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Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working schedule. In precision agriculture, choosing the opportunity and amount of farm inputs is the critical part, which will improve the yield and decrease the cost. The potato canopy coverage and plant height were quickly extracted, which could be used to estimate the spraying volume using the evaluation model obtained by indoor tests. The vegetation index approach was used to extract potato canopy coverage, and the color point cloud data method at different height rates was formed to estimate the plant height of potato at different growth stages. The original data were collected using a low-cost UAV, which was mounted on a high-resolution RGB camera. Then, the Structure from Motion (SFM) algorithm was used to extract the 3D point cloud from ordered images that could form a digital orthophoto model (DOM) and sparse point cloud. The results show that the vegetation index-based method could accurately estimate canopy coverage. Among EXG, EXR, RGBVI, GLI, and CIVE, EXG achieved the best adaptability in different test plots. Point cloud data could be used to estimate plant height, but when the potato coverage rate was low, potato canopy point cloud data underwent rarefaction; in the vigorous growth period, the estimated value was substantially connected with the measured value (R2 = 0.94). The relationship between the coverage area of spraying on potato canopy and canopy coverage was measured indoors to form the model. The results revealed that the model could estimate the dose accurately (R2 = 0.878). Therefore, combining agronomic factors with data extracted from the UAV RGB image had the ability to predict the field spraying volume.

The Miyanaka Intake Dam fishway underwent improvements in 2012, and we established a new rock-ramp fishway called the Seseragi Fishway, cognizant of its utility as a passage and a habitat for bottom-dwelling and small fish with weak swimming ability. However, the fishway is occasionally submerged by floods, causing sediment accumulation that leads to changes in the vegetation composition. In addition, the arrival and inflow of seeds from upstream and the surrounding areas result in vegetation changes. In this study, the inside and outside of the rock-ramp fishway were divided into eight areas, and the vegetation succession after 2012 was determined. A correlation was observed between the results of fish catch surveys during the same period and the vegetation. Based on these results, we reported on the process of steadily operating the rock-ramp fishway while devising and improving specific management methods. Changes in vegetation, such as an increase in upright vegetation and a decrease in flow-obstructing vegetation, contributed to an increase in the population of bottom-dwellers, weak swimmers, and juvenile fish. The existence and management of appropriate vegetation are important for maintaining fishways inhabited by a variety of fish species.

【Background and Objective】Topsoil water content is a critical factor influencing crop growth and yield, yet traditional measurement methods are often limited in efficiency and scalability. UAV-based remote sensing provides a promising alternative for rapid, high-resolution in situ measurements. This paper evaluates the factors that affect the accuracy of UAV-based soil water content inversion and identifies the optimal combinations of data types, features, and modelling approaches for improving the accuracy of the UAV-based method.【Method】The experiment was conducted in a maize field during its early growth stage, characterized by substantial variation in canopy coverage. UAV imageries and ground-truth measurements were collected simultaneously. A threshold method was applied to remove the influence of soil background information and calculate vegetation coverage. Spectral and texture features were extracted, and vegetation coverage was integrated into different data combination patterns. Three regression methods: random forest regression, ridge regression and partial least squares regression, were used to construct the inversion model for estimating topsoil water content; comparison of their performance was analyzed under different scenarios.【Result】① The effect of background information removal on model accuracy varied with regression method and the data extracted from sensors. In particular, inversion accuracy improved after soil background information removal for RGB sensors but decreased for TIR sensors. ② The combination of visible and thermal infrared data significantly improved model accuracy, providing richer information and improving robustness. ③ Incorporating vegetation coverage improved accuracy of the predicted topsoil water content both with and without background information removal. For datasets without background information removal, the R2 of the methods using the RGB+TIR+FVC pattern increased by 0.01 compared to that of using the RGB+TIR pattern. After background information removal, their R2 increased by 0.11.【Conclusion】Our results show that different data combinations and inclusion of vegetation coverage had varying effects on the accuracy of UAV-based method for topsoil water content estimation. We screened optimal combinations and methods to increase the accuracy of the method for estimating topsoil water content in the early maize growing stage.

Due to the steep slope of mountainous watersheds and large changes in vegetation coverage degree, flood response processes after rainstorms are complicated. The flow concentration time of the slope is a key parameter for the simulation of flood processes. The most widely used flow concentration time formula currently in the distributed hydrological model is T = L0.6n0.6i−0.4S−0.3, which is derived from the kinematic wave theory (Melesse and Graham in J Am Water Resour As 40(4):863–879, 2004; Lee in Hydrol Sci 53(2):323–337, 2008). The flow confluence time T is characterized by the constant exponent of the slope length L, roughness n, effective rainfall intensity i and slope S, and the influence of vegetation on the flow concentration time is implied by the roughness. In this study, a series of heavy rainfall slope surface confluence tests under different slopes and vegetation coverage were carried out, a vegetation coverage factor, C, which was introduced, a statistical analysis method was used, and the vegetation coverage index was fitted. The results showed that the types of vegetation have a certain influence on the flow concentration time of slope, and the flow confluence time under turf vegetation was larger than the flow confluence time under shrubs vegetation; especially in the slope of the larger slope, the relative impact is more significant; at the same time, the influence of vegetation coverage on the flow concentration time of slope was more significant; no matter the condition of turf or shrub, the slope confluence time increased obviously with the increase in vegetation coverage. The index of vegetation coverage factor C varied with the slope and rain intensity. In general, the index of vegetation coverage factor C increased with the decrease in slope and decreased with the increase in rain intensity. In regard to the turf vegetation coverage index, when the slope is 45° and 30°, the decreasing trend of the vegetation coverage index a0 is obvious with increasing rainfall intensity. When the slope is 15°, the vegetation coverage index a0 also decreases with increasing rainfall intensity. When the slope is 5°, the vegetation coverage index a0 basically has no change. In regard to the shrubs vegetation coverage index, when the slope is 45° and 30°, the decreasing trend of the vegetation coverage index a0 is obvious with increasing rainfall intensity. When the slope is 15°, the vegetation coverage index a0 also decreases with increasing rainfall intensity. When the slope is 5°, the vegetation coverage index a0 basically has no change.

The mountainous vegetation is important to regional sustainable development. However, the topographic effect is the main obstacle to the monitoring of mountainous vegetation using remote sensing. Aiming to retrieve the reflectance of frequently-used red–green–blue and near-infrared (NIR) wavebands of rugged mountains for vegetation mapping, we developed a new integrated topographic correction (ITC) using the SCS + C correction and the shadow-eliminated vegetation index. The ITC procedure consists of image processing, data training, and shadow correction and uses a random forest machine learning algorithm. Our study using the Landsat 8 Operational Land Imager (OLI) multi-spectral images in Fujian province, China, showed that the ITC achieved high performance in topographic correction of regional mountains and in transferability from the sunny area of a scene to the shadow area of three scenes. The ITC-corrected multi-spectral image with an NIR–red–green composite exhibited flat features with impressions of relief and topographic shadow removed. The linear regression of corrected waveband reflectance vs. the cosine of the solar incidence angle showed an inclination that nearly reached the horizontal, and the coefficient of determination decreased to 0.00~0.01. The absolute relative errors of the cast shadow and the self-shadow all dramatically decreased to the range of 0.30~6.37%. In addition, the achieved detection rate of regional vegetation coverage for the three cities of Fuzhou, Putian, and Xiamen using the ITC-corrected images was 0.92~6.14% higher than that using the surface reflectance images and showed a positive relationship with the regional topographic factors, e.g., the elevation and slope. The ITC-corrected multi-spectral images are beneficial for monitoring regional mountainous vegetation. Future improvements can focus on the use of the ITC in higher-resolution imaging.

With the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image information combined from two different sensors mounted on an UAV to evaluate leaf nitrogen content (LNC) in corn. Field experiments with corn were conducted using different nitrogen rates and cultivars at the National Precision Agriculture Research and Demonstration Base in China in 2017. Digital RGB and multispectral images were obtained synchronously by UAV in the V12, R1, and R3 growth stages of corn, respectively. A novel family of modified vegetation indices, named coverage adjusted spectral indices (CASIs (CASI =VI/1+FVcover, where VI denotes the reference vegetation index and FVcover refers to the fraction of vegetation coverage), has been introduced to estimate LNC in corn. Thereby, typical VIs were extracted from multispectral images, which have the advantage of relatively higher spectral resolution, and FVcover was calculated by RGB images that feature higher spatial resolution. Then, the PLS (partial least squares) method was employed to investigate the relationships between LNC and the optimal set of CASIs or VIs selected by the RFA (random frog algorithm) in different corn growth stages. The analysis results indicated that whether removing soil noise or not, CASIs guaranteed a better estimation of LNC than VIs for all of the three growth stages of corn, and the usage of CASIs in the R1 stage yielded the best R2 value of 0.59, with a RMSE (root mean square error) of 22.02% and NRMSE (normalized root mean square error) of 8.37%. It was concluded that CASIs, based on the fusion of information acquired synchronously from both lower resolution multispectral and higher resolution RGB images, have a good potential for crop nitrogen monitoring by UAV. Furthermore, they could also serve as a useful way for assessing other physical and chemical parameters in further applications for crops.

The Yellow River Basin (YRB) is a fundamental ecological barrier in China and is one of the regions where the ecological environment is relatively fragile. Studying the spatio-temporal variations in vegetation coverage in the YRB and their driving factors through a long-time-series vegetation dataset is of great significance to eco-environmental construction and sustainable development in the YRB. In this study, we sought to characterize the spatio-temporal variation in vegetation coverage and its climatic driving factors in the YRB from 2001 to 2020 by constructing a new kernel normalized difference vegetation index (kNDVI) dataset based on MOD13 A1 V6 data from the Google Earth Engine (GEE) platform. Using Theil–Sen median trend analysis, the Mann–Kendall test, and the Hurst exponent, we investigated the spatio-temporal variation characteristics and future development trends of the vegetation coverage. The climatic driving factors of vegetation coverage in the YRB were obtained via partial correlation analysis and complex correlation analysis of the associations between kNDVI and both temperature and precipitation. The results reveal the following: The spatial distribution pattern of kNDVI in the YRB showed that vegetation coverage was high in the southeast and low in the northwest. Vegetation coverage fluctuated from 2001 to 2020, with a main significant trend of increasing growth at a rate of 0.0995/5a. The response of vegetation to climatic factors was strong in the YRB, with a stronger response to precipitation than to temperature. Additionally, the main driving factors of vegetation coverage in the YRB were found to be non-climatic factors, which were mainly distributed in Henan, southern Shaanxi, Shanxi, western Inner Mongolia, Ningxia, and eastern Gansu. The areas driven by climatic factors were mainly distributed in northern Shaanxi, Shandong, Qinghai, western Gansu, northeastern Inner Mongolia, and Sichuan. Our findings have implications for ecosystem restoration and sustainable development in the YRB.

Vegetation is a natural tie that connects the atmosphere, hydrosphere, biosphere, and pedosphere. Quantitatively evaluating the variability of vegetation coverage and exploring its associated influence factors are essential for ecological security and sustainable economic development. In this paper, the spatiotemporal variation of vegetation coverage and its response to climatic factors and land use change were investigated in the Yangtze River Delta (YRD) from 2001 to 2015, based on normalized difference vegetation index (NDVI) data, vegetation type data, climate data, and land use/cover change (LUCC) data. The results indicated that the annual mean vegetation coverage revealed a nonsignificant decreasing trend over the whole YRD. Areas characterized by significant decreasing ( P < 0.05) trends were mainly concentrated on the central and northern part of the YRD, and significant increasing ( P < 0.05) trends were mainly located in the southern part of the study area. Except for grassland and cultivated crops, vegetation coverage of the other types of vegetation was all exhibiting increasing trends. Temperature has a more pronounced impact on vegetation growth than precipitation at both the annual and monthly scales. Furthermore, vegetation growth exhibited a time lag effect for 1~2 months in response to precipitation, while there was no such phenomenon with temperature. Land use change caused by urbanization is an important driving factor for the decrease of vegetation coverage in the YRD, and the effect of land use change on the vegetation dynamic should not be overlook.

Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study, 19 vegetation indices were calculated for 30 unmanned aerial vehicle (UAV) visible light images at five grades of grassland desertification in the Mu Us Sandy. Fractional Vegetation Coverage (FVC) with high accuracy was obtained through Support Vector Machine (SVM) classification, and the results were used as the reference values. Based on the FVC, the grassland desertification grades were divided into five grades: severe (FVC < 5%), high (FVC: 5–20%), moderate (FVC: 21–50%), slight (FVC: 51–70%), and non-desertification (FVC: 71–100%). The accuracy of the vegetation indices was assessed by the overall accuracy (OA), the kappa coefficient (k), and the relative error (RE). Our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. Excess Green Red Blue Difference Index (EGRBDI), Visible Band Modified Soil Adjusted Vegetation Index (V-MSAVI), Green Leaf Index (GLI), Color Index of Vegetation Vegetative (CIVE), Red Green Blue Vegetation Index (RGBVI), and Excess Green (EXG) accurately assessed grassland desertification at severe, high, moderate, and slight grades. In addition, the Red Green Ratio Index (RGRI) and Combined 2 (COM2) were accurate in assessing severe desertification. The assessment of the 19 indices of the non-desertification grade had low accuracy. Moreover, our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. This study emphasizes that the applicability of the vegetation indices varies with the degree of grassland desertification and hopes to provide scientific guidance for a more accurate grassland desertification assessment.

With the rapid urban development in China, urbanization has brought more and more pressure on the ecological environment. As one of the most dynamic, open, and innovative regions in China, the eco-environmental issues in the Yangtze River Delta have attracted much attention. This paper takes the central region of the Yangtze River Delta as the research object, through building the index system of urbanization and ecological environment based on statistical data and two new indicators (fraction of vegetation coverage and surface urban heat island intensity) extracted from remote sensing images, uses the Entropy-TOPSIS method to complete the comprehensive assessment, and then analyzes the coupling coordination degree between the urbanization and ecological environment and main obstacle factors. The results showed that the coupling coordination degree in the study region generally shows an upward trend from 0.604 in 2008 to 0.753 in 2017, generally changing from an imbalanced state towards a basically balanced state. However, regional imbalance of urbanization and ecological environment always exists, which is mainly affected by social urbanization, economic urbanization, landscape urbanization, pollution loading and resource consumption. Finally, on the basis of the obstacle factor analysis, some specific suggestions for promoting the coordinated development of the Yangtze River Delta are put forward.