Improving Estimation of Forest Canopy Cover by Introducing Loss Ratio of Laser Pulses Using Airborne LiDAR

2020 ◽  
Vol 58 (1) ◽  
pp. 567-585 ◽  
Author(s):  
Qingwang Liu ◽  
Liyong Fu ◽  
Guangxing Wang ◽  
Shiming Li ◽  
Zengyuan Li ◽  
...  
2019 ◽  
Vol 117 (5) ◽  
pp. 492-503 ◽  
Author(s):  
Iver T Hull ◽  
Lisa A Shipley

Abstract Vegetation in the forest understory is a key food resource for wild ungulates like deer (Odocoileus spp.) because the amount of nutritious forage influences animal productivity and density. Therefore, measuring the abundance of understory vegetation available to wildlife populations is often a key objective for wildlife managers. Field-based methods for measuring understory vegetation across remote landscapes are time- and resource-intensive, so we compared estimates of understory vegetation density derived from airborne light detection and ranging (LiDAR) returns with vegetation biomass sampled directly on 65 field plots across 4 years and >250,000 hectares of xeric conifer forests in northeastern Washington. We found that LiDAR-derived estimates of understory vegetation density were only able to predict field-sampled vegetation biomass when the two sampling methods occurred within 3 years of each other, and overstory canopy cover was <50 percent. Our results demonstrate limitations in the ability of LiDAR, at the intensity and frequency currently applied for multiuse purposes, to measure the quantity of forage. However, further testing with synchronous field sampling and higher-density laser pulses holds promise.


2021 ◽  
Author(s):  
Alba Viana Soto ◽  
Mariano García ◽  
Inmaculada Aguado ◽  
Javier Salas

2006 ◽  
Vol 21 (3) ◽  
pp. 149-158 ◽  
Author(s):  
A. Farid ◽  
D.C. Goodrich ◽  
S. Sorooshian

Abstract Airborne lidar (light detecting and ranging) is a useful tool for probing the structure of forest canopies. Such information is not readily available from other remote sensing methods and is essential for modern forest inventories. In this study, small-footprint lidar data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the San Pedro River basin near Benson, Arizona. The lidar data were acquired in June 2004, using Optech’s 1233 ALTM during flyovers conducted at an altitude of 600 m. Canopy height, crown diameter, stem dbh, canopy cover, and mean intensity of return laser pulses from the canopy surface were estimated for the cottonwood trees from the data. Linear regression models were used to develop equations relating lidar-derived tree characteristics with corresponding field acquired data for each age class of cottonwoods. The lidar estimates show a good degree of correlation with ground-based measurements. This study also shows that other parameters of young, mature, and old cottonwood trees such as height and canopy cover, when derived from lidar, are significantly different (P < 0.05). Additionally, mean crown diameters of mature and young trees are not statistically different at the study site (P = 0.31). The results illustrate the potential of airborne lidar data to differentiate different age classes of cottonwood trees for riparian areas quickly and quantitatively. West. J. Appl. For. 21(3):149–158.


2021 ◽  
Vol 136 ◽  
pp. 106728
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Yuanping Xia ◽  
Yunju Nie ◽  
Xiaowei Xie ◽  
...  

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.


Author(s):  
Qingwang Liu ◽  
Shiming Li ◽  
Kailong Hu ◽  
Yong Pang ◽  
Zengyuan Li
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