Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

Kim, Jaebeom; Kang, Sinkyu; Seo, Byong Seol; Narantsetseg, A.; Han, Youngji

doi:10.1080/15481603.2019.1662166

Cited by 26 publications

(16 citation statements)

References 35 publications

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“…LiDAR point data were mainly classified as vegetation, ground and other noise by the triangulated irregular network (TIN) filtering method [38], which is embedded in TerraScan (Terrasolid Ltd., Helsinki, Finland) for module classification in the Terrasolid software package to create a digital terrain model (DTM) and a digital surface model (DSM) from the LiDAR data. Classification points were assessed to ensure that most LiDAR points were correctly classified.…”

Section: Lidar Data Analysismentioning

confidence: 99%

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

et al. 2021

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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.

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Section: Lidar Data Analysismentioning

confidence: 99%

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

et al. 2021

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“…They are also less influenced by the specific differences in illumination and shade within and between images [40,41]. The use of different colour spaces is well-established for estimating fractional crop cover and for image segmentation and classification (e.g., [38,[42][43][44][45][46][47], but has also been applied for disease detection [48] and for assessing nitrogen content of leaves and canopies [39,49].…”

Section: Introductionmentioning

confidence: 99%

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Swaef¹,

Maes

Aper³

et al. 2021

Remote Sensing

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The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.

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“…The last decades have seen the development of methods to estimate crop FVC based on remote-sensing images from unmanned aerial vehicle (UAV), aerial, or satellite platforms [ 5 , 29 – 33 ]. These methods can be divided into five categories: (i) physical model methods, (ii) semi-empirical methods, (iii) empirical methods, (iv) crop growth methods, and (v) hybrid methods.…”

Section: Introductionmentioning

confidence: 99%

“…However, many of the parameters required by these models may not be readily available, which limits the application of the models. Semi-empirical methods are often simplified versions of physical models and include the soil line method [ 39 ], the pixel dichotomy model (PDM) [ 40 , 41 ], and the Baret model [ 29 , 32 ]. The PDM hypothesizes that pixels contain mixed information from soils and crops [SI total = (1 − FVC) × SI soil + FVC × SI vegetation ], which allows FVC to be calculated [FVC = (SI total − SI soil )/(SI vegetation − SI soil )] [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing

et al. 2021

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Background Fractional vegetation cover (FVC) is an important parameter for evaluating crop-growth status. Optical remote-sensing techniques combined with the pixel dichotomy model (PDM) are widely used to estimate cropland FVC with medium to high spatial resolution on the ground. However, PDM-based FVC estimation is limited by effects stemming from the variation of crop canopy chlorophyll content (CCC). To overcome this difficulty, we propose herein a “fan-shaped method” (FSM) that uses a CCC spectral index (SI) and a vegetation SI to create a two-dimensional scatter map in which the three vertices represent high-CCC vegetation, low-CCC vegetation, and bare soil. The FVC at each pixel is determined based on the spatial location of the pixel in the two-dimensional scatter map, which mitigates the effects of CCC on the PDM. To evaluate the accuracy of FSM estimates of the FVC, we analyze the spectra obtained from (a) the PROSAIL model and (b) a spectrometer mounted on an unmanned aerial vehicle platform. Specifically, we use both the proposed FSM and traditional remote-sensing FVC-estimation methods (both linear and nonlinear regression and PDM) to estimate soybean FVC. Results Field soybean CCC measurements indicate that (a) the soybean CCC increases continuously from the flowering growth stage to the later-podding growth stage, and then decreases with increasing crop growth stages, (b) the coefficient of variation of soybean CCC is very large in later growth stages (31.58–35.77%) and over all growth stages (26.14%). FVC samples with low CCC are underestimated by the PDM. Linear and nonlinear regression underestimates (overestimates) FVC samples with low (high) CCC. The proposed FSM depends less on CCC and is thus a robust method that can be used for multi-stage FVC estimation of crops with strongly varying CCC. Conclusions Estimates and maps of FVC based on the later growth stages and on multiple growth stages should consider the variation of crop CCC. FSM can mitigates the effect of CCC by conducting a PDM at each CCC level. The FSM is a robust method that can be used to estimate FVC based on multiple growth stages where crop CCC varies greatly.

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Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

Cited by 26 publications

References 35 publications

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing

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