Biomass and vegetation coverage survey in the Mu Us sandy land - based on unmanned aerial vehicle RGB images

The goal of this research was to study and optimize multi-temporal RGB images obtained by a UAV (unmanned aerial vehicle). A digital camera onboard the UAV allows obtaining data with a high temporal and spatial resolution of ground objects. In the case considered by us, the object of study is agricultural fields, for which, based on numerous images covering the agricultural field, image mosaics (orthomosaics) are constructed. The acquisition time for each orthomosaic takes at least several hours, which imposes a change in the illuminance of each image, when considered separately. Orthomosaics obtained in different periods of the year (several months) will also differ from each other in terms of illuminance. For a comparative analysis of different parts of the field (orthomosaic), obtained in the same time interval or comparison of areas for different periods of time, their alignment by illumination is required. Currently, the majority of alignment approaches rely rather on colour (RGB) methods, which cannot guarantee finding efficient solutions, especially when it is necessary to obtain a quantitative result. In the paper, a new method is proposed that takes into account the change in illuminance during the acquisition of each image. The general formulation of the problem of light correction of RGB images in terms of assessing the colour vegetation index Greenness is considered. The results of processing real measurements are presented.

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AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

Remote Sensing ◽

10.3390/rs11121413 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1413 ◽

Cited By ~ 8

Author(s):

Víctor González-Jaramillo ◽

Andreas Fries ◽

Jörg Bendix

Keyword(s):

High Resolution ◽

Unmanned Aerial Vehicle ◽

Tree Level ◽

Mountain Forest ◽

Multispectral Images ◽

Individual Tree ◽

Tropical Mountain ◽

Aerial Vehicle ◽

Rgb Images ◽

Very High

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

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Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

Remote Sensing ◽

10.3390/rs13061187 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1187

Author(s):

Rubén Rufo ◽

Jose Miguel Soriano ◽

Dolors Villegas ◽

Conxita Royo ◽

Joaquim Bellvert

Keyword(s):

Bread Wheat ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Agronomic Traits ◽

Stepwise Multiple Regression ◽

Area Index ◽

Rainfed Conditions ◽

Aerial Vehicle ◽

High Throughput Phenotyping ◽

Rgb Images

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.

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Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat

Remote Sensing ◽

10.3390/rs11222667 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2667 ◽

Cited By ~ 2

Author(s):

Jiang ◽

Cai ◽

Zheng ◽

Cheng ◽

Tian ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Near Infrared ◽

Vegetation Indices ◽

Crop Growth ◽

Digital Cameras ◽

Status Monitoring ◽

Aerial Vehicle ◽

Rgb Images ◽

N Status

Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.

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Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice

Remote Sensing ◽

10.3390/rs13010084 ◽

2020 ◽

Vol 13 (1) ◽

pp. 84

Author(s):

Tomoaki Yamaguchi ◽

Yukie Tanaka ◽

Yuto Imachi ◽

Megumi Yamashita ◽

Keisuke Katsura

Keyword(s):

Deep Learning ◽

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Regression Models ◽

Estimation Model ◽

Area Index ◽

Aerial Vehicle ◽

Rgb Images ◽

Multispectral Camera

Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R2 = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.

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Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera

Semina Ciências Agrárias ◽

10.5433/1679-0359.2019v40n1p49 ◽

2019 ◽

Vol 40 (1) ◽

pp. 49 ◽

Cited By ~ 7

Author(s):

Adnane Beniaich ◽

Marx Leandro Naves Silva ◽

Fabio Arnaldo Pomar Avalos ◽

Michele Duarte de Menezes ◽

Bernardo Moreira Cândido

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Cover ◽

Vegetation Indices ◽

Jack Bean ◽

Photographic Camera ◽

Frequent Monitoring ◽

Relevant Role ◽

Aerial Vehicle ◽

Crop Cycle ◽

Rgb Images

The permanent monitoring of vegetation cover is important to guarantee a sustainable management of agricultural activities, with a relevant role in the reduction of water erosion. This monitoring can be carried out through different indicators such as vegetation cover indices. In this study, the vegetation cover index was obtained using uncalibrated RGB images generated from a digital photographic camera on an unmanned aerial vehicle (UAV). In addition, a comparative study with 11 vegetation indices was carried out. The vegetation indices CIVE and EXG presented a better performance and the index WI presented the worst performance in the vegetation classification during the cycles of jack bean and millet, according to the overall accuracy and Kappa coefficient. Vegetation indices were effective tools in obtaining soil cover index when compared to the standard Stocking method, except for the index WI. Architecture and cycle of millet and jack bean influenced the behavior of the studied vegetation indices. Vegetation indices generated from RGB images obtained by UAV were more practical and efficient, allowing a more frequent monitoring and in a wider area during the crop cycle.

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Vegetation Coverage change and its influencing factors in the Mu Us Sandy Land from 2000 to 2015

10.5194/egusphere-egu2020-8545 ◽

2020 ◽

Author(s):

Yan Gao

Keyword(s):

Influencing Factors ◽

Annual Growth ◽

Annual Precipitation ◽

Vegetation Coverage ◽

Sandy Land ◽

Annual Average ◽

Growth Season ◽

Mu Us Sandy Land ◽

Average Temperature ◽

Annual Average Temperature

<p>Introducing and establishing sand-binding vegetation, as one of important approaches for combating desertification, has already applied in the ecological restoration and recovery in Mu Us sandy land for more than 60 years. Study on the dynamics of vegetation coverage in Mu Us Sandy Land and its influencing factors is thus a crucial requirement for guiding and establishing sand-binding vegetation. Based on MOD13A2 NDVI time-series data from 2000 to 2015&#65292;annual average temperature, annual precipitation, annual growth season precipitation, the land-use/land-cover (LULC) data, and topographic data, explored its dynamics during 2000&#8211;2015 and detected their influencing factors by the geo-detector method. The results showed that: (1) the vegetation coverage decrease from east to west in the Mu Us sandy land; (2) from 2000 to 2015&#65292;the vegetation coverage in the Mu Us sandy land has been increasing generally, the growth rate was 0.006 /a; (3) the number of pixels with significant increase in vegetation coverage accounted for 33.24% of the study area, meanwhile there was obvious spatial difference, the areas with significant or extremely significant increase of vegetation coverage were mainly distribute in eastern parts; (4) the main influencing factors of vegetation coverage change were annual precipitation, annual growth season precipitation, annual average temperature and LULC. Results indicate that, the influence of climate factors on Mu Us sandy land vegetation coverage was higher than LULC. It is necessary to put forward a suitable vegetation restoration plan under the projected climate change.</p>

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Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters

Sustainability ◽

10.3390/su11236829 ◽

2019 ◽

Vol 11 (23) ◽

pp. 6829 ◽

Cited By ~ 1

Author(s):

Umut Hasan ◽

Mamat Sawut ◽

Shuisen Chen

Keyword(s):

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Precision Agriculture ◽

Area Index ◽

Aerial Vehicle ◽

Rgb Images ◽

Jointing Stage ◽

Rgb Image

The leaf area index (LAI) is not only an important parameter for monitoring crop growth, but also an important input parameter for crop yield prediction models and hydrological and climatic models. Several studies have recently been conducted to estimate crop LAI using unmanned aerial vehicle (UAV) multispectral and hyperspectral data. However, there are few studies on estimating the LAI of winter wheat using unmanned aerial vehicle (UAV) RGB images. In this study, we estimated the LAI of winter wheat at the jointing stage on simple farmland in Xinjiang, China, using parameters derived from UAV RGB images. According to gray correlation analysis, UAV RGB-image parameters such as the Visible Atmospherically Resistant Index (VARI), the Red Green Blue Vegetation Index (RGBVI), the Digital Number (DN) of Blue Channel (B) and the Green Leaf Algorithm (GLA) were selected to develop models for estimating the LAI of winter wheat. The results showed that it is feasible to use UAV RGB images for inverting and mapping the LAI of winter wheat at the jointing stage on the field scale, and the partial least squares regression (PLSR) model based on the VARI, RGBVI, B and GLA had the best prediction accuracy (R2 = 0.776, root mean square error (RMSE) = 0.468, residual prediction deviation (RPD) = 1.838) among all the regression models. To conclude, UAV RGB images not only have great potential in estimating the LAI of winter wheat, but also can provide more reliable and accurate data for precision agriculture management.

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Vegetation Coverage Monitoring in Mu-us Sandy Land Based on Multiscale Remote Sensing Data-A Case Study of Yanchi County, Ningxia

Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017) ◽

10.2991/iccia-17.2017.73 ◽

2017 ◽

Author(s):

Zhen Bian ◽

Wanxue You ◽

Dian Yu ◽

Kebin Zhang ◽

Limeng Meng ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Vegetation Coverage ◽

Sandy Land ◽

Mu Us Sandy Land ◽

Sensing Data ◽

Yanchi County

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Correlation Assessment of NDVI and Land use Dynamics with Water Resources for the Southern Margin of Mu Us Sandy Land, China

10.21203/rs.3.rs-483664/v1 ◽

2021 ◽

Author(s):

Menglong Zhao ◽

Yu Wang ◽

Siyuan Liu ◽

Ping-an Zhong ◽

Hongzhen Liu ◽

...

Keyword(s):

Water Resources ◽

Water Consumption ◽

Water Shortage ◽

Vegetation Coverage ◽

Causal Relationships ◽

Sandy Land ◽

Mu Us Sandy Land ◽

Southern Margin ◽

Land Water ◽

Artificial Vegetation

Abstract To prevent desertification, countries all over the world have made diversified efforts and vegetation restoration has been proved to be an effective approach. However, for sandy land that has limited water resources, measures such as artificial vegetation, may lead to the increase risk of drought. While affirming the achievements of sand utilization, there are many controversies exist regarding the advantages of turning deserts green, especially considering the water scarcity. Therefore, the long-run and causal relationships between sandy land, water consumption and vegetation coverage are necessary for explorations. Taken the southern margin of the Mu Us Sandy Land as the study area, this study explored the interactions between sandy land, water consumption and NDVI over a period of 2000–2018 with a VAR model approach. In the study area, various revegetation projects have made great achievements, resulting in a significant reduction of the sandy land area. In addition, the NDVI has ascend from 0.196 in 2000 to 0.371 in 2018 with a ratio of 89.3%. Results showed that there exist long-term stable equilibrium and causal relationships between water consumption with sandy land and NDVI. The increase of NDVI is relatively the direct factor causes the increase of water consumption. It could be inferred that those artificial vegetation measures may be based on large amount of water consumption, which may aggravate further water shortage and ecological damage. More scientific and stronger water resources management measures need to be implemented locally to achieve a balance between water resources and revegetation.

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