Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

Soybean seedling detection and counting from UAV images based on an improved YOLOv8 Network

The utilization of unmanned aerial vehicle (UAV) for soybean seedling detection is an effective way to estimate soybean yield, which plays a crucial role in agricultural planning and decision-making. However, the soybean seedlings objects in the UAV image are small, in clusters, and occluded each other, which makes it very challenging to achieve accurate object detection and counting. To address these issues, we optimize the YOLOv8 model and propose a GAS-YOLOv8 network, aiming to enhance the detection accuracy for the task of soybean seedling detection based on UAV images. Firstly, a global attention mechanism (GAM) is incorporated into the neck module of YOLOv8, which reallocates weights and prioritizes global information to more effectively extract soybean seedling features. Secondly, the CIOU loss function is replaced with the SIOU loss, which includes an angle loss term to guide the regression of bounding boxes. Experimental results show that, on the soybean seedling dataset, the proposed GAS-YOLOv8 model achieves a 1.3% improvement in [email protected] and a 6% enhancement in detection performance in dense seedling areas, when compared to the baseline model YOLOv8s.When compared to other object detection models (YOLOv5, Faster R-CNN, etc.), the GAS-YOLOv8 model similarly achieved the best detection performance. These results demonstrate the effectiveness of the GAS-YOLOv8 in detecting dense soybean seedlings, providing more accurate theoretical support for subsequent yield estimation

Crop leaves high-resolution images analysis and segmentation by a convolutional neural network under small sampling condition

The authors propose an algorithm for analysing and segmenting high-resolution images of cultivated plant leaves by a convolutional neural network of deep learning in conditions of small samples. The algorithm implemented in the hardware and software complex includes images preprocessing procedures with the elimination of distortions if they are present, data augmentation to increase the number of variations, classification of signs by textural characteristics in order to identify diseases with subsequent segmentation of images of affected leaves

Effect of the Red-Edge Band from Drone Altum Multispectral Camera in Mapping the Canopy Cover of Winter Wheat, Chickweed, and Hairy Buttercup

The detection and mapping of winter wheat and the canopy cover of associated weeds, such as chickweed and hairy buttercup, are essential for crop and weed management. With emerging drone technologies, the use of a multispectral camera with the red-edge band, such as Altum, is commonly used for crop and weed mapping. However, little is understood about the contribution of the red-edge band in mapping. The aim of this study was to examine the addition of the red-edge band from a drone with an Altum multispectral camera in improving the detection and mapping of the canopy cover of winter wheat, chickweed, and hairy buttercup. The canopy cover of winter wheat, chickweed, and hairy buttercup were classified and mapped with the red-edge band inclusively and exclusively using a random forest classification algorithm. Results showed that the addition of the red-edge band increased the overall mapping accuracy of about 7%. Furthermore, the red-edge wavelength was found to better detect winter wheat relative to chickweed and hairy buttercup. This study demonstrated the usefulness of the red-edge band in improving the detection and mapping of winter wheat and associated weeds (chickweed and hairy buttercup) in agricultural fields

Automatic features detection in a fluvial environment through machine learning techniques based on uavs multispectral data

The present work aims to demonstrate how machine learning (ML) techniques can be used for automatic feature detection and extraction in fluvial environments. The use of photogrammetry and machine learning algorithms has improved the understanding of both environmental and an-thropic issues. The developed methodology was applied considering the acquisition of multiple photogrammetric images thanks to unmanned aerial vehicles (UAV) carrying multispectral cam-eras. These surveys were carried out in the Salbertrand area, along the Dora Riparia River, situated in Piedmont (Italy). The authors developed an algorithm able to identify and detect the water table contour concerning the landed areas: the automatic classification in ML found a valid identification of different patterns (water, gravel bars, vegetation, and ground classes) in specific hydraulic and geomatics conditions. Indeed, the RE+NIR data gave us a sharp rise in terms of accuracy by about 11% and 13.5% of F1-score average values in the testing point clouds compared to RGB data. The obtained results about the automatic classification led us to define a new procedure with precise validity conditions

Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data

Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests

Towards 3D Crop Phenotyping of Above- and Belowground Grain Sorghum Plant Traits Using Airborne Mounted Multispectral Camera, TLS, and GPR Remote Sensing Technologies

Remote sensing technologies are increasingly being used in agriculture with a focus on crop phenotyping and nondestructive plant health monitoring. Climate change in the western United States has led to increasing aridity, and reduced precipitation and thus a growing need for emerging varieties of drought-tolerant crops and technology to accurately monitor their establishment. Remote sensing technologies have the capability of efficiently fulfilling this need. The utilization of terrestrial laser scanning (TLS), ground penetrating radar (GPR) and small unoccupied aerial vehicles (sUAS) allow us to monitor and assess plant traits and health metrics. These three technologies were employed during the 2021 and 2022 growing seasons of two varieties of droughttolerant grain sorghum (Sorghum Bicolor L. Moench) under deficit irrigation. The red and white varieties of sorghum were monitored using a randomized split plot design with three levels of deficit irrigation (30%, 60%, and 100%). These plots were replicated three times for a total of 18 plots. We hypothesized that crop height and an index of plant health: the normalized difference vegetation index (NDVI), would significantly decrease and that the sorghum’s belowground biomass (BGB) would increase, as an indication of drought tolerance, in response to deficit irrigation. For both the 2021 and 2022 growing seasons, we measured changes in plant height, NDVI, and below-ground root response using a Leica C10 TLS, a MicaSense Altum multispectral camera mounted on a DJI Matrice 600 Pro sUAS, and an IDS GeoRadar RIS MF Hi-Mod dual frequency (400/900 MHz) GPR, respectively. Our hypotheses of a decrease in above-ground plant traits, i.e., height for both varieties in response to deficit irrigation treatments were confirmed. However, for NDVI, the red variety increased with decreasing water availability, and the white variety was consistent with our hypothesis. We found significant differences in the amplitude response maps, a surrogate of BGB phenology, that indicated increasing biomass with increasing deficit irrigation. However, this response was not consistent with field measures of BGB that showed the highest BGB at 60%, then 100%, and then 30%. This indicated that a 30% deficit level exceeded drought tolerance for the two varieties. These results support the use of remote sensing technologies for field plot-level plant health monitoring and crop phenotyping

Enhancing the usability of Satellite Earth Observations through Data Driven Models. An application to Sea Water Quality

Earth Observation from satellites has the potential to provide comprehensive, rapid and inexpensive information about land and water bodies. Marine monitoring could gain in effectiveness if integrated with approaches that are able to collect data from wide geographic areas, such as satellite observation. Integrated with in situ measurements, satellite observations enable to extend the punctual information of sampling campaigns to a synoptic view, increase the spatial and temporal coverage, and thus increase the representativeness of the natural diversity of the monitored water bodies, their inter-annual variability and water quality trends, providing information to support EU Member States’ action plans. Turbidity is one of the optically active water quality parameters that can be derived from satellite data, and is one of the environmental indicator considered by EU directives monitoring programmes. Turbidity is a visual property of water, related to the amount of light scattered by particles in water, and it can act as simple and convenient indirect measure of the concentration of suspended solids and other particulate material. A review of the state-of-the-art shows that most traditional methods to estimate turbidity from optical satellite images are based on semi-empirical models relying on few spectral bands. The choice of the most suitable bands to be used is often site and season specific, as it is related to the type and concentration of suspended particles. When investigating wide areas or long time series that include different optical water types, the application of machine learning algorithms seems to be promising due to their flexibility, responding to the need of a model that can adapt to varying water conditions with smooth transition, and their ability to exploit the wealth of spectral information. Moreover, machine learning models have shown to be less affected by atmospheric and other background factors. Atmospheric correction for water leaving reflectance, in fact, still remains one of the major challenges in aquatic remote sensing. The use of machine learning for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development, computing power, and availability of higher spatial resolution data. Among all existing algorithms, the choice of the complexity of the model derives from the nature and number of available data. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of a Polynomial Kernel Regularized Least Squares regression. This algorithms is characterized by a simple model structure, good generalization, global optimal solution, especially suitable for non-linear and high dimension problems. The study area is located in the North Tyrrhenian Sea (Italy), covering a coastline of about 100 km, characterized by a varied shoreline, embracing environments worthy of protection and valuable biodiversity, but also relevant ports, and three main river flow and sediment discharge. The coastal environment in this area has been monitored since 2001, according to the 2000/60/EC Water Framework Directive, and in 2008 EU Marine Strategy Framework Directive 2008/56/EC further strengthened the investigation in the area. A dataset of combination of turbidity measurements, expressed in nephelometric turbidity units (NTU), and values of the 13 spectral bands in the pixel corresponding to the sample location was used to calibrate and validate the model. The developed turbidity model shows good agreement of the estimated satellite-derived surface turbidity with the measured one, confirming that the use of ML techniques allows to reach a good accuracy in turbidity estimation from satellite Top of Atmosphere reflectance. Comparison between turbidity estimates obtained from the model with turbidity data from Copernicus CMEMS dataset named ’Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’, which was used as benchmark, produced consistent results. A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the model to catch extreme events and, overall, how it represents an important tool to improve our understanding of the complex factors that influence water quality in our oceans

Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data

A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing

Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images

Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 μg/L, MAE = 0.57 μg/L, slope = 0.94, and R2 = 0.98; Validation: N = 40, RMSE = 2.12 μg/L, MAE = 1.58 μg/L, slope = 0.91, R2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion