraying study area maps (c) Image Processing and Preprocessing tools and (d) implementation of machine learning algorithms. Access to the data and software suite is being provided for open research work. Key Points: This research puts emphasis on delineating granite and limestone deposits of Mardan district through latest multispectral sensorsThis research finds band ratios‐(B4/B5) and (B7 + B9)/(B8*2) of Short Wave Infrared (SWIR) to be effective in identifying minerals patchesArc GIS 10.2 was used to create ground truth points at random for accuracy assessment and portraying study area maps [ABSTRACT FROM AUTHOR]

Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models. [ABSTRACT FROM AUTHOR]

Over-application of nitrogen fertilizers in farm lands is the main reason to contaminate nitrogen components in the environment. Nitrogen can play a main role on the plant growth, biomass and crop yield. Therefore, the aim of this study was to evaluate different nitrogen rates and the response of maize plant characteristics on some vegetative indices using aerial multispectral imaging. The study was carried out in an experimental farm in Varamin city (35.80°N, 51.40°E), Tehran province, Iran. The experiment was performed in Randomized Complete Block Design (RCBD) with four treatments and two sampling stage in four replications. The treatments were different nitrogen rates included none (as control), 250, 500 and 750 kg urea ha−1 fertilizer which was applied by irrigation water and two sampling stage, 8-leaf stage (V8) and tasseling stage. Data analysis based on the different growth stages showed that, there were significantly increased in the average plant height (198 cm), total dry weight (336.73 g), SPAD index, and leaf nitrogen content in both V8 and tasseling stage. Based on our results, in the V8 growth stage chlorophyll index, modified triangle vegetation index (MTVI), and green model index with the correlation coefficients 0.88, 0.86, and 0.80, respectively, were the most suitable indicators for detecting nitrogen stress in this edaphic and climatic conditions for this plant. Generally, it can be concluded that chlorophyll index in the V8 growth stage and nitrogen reflectance index (NRI) in the tasseling growth stage are the most appropriate indicators for detecting nitrogen stress in that studied area. As a result, aerial multispectral imaging is a reliable technology to assist farmers in fertilizing plants at the right time and place, as well as the amount that the plants need. So, this technology is in accordance with the precious agriculture' goals and eco-friendly. [ABSTRACT FROM AUTHOR]

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future.

Unmanned aerial vehicles (UAVs) equipped with multispectral cameras offer great potential for applications in precision agriculture. A critical challenge that limits the deployment of this technology is the varying ambient illumination caused by cloud movement. Rapidly changing solar irradiance primarily affects the radiometric calibration process, resulting in reflectance distortion and heterogeneity in the final generated orthomosaic. In this study, we optimized the radiometric block adjustment (RBA) method, which corrects for changing illumination by comparing adjacent images and from incidental observations of reference panels to produce accurate and uniform reflectance orthomosaics regardless of variable illumination. The radiometric accuracy and uniformity of the generated orthomosaic could be enhanced by improving the weights of the information from the reference panels and by reducing the number of tie points between adjacent images. Furthermore, especially for crop monitoring, we proposed the RBA-Plant method, which extracts tie points solely from vegetation areas, to further improve the accuracy and homogeneity of the orthomosaic for the vegetation areas. To validate the effectiveness of the optimization techniques and the proposed RBA-Plant method, visual and quantitative assessments were conducted on a UAV-image dataset collected under fluctuating solar irradiance conditions. The results demonstrated that the optimized RBA and RBA-Plant methods outperformed the current empirical line method (ELM) and sensor-corrected approaches, showing significant improvements in both radiometric accuracy and homogeneity. Specifically, the average root mean square error (RMSE) decreased from 0.084 acquired by the ELM to 0.047, and the average coefficient of variation (CV) decreased from 24% (ELM) to 10.6%. Furthermore, the orthomosaic generated by the RBA-Plant method achieved the lowest RMSE and CV values, 0.039 and 6.8%, respectively, indicating the highest accuracy and best uniformity. In summary, although UAVs typically incorporate lighting sensors for illumination correction, this research offers different methods for improving uniformity and obtaining more accurate reflectance values from orthomosaics.

Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light detection and ranging (LiDAR) techniques, with deep learning (DL) algorithms for land cover classification can help to address this problem. Therefore, we propose an image-based land cover classification methodology based on fusing multispectral and airborne LiDAR data by adopting CNN-based semantic segmentation in a semi-arid Mediterranean area of northeastern Aegean, Greece. The methodology consists of three stages: (i) data pre-processing, (ii) semantic segmentation, and (iii) accuracy assessment. The multispectral bands were stacked with the calculated Normalized Difference Vegetation Index (NDVI) and the LiDAR-based attributes height, intensity, and number of returns converted into two-dimensional (2D) images. Then, a hyper-parameter analysis was performed to investigate the impact on the classification accuracy and training time of the U-Net architecture by varying the input tile size and the patch size for prediction, including the learning rate and algorithm optimizer. Finally, comparative experiments were conducted by altering the input data type to test our hypothesis, and the CNN model performance was analyzed by using accuracy assessment metrics and visually comparing the segmentation maps. The findings of this investigation showed that fusing multispectral and LiDAR data improves the classification accuracy of the U-Net, as it yielded the highest overall accuracy of 79.34% and a kappa coefficient of 0.6966, compared to using multispectral (OA: 76.03%; K: 0.6538) or LiDAR (OA: 37.79%; K: 0.0840) data separately. Although some confusion still exists among the seven land cover classes observed, the U-Net delivered a detailed and quite accurate segmentation map.

Accurate and timely monitoring of biomass in breeding nurseries is essential for evaluating plant performance and selecting superior genotypes. Traditional methods for phenotyping above-ground biomass in field conditions requires significant time, cost, and labor. Unmanned Aerial Vehicles (UAVs) offer a rapid and non-destructive approach for phenotyping multiple field plots at a low cost. While Vegetation Indices (VIs) extracted from remote sensing imagery have been widely employed for biomass estimation, they mainly capture spectral information and disregard the 3D canopy structure and spatial pixel relationships. Addressing these limitations, this study, conducted in 2020 and 2021, aimed to explore the potential of integrating UAV multispectral imagery-derived canopy spectral, structural, and textural features with machine learning algorithms for accurate oat biomass estimation. Six oat genotypes planted at two seeding rates were evaluated in two South Dakota locations at multiple growth stages. Plot-level canopy spectral, structural, and textural features were extracted from the multispectral imagery and used as input variables for three machine learning models: Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). The results showed that (1) in addition to canopy spectral features, canopy structural and textural features are also important indicators for oat biomass estimation; (2) combining spectral, structural, and textural features significantly improved biomass estimation accuracy over using a single feature type; (3) machine learning algorithms showed good predictive ability with slightly better estimation accuracy shown by RFR (R2 = 0.926 and relative root mean square error (RMSE%) = 15.97%). This study demonstrated the benefits of UAV imagery-based multi-feature fusion using machine learning for above-ground biomass estimation in oat breeding nurseries, holding promise for enhancing the efficiency of oat breeding through UAV-based phenotyping and crop management practices. [ABSTRACT FROM AUTHOR]

Following consecutive years of governance efforts, there has been a substantial reduction in sediment transport in the Yellow River, resulting in significant changes in its water–sediment dynamics. This necessitates precise monitoring of sediment-bearing tributary inflows, a crucial requirement for effective governance strategies on the Loess Plateau's current developmental stage. While satellite remote sensing technology has been widely used to estimate suspended particulate matter concentration (CSPM) in open water bodies like oceans and lakes, its application in narrow rivers presents challenges related to hybrid pixel and proximity effects. As a result, the effectiveness and competence of satellite remote sensing in monitoring CSPM in such confined river environments are reduced. This study attempted to use unmanned aerial vehicle (UAV) remote sensing with multispectral technology to invert CSPM in the Wuding River, a sediment-bearing Yellow River tributary. A novel CSPM concentration inversion model was introduced for highly turbid river settings. The results showed that the accuracy of the new band ratio model in this study is significantly improved compared with the existing models. The validation dataset had a coefficient of determination (R2) of 0.83, a root mean square error (RMSE) of 3.73 g/L, and a mean absolute percentage error (MAPE) of 44.95% (MAPE is 40.68% at 1–20 g/L, and 12.37% at >20 g/L). On this basis, the UAV also monitored the impacts of heavy rainfall on the CSPM, resulting in a rapid rise and fall in CSPM over a period of ten hours. This study demonstrated the potential of UAV remote sensing for CSPM monitoring in extremely turbid narrow rivers (tens to tens of meters), especially before and after rainfall sediment production events, which can provide technical support for accurate sediment management and source identification in the main tributaries of the Yellow River and help realize the goal of high-quality development of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Peri-urban areas are transitional zones on the outer boundaries of cities. These regions have immense growth potential, and it is necessary to observe the land use landcover changes to understand the dynamics of these transformations. The area selected for this study is towards the Southern fringe of Chennai, Tamil Nādu, India, and is analyzed using multi-spectral satellite imagery from Landsat 5 and 8. The primary objective of the study is to assess the change in landcover classes, namely water, land, and vegetation, over a 30-year study period between 1991 and 2021. The peri-urban regions majorly are arable land. Hence, NDVI is considered a suitable index to monitor the landcover changes. The spatiotemporal analysis indicates an increase of 19.43% in land /barren areas towards the Northern parts near the study area and along the transit and industrial corridors. No significant changes are observed in the areas of vegetation that could be attributed to efforts taken to conserve reserve forests and increase green zones in newer developments. A steep depletion of 46.86% of water bodies observed in the region also corresponds to water scarcity problems. Accuracy was assessed using ground-truthing methods, computing the confusion matrix and Kappa coefficient. NDVI is used efficiently in the landcover classification but does not indicate the difference between built-up areas and barren land. Change detection map prepared using ARCGIS indicates the areas that have been converted to other landcover over a period of 30 years. The study reveals an urgent need to bring in policy decisions to conserve waterbodies and green spaces in the initial stages of urban planning for sustainable developments in the fringe areas. [ABSTRACT FROM AUTHOR]

Infiltration models and impervious surface models have gained significant attention in recent years as crucial tools in urban and environmental planning, to assess the extent of land-surface changes and their impacts on hydrological processes. These models are important for understanding the hydrological dynamics and ecological impacts of urbanization and for the improvement of sustainable land-use planning and stormwater-management strategies. Due to the fact that many authors partially or entirely overlook the significance of the infiltration process in geographic information system (GIS) analyses, there is currently no universally accepted method for creating an infiltration model that is suitable for GIS multicriteria decision analysis (GIS-MCDA). This research paper presents an innovative approach to modeling the infiltration-efficiency index (IEI) for GIS analysis, with a focus on achieving high-quality results. The proposed methodology integrates very-high-resolution (VHR) remote-sensing data, GIS-MCDA, and statistical methods. The methodology was tested and demonstrated on a small sub-catchment in Metković, Croatia. The study developed a VHR IEI model from six specific criteria that produced values between 0 and 0.71. The model revealed that 14.89% of the research area is covered by impervious surfaces. This percentage is relatively favorable when compared to urban areas globally. The majority of the research area (62.79%) has good infiltration efficiency. These areas are predominantly characterized by agricultural land use, encompassing orchards, tangerines, olive groves, vineyards, and a diverse range of low-lying and high vegetation on flat terrain. The IEI model can provide input spatial data for high-resolution GIS analysis of hydrological processes. This model will aid decision-makers in stormwater-management, flood-risk assessment, land-use planning, and the design of green infrastructure. By utilizing the information derived from this study, policymakers can make informed decisions to mitigate flooding risks and promote sustainable urban development. [ABSTRACT FROM AUTHOR]

Plant potassium content (PKC) is a crucial indicator of crop potassium nutrient status and is vital in making informed fertilization decisions in the field. This study aims to enhance the accuracy of PKC estimation during key potato growth stages by using vegetation indices (VIs) and spatial structure features derived from UAV-based multispectral sensors. Specifically, the fraction of vegetation coverage (FVC), gray-level co-occurrence matrix texture, and multispectral VIs were extracted from multispectral images acquired at the potato tuber formation, tuber growth, and starch accumulation stages. Linear regression and stepwise multiple linear regression analyses were conducted to investigate how VIs, both individually and in combination with spatial structure features, affect potato PKC estimation. The findings lead to the following conclusions: (1) Estimating potato PKC using multispectral VIs is feasible but necessitates further enhancements in accuracy. (2) Augmenting VIs with either the FVC or texture features makes potato PKC estimation more accurate than when using single VIs. (3) Finally, integrating VIs with both the FVC and texture features improves the accuracy of potato PKC estimation, resulting in notable R² values of 0.63, 0.84, and 0.80 for the three fertility periods, respectively, with corresponding root mean square errors of 0.44%, 0.29%, and 0.25%. Overall, these results highlight the potential of integrating canopy spectral information and spatial-structure information obtained from multispectral sensors mounted on unmanned aerial vehicles for monitoring crop growth and assessing potassium nutrient status. These findings thus have significant implications for agricultural management. [ABSTRACT FROM AUTHOR]

Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPARf), RUE, and biomass throughout the season, 2) to estimate lint yield using the cotton fiber index (CFI), and 3) to determine the potential use of biomass and lint yield models for estimating cotton HI. An experiment was conducted during the 2021 and 2022 growing seasons in Tifton, Georgia, USA in randomized complete block design with five different nitrogen treatments. Different nitrogen treatments were applied to generate substantial variability in canopy development and yield. UAV imagery was collected bi-weekly along with light interception and biomass measurements throughout the season, and 20 different vegetation indices (VIs) were computed from the imagery. Generalized linear regression was performed to develop models using VIs and growing degree days (GDDs). The IPARf models had R2 values ranging from 0.66 to 0.90, and models based on RVI and RECI explained the highest variation (93%) in IPARf during cross-validation. Similarly, cotton above-ground biomass was best estimated by models from MSAVI and OSAVI. Estimation of RUE using actual biomass measurement and RVI-based IPARf model was able to explain 84% of variation in RUE. CFI from UAV-based RGB imagery had strong relationship (R2 = 0.69) with machine harvested lint yield. The estimated HI from CFI-based lint yield and MSAVI-based biomass models was able to explain 40 to 49% of variation in measured HI for the 2022 growing season. The models developed to estimate the yield-contributing physiological parameters in cotton showed low to strong performance, with IPARf and above-ground biomass having greater prediction accuracy. Future studies on accurate estimation of lint yield is suggested for precise cotton HI prediction. This study is the first attempt of its kind and the results can be used to expand and improve research on predicting functional yield drivers of cotton. [ABSTRACT FROM AUTHOR]

In the context of a changing environment, understanding the interaction between vegetation and climate is crucial for assessing, predicting, and adapting to future changes in different vegetation types. Vegetation exhibits high sensitivity to external environmental factors, making this understanding particularly significant. This study utilizes geospatial analysis techniques, such as geographic information systems, to investigate vegetation dynamics based on remote sensing data and climatic variables, including annual air temperature, annual precipitation, and annual solar radiation. The research methodology encompasses data collection, processing, and analysis, incorporating multispectral imagery and multilayered maps of various parameters. The calculation of the normalized difference vegetation index serves to evaluate changes in vegetation cover, identify areas experiencing variations in green biomass, and establish strategies for the future development of different vegetation types. During the period from 2001 to 2022, the average normalized difference vegetation index value in the Southeastern Crimea region amounted to 0.443. The highest average values were recorded in the year 2006, reaching a magnitude of 0.469. Conversely, the lowest values were observed in the years 2001–2002, constituting 0.397. It has been ascertained that an overarching positive trend in the evolution of NDVI values from 2001 to 2022 is apparent, thus implying a notable augmentation in vegetative biomass. However, adversarial trends manifest in discrete locales adjacent to the cities of Sudak and Feodosia, along with the coastal stretches of the Black Sea. Correlation analysis is employed to establish relationships between vegetation changes and climatic indicators. The findings contribute to our understanding of the vulnerability of various vegetation types and ecosystems in the Southeastern Crimea region. The obtained data provide valuable insights for the development of sustainable vegetation resource management strategies and climate change adaptation in the region. [ABSTRACT FROM AUTHOR]

Crop yield prediction and mapping are essential for crop management and decision-making. The development of unmanned aerial vehicles (UAVs) accelerates the acquisition of high-resolution imagery for the evaluation and monitoring of crop growth and development. In this study, a cost-effective yield mapping workflow was developed in a commercial setting. Two UAVs were deployed to rapidly obtain RGB and multispectral images of spinach at different growth stages in multiple spinach fields to assess crop physiological attributes in the field environment. Based on the imagery, an image processing workflow for farm field orthoimage rotation and segmentation was proposed. Then, twelve vegetation indices (VIs) were extracted from the processed images. The most robust VIs were selected by comparing the correlations among the VIs and yields and regression tree and stepwise multiple linear regression. Excess Green Index (ExG) and Normalized Difference Vegetation Index (NDVI) were identified as two of the most robust VIs for predicting spinach yield at various stages of growth. A linear regression model was developed using 45 manually harvested calibration locations and a coefficient of determination (R2) of 0.98. The root mean squared error (RMSE) was 0.19 kg/m with a mean absolute percentage error (MAPE) of 9.0 % when using a validation dataset. The produced yield maps provided the basis for a farm harvesting decision-making process and for a general crop management strategy.

AbstractSpatio-temporal dynamics of surface water reservoirs at regional and global scales remain poorly understood. Using satellite remote sensing provides a unique opportunity to address this problem. This study aims to (1) quantify the extent and volume of surface water and (2) compare our approach with other datasets. We utilized MODIS-based spectral indices to monitor temporal variations in inundation extent. By interpolating water levels across the surface water extent, we generated water level maps and quantified surface water volume from 2000 to 2022. Evaluation against ICESat-2 data involved 64 comparisons, with approximately 58% showing an R2 value greater than or equal to 0.5, and 38% were higher than or equal to 0.7. Compared to the flooding model, our method aligns more closely with ICESat-2 data, contrary to the flooding model which tends to overestimate water levels and, consequently, water storage.

Remote sensing (RS) is often employed to estimate suspended sediment concentration (SSC) in rivers, and the availability of hyperspectral imagery enhances the effectiveness of RS-based water quality monitoring due to its high spectral resolution. Yet, the necessity of hyperspectral imagery for SSC estimation in rivers has not been fully validated. This study thus compares the performance of hyperspectral RS with that of multispectral RS by conducting field-scale experiments in shallow rivers. In the field experiments, we measured radiance from a water body mixed with suspended sediments using a drone-mounted hyperspectral sensor, with the sediment and riverbed types considered as controlling factors. We retrieved the SSC from UAV imagery using an optimal band ratio analysis, which successfully estimated SSC distributions in the sand bed conditions with both multispectral and hyperspectral data. In the vegetated bed conditions, meanwhile, the prediction accuracy decreased significantly due to the temporally varying bottom reflectance associated with the random movement of vegetation caused by near-bed turbulence. This is because temporally inhomogeneous bottom reflectance distorts the relationship between the SSC and total reflectance. Nevertheless, the hyperspectral imaging exhibited better prediction accuracy than the multispectral imaging, effectively extracting optimal spectral bands sensitive to back-scattered reflectance from sediments while constraining the bottom reflectance caused by the vegetation-covered bed.

This study examines the application of unsupervised classification techniques in the mapping of forest vegetation, aiming to align vegetation cover with the Braun-Blanquet classification system through remote sensing. By leveraging Landsat 8 and 9 satellite imagery and advanced clustering algorithms, specifically the Weka X-Means, this research addresses the challenge of minimizing researcher subjectivity in vegetation mapping. The methodology incorporates a two-step clustering approach to accurately classify forest communities, utilizing a comprehensive set of vegetation indices to distinguish between different types of forest ecosystems. The validation of the classification model relied on a detailed analysis of over 17,000 relevés from the “Flora” database, ensuring a high degree of accuracy in matching satellite-derived vegetation classes with field observations. The study’s findings reveal the successful identification of 44 forest community types that was aggregated into seven classes of Braun-Blanquet classification system, demonstrating the efficacy of unsupervised classification in generating reliable vegetation maps. This work not only contributes to the advancement of remote sensing applications in ecological research, but also provides a valuable tool for natural resource management and conservation planning. The integration of unsupervised classification with the Braun-Blanquet system presents a novel approach to vegetation mapping, offering insights into ecological characteristics, and can be good starter point for sequestration potential of forest communities’ assessment in the Republic of Tatarstan.

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

The unmanned aerial vehicle (UAV), as a remote sensing platform, has attracted many researchers in precision agriculture because of its operational flexibility and capability of producing high spatial and temporal resolution images of agricultural fields. This study proposed machine learning (ML) models and their ensembles for peanut yield prediction using UAV multispectral data. We utilized five bands (red, green, blue, near-infra-red (NIR) and red-edge) multispectral images acquired at various growth stages of peanuts using UAV. The correlation between spectral bands and yield was analyzed for each growth stage, which showed that the maturity stages had a significant correlation between peanut yield and spectral bands: red, green, NIR and red edge (REDE). Using these four bands spectral data, we assessed the potential for peanut yield prediction using multiple linear regression and seven non-linear ML models whose hyperparameters were optimized using simulated annealing (SA). The best three ML models, random forest (RF), support vector machine (SVM) and XGBoost, were then selected to construct a cooperative yield prediction framework with both the best ML model and the ensemble scheme from the best three as comparable recommendations to the farmers.

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R2 = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R2 =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.