scholarly journals Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

2021 ◽  
Vol 13 (4) ◽  
pp. 656
Author(s):  
Xiang Zhang ◽  
Yuhai Bao ◽  
Dongliang Wang ◽  
Xiaoping Xin ◽  
Lei Ding ◽  
...  
Keyword(s):  
Major Effect ◽  
Canopy Height ◽  
Vegetation Coverage ◽  
Accurate Estimation ◽  
Grassland Vegetation ◽  
Pixel Resolution ◽  
Aerial Vehicle ◽  
Grazing Intensities ◽  
Vegetation Parameters ◽  
Mongolian Grassland

The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.

scholarly journals Tracking the Evolution of Riverbed Morphology on the Basis of UAV Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 829
Author(s):  
Teresa Gracchi ◽  
Guglielmo Rossi ◽  
Carlo Tacconi Stefanelli ◽  
Luca Tanteri ◽  
Rolando Pozzani ◽  
...  
Keyword(s):  
Management Practices ◽  
Low Cost ◽  
3D Models ◽  
Vegetation Coverage ◽  
Detection Analysis ◽  
Digital Terrain ◽  
Fluvial Dynamics ◽  
Aerial Vehicle ◽  
One Year ◽  
Short Time

Unmanned aerial vehicle (UAV) photogrammetry has recently become a widespread technique to investigate and monitor the evolution of different types of natural processes. Fluvial geomorphology is one of such fields of application where UAV potentially assumes a key role, since it allows for overcoming the intrinsic limits of satellite and airborne-based optical imagery on one side, and in situ traditional investigations on the other. The main purpose of this paper was to obtain extensive products (digital terrain models (DTMs), orthophotos, and 3D models) in a short time, with low costs and at a high resolution, in order to verify the capability of this technique to analyze the active geomorphic processes on a 12 km long stretch of the French–Italian Roia River at both large and small scales. Two surveys, one year apart from each other, were carried out over the study area and a change detection analysis was performed on the basis of the comparison of the obtained DTMs to point out and characterize both the possible morphologic variations related to fluvial dynamics and modifications in vegetation coverage. The results highlight how the understanding of different fluvial processes may be improved by appropriately exploiting UAV-based products, which can thus represent a low-cost and non-invasive tool to crucially support decisionmakers involved in land management practices.

Regulation and Critical Threshold of Grass Cover on Slope Runoff in LoessHilly Gully Region under Artificial

2021 ◽  
Author(s):  
Qiufen Zhang ◽  
Xizhi Lv ◽  
Rongxin Chen ◽  
Yongxin Ni ◽  
Li Ma
Keyword(s):  
Soil Erosion ◽  
Rainfall Intensity ◽  
Climatic Conditions ◽  
Vegetation Coverage ◽  
Critical Threshold ◽  
Runoff Generation ◽  
Grassland Vegetation ◽  
Slope Flow ◽  
The Impact ◽  
Slope Runoff

<p>The slope runoff caused by rainstorm is the main cause of serious soil and water loss in the loess hilly area, the grassland vegetation has a good inhibitory effect on the slope runoff, it is of great significance to reveal the role of grassland vegetation in the process of runoff generation and control mechanism for controlling soil erosion in this area. In this study, typical grassland slopes in hilly and gully regions of the loess plateau were taken as research objects. Through artificial rainfall in the field, the response rules of slope rainfall-runoff process to different grass coverage were explored. The results show that: (1) The time for the slope flow to stabilize is prolonged with the increase of vegetation coverage, and shortened with the increase of rainfall intensity; (2) At 60 mm·h <sup>−1</sup> rainfall intensity, the threshold of grassland vegetation coverage is 75.38%; at 90 mm·h<sup> −1</sup> rainfall intensity, the threshold of grassland vegetation coverage is 90.54%; at 120 mm·h <sup>−1</sup> rainfall intensity, the impact of grassland vegetation coverage on runoff is not significant; (3) the Reynolds number and Froude number of slope flow are 40.07‒695.22 and 0.33‒1.56 respectively, the drag coefficient is 1.42‒43.53. Under conditions of heavy rainfall, the ability of grassland to regulate slope runoff is limited. If only turf protection is considered, about 90% of grassland coverage can effectively cope with soil erosion caused by climatic conditions in loess hilly and gully regions. Therefore, in loess hilly areas where heavy rains frequently occur, grassland's protective effect on soil erosion is obviously insufficient, and investment in vegetation measures for trees and shrubs should be strengthened.</p>

Spatial Dynamic Analysis of a Mangeove Patch Based on Unmanned Aerial Vehicle: A Case Study in Pear Bay in Guangxi, China

2021 ◽  
Author(s):  
Xin Lin ◽  
Chungan Li ◽  
Mei Zhou ◽  
Wenhai Liang ◽  
Biao Li
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Dynamics ◽  
Absolute Error ◽  
Maximum Error ◽  
Canopy Height ◽  
Accurate Information ◽  
Maximum Absolute Error ◽  
Patch Area ◽  
Maximum Increase ◽  
Aerial Vehicle

Abstract This study investigated the short-term spatial variability of an mangrove patch, located in the Pearl Bay in Guangxi, China. Unmanned aerial vehicle (UAV) imagery covering the period from March 2015 to October 2017 were used and the following models were developed: two annual ultra-high resolution spatial resolution digital orthophoto maps (DOMs), two digital elevation models (DEMs), two digital surface models (DSMs), two canopy height models (CHMs), and a canopy height difference model (d-CHM). Using these models, the spatial dynamics of the extent and canopy height of the patch were analyzed. The resolution of the DOMs was 0.1 m, with an average geometrical error of 0.17 m and a maximum error of 0.44 m. The resolutions of DEMs, DSMs, CHMs, d-CHM were all 1 m. The average elevation errors of CHM in 2015 and 2017 were 0.002 m and -0.001 m, respectively, with maximum absolute errors of 0.034 m and 0.030 m, respectively. The average elevation error of d-CHM was -0.003 m and the maximum absolute error was 0.036 m, and the data quality were rated as good. From 2015 to 2017, the area of the mangrove patch increased from 8.16 ha to 8.79 ha, with an average annual increase of 3.7%. Specifically, the areas of expansion, shrinkage, and maximum seaward expansion were 6356 m2, 19 m2, and 24 m, respectively. The driving factor for the variability was natural processes. Stand canopy height exhibited a particular trend of decrease from northwest to southeast (horizontal; parallel to the seawall) and from the land to the sea (vertically; perpendicular to the seawall). From 2015 to 2017, 88.2% of the patch area showed increased canopy height, with an average increase of 0.78 m and a maximum increase of 3.2 m. In contrast, 11.8% of the patch area showed decreased canopy height with a maximum decrease of 3.1 m. The main reason for the decrease in canopy height was the death of trees caused by serious insect plagues. On the other hand, the reason for the increase in height could be attributed to the natural growth of mangrove trees, but further studies are required to verify the cause. UAV remote sensing has an incomparable advantage over traditional methods in that it provides extremely detailed and highly accurate information for in-depth study of the spatial evolution of mangrove patches, which would significantly contribute towards the protection and management of mangroves.

scholarly journals Estimation of Groundwater Evapotranspiration Using Diurnal Groundwater Level Fluctuations under Three Vegetation Covers at the Hinterland of the Badain Jaran Desert

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Wenjia Zhang ◽  
Liqiang Zhao ◽  
Xinran Yu ◽  
Lyulyu Zhang ◽  
Nai’ang Wang
Keyword(s):  
Groundwater Level ◽  
Potential Evapotranspiration ◽  
Vegetation Coverage ◽  
Accurate Estimation ◽  
Badain Jaran Desert ◽  
Arid Environments ◽  
Vegetation Types ◽  
Groundwater Evapotranspiration ◽  
Regional Water ◽  
Lake Basins

Accurate estimation of groundwater evapotranspiration (ETG) is the key for regional water budget balance and ecosystem restoration research in hyper-arid regions. Methods that use diurnal groundwater level (GWL) fluctuations have been applied to various ecosystems, especially in arid or semi-arid environments. In this study, groundwater monitoring devices were deployed in ten lake basins at the hinterland of the Badain Jaran Desert, and the White method was used to estimate the ETG of these sites under three main vegetation covers. The results showed that regular diurnal fluctuations in GWL occurred only at sites with vegetation coverage and that vegetation types and their growth status were the direct causes of this phenomenon. On a seasonal scale, the amplitudes of diurnal GWL fluctuations are related to vegetation phenology, and air temperature is an important factor controlling phenological amplitude differences. The estimation results using the White method revealed that the ETG rates varied among the observation sites with different vegetation types, and the months with the highest ETG rates were also different among the sites. Overall, ETG was 600∼900 mm at observation sites with Phragmites australis during a growing season (roughly early May to late October), 600∼650 mm in areas with Achnatherum splendens, and 500∼650 mm in areas with Nitraria tangutorum and Achnatherum splendens. Depth to water table and potential evapotranspiration jointly control the ETG rates, while the influence of these two factors varied, depending on the specific vegetation conditions of each site. This study elucidated the relationship between diurnal GWL fluctuations and vegetation in desert groundwater-recharged lake basins and expanded the application of the White method, providing a new basis for the calculation and simulation of regional water balance.

scholarly journals Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 146 ◽  
Author(s):  
Longfei Zhou ◽  
Xiaohe Gu ◽  
Shu Cheng ◽  
Guijun Yang ◽  
Meiyan Shu ◽  
...  
Keyword(s):  
Plant Height ◽  
Maize Yield ◽  
Canopy Height ◽  
Estimation Accuracy ◽  
Yield Reduction ◽  
Lidar Data ◽  
Height Estimation ◽  
Aerial Vehicle ◽  
Yield Quality ◽  
Maize Plants

Lodging stress seriously affects the yield, quality, and mechanical harvesting of maize, and is a major natural disaster causing maize yield reduction. The aim of this study was to obtain light detection and ranging (LiDAR) data of lodged maize using an unmanned aerial vehicle (UAV) equipped with a RIEGL VUX-1UAV sensor to analyze changes in the vertical structure of maize plants with different degrees of lodging, and thus to use plant height to quantitatively study maize lodging. Based on the UAV-LiDAR data, the height of the maize canopy was retrieved using a canopy height model to determine the height of the lodged maize canopy at different times. The profiles were analyzed to assess changes in maize plant height with different degrees of lodging. The differences in plant height growth of maize with different degrees of lodging were evaluated to determine the plant height recovery ability of maize with different degrees of lodging. Furthermore, the correlation between plant heights measured on the ground and LiDAR-estimated plant heights was used to verify the accuracy of plant height estimation. The results show that UAV-LiDAR data can be used to achieve maize canopy height estimation, with plant height estimation accuracy parameters of R2 = 0.964, RMSE = 0.127, and nRMSE = 7.449%. Thus, it can reflect changes of plant height of lodging maize and the recovery ability of plant height of different lodging types. Plant height can be used to quantitatively evaluate the lodging degree of maize. Studies have shown that the use of UAV-LiDAR data can effectively estimate plant heights and confirm the feasibility of LiDAR data in crop lodging monitoring.

Research on Wind Erosion of Xilamuren Grassland, Inner Mongolia

2014 ◽  
Vol 955-959 ◽  
pp. 3505-3508 ◽  
Author(s):  
Tian Ming Gao ◽  
Rui Qiang Zhang ◽  
Jian Ying Guo
Keyword(s):  
Inner Mongolia ◽  
Wind Erosion ◽  
Initial Period ◽  
Northern China ◽  
Vegetation Coverage ◽  
Available Phosphorus ◽  
Grassland Vegetation ◽  
Available Nitrogen ◽  
Vegetation Height ◽  
Degraded Grassland

In northern China, grassland has degraded severely and wind erosion occurs remarkably due to irrational land use in recent years. By employing sand sampler and mobile wind tunnel, an observation for 6 years was made to analyze the mechanisms of wind erosion in Xilamuren grassland, the central of Yinshan Mountains, Inner Mongolia. Results show that: (1) vegetation is the decisive factor for controlling wind erosion and the inhibiting effect of vegetation height on wind erosion is greater than that of vegetation coverage. (2) Wind erosion modulus in the initial period of enclosure reaches 1313.7 t km-2a-1 and with the improvement of the grassland vegetation, wind erosion decreases year by year. (3) For every 1000 kg soil eroded by wind, 15 kg organic matter, 227g available nitrogen, 262g available phosphorus and 120g available potassium lose in the region at the same time, being a tremendous fertility loss. Therefore, the protection of base grassland and restoration of degraded grassland are two fundamental approaches to control wind erosion on the grassland.

scholarly journals Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

2020 ◽  
Vol 12 (9) ◽  
pp. 1357 ◽  
Author(s):  
Maitiniyazi Maimaitijiang ◽  
Vasit Sagan ◽  
Paheding Sidike ◽  
Ahmad M. Daloye ◽  
Hasanjan Erkbol ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Data Fusion ◽  
Unmanned Aerial Vehicle ◽  
Canopy Structure ◽  
Canopy Height ◽  
Spectral Information ◽  
Structure Information ◽  
Crop Monitoring ◽  
Aerial Vehicle

Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.

scholarly journals Does Climate Change or Human Activity Lead to the Degradation in the Grassland Ecosystem in a Mountain-Basin System in an Arid Region of China?

2019 ◽  
Vol 11 (9) ◽  
pp. 2618 ◽  
Author(s):  
Junjie Yan ◽  
Guangpeng Zhang ◽  
Xiaoya Deng ◽  
Hongbo Ling ◽  
Hailiang Xu ◽  
...  
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Arid Region ◽  
Vegetation Coverage ◽  
Soil Drought ◽  
Grassland Ecosystem ◽  
Grassland Vegetation ◽  
Irrigated Area ◽  
The Impact ◽  
Basin System

In mountain-basin systems in the arid region, grasslands are sensitive to the impacts of climate change and human activities. In this study, we aimed to resolve two key scientific issues: (1) distinguish and explain the laws of grassland ecosystem deterioration in a mountain-basin system and identify the key factors related; and (2) evaluate whether damaged grasslands ecosystem have the potential for natural revegetation. Hence, by combining spatial analysis with statistical methods, we studied the trends of the deterioration of the grassland ecosystem and its spatial characteristics in Kulusitai, a mountain-basin system in the arid region of Northwest China. According to our results, vegetation coverage and productivity exhibited significant decreasing trends, while the temperature vegetation drought index (TVDI) exhibited a significant increasing trend. Drainage of groundwater, because of increase in irrigation for the expanded irrigated area around Kulusitai, and climate warming were the critical triggers that leaded to the soil drought. Soil drought and overgrazing, resulting from the impact of human activities, were the main factors responsible for the deterioration of the grassland ecosystems. However, limiting the number of livestock to a reasonable scale and reducing the irrigated area may help to increase the soil moisture, thus promoting the germination of soil seed banks and facilitating the normal growth of grassland vegetation. Furthermore, based on analysis of the phenology of the grassland vegetation, the reasonable period for harvesting and storage is from July 29 to August 5. The results of this study provide a scientific basis and practical guide for restoring mountain-basin grassland systems in arid regions.

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