UAV-HiRAP: A novel method to improve landscape-level vegetation classificationand coverage fraction estimation with unmanned aerial vehicle platform
Haozhou WANG1, 2, Feng WANG1*, Xueling YAO1, Yue MU1, Yongfei Bai3*, Qi LU1*
1

Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China

2

College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China

3

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy ofSciences, Beijing, 100093, China*Corresponding Author
Wang F: phone. +86 10 6282 4067, e-mail: wangfeng@caf.ac.cn; Bai Y: phone. +86 10-62836272, e-mail:yfbai@ibcas.ac.cn; Lu Q: phone. +86 10 62824099, e-mail: luqi@caf.ac.cn

Abstract:
Lightweight unmanned aerial vehicles(UAV) are revolutionizing spatial ecology and environment monitoring due totheir ability to acquire high-resolution imagery and flexibility of flying time. Development of image-analyzing tools
is currently challenging to extract efficiently and effectively vegetation structural and functional attributes atlandscape-level in the local ecosystems with UAV platform.
In this study, we proposed a novel method with machine learning algorithm to classify vegetation and calculate thevegetation coverage fraction(VCF) on the high spatial-resolution orthomosaic generated from thousands of images
acquired by UAV with structure-from-motion (SfM) computer vision technique. We validated this method withdetailed ground measured datasets, including 3953 trees and 19677 shrubs in the 100 ha (1000 m×1000 m) long-termmonitoring plot (115.95E，42.96N) in Elm (Ulmus pumila) Sparse FOrest Grassland Ecosystem(ESFOGE-Plot),which is located in Otindag Sandy land, Inner Mongolia, China. The new method was incorporated into a userfriendly software “Unmanned Aerial Vehicles - High Resolution imagery Analysis Platform” (UAV-HiRAP), which
integrated color space transformation function, classification and regression tree (CART) algorithm and parallel
computing. Finally, we developed a model to optimize the workload of vegetation survey by Monte Carlo stochastic
simulation based on the classified vegetation map.
Using this new method, bare sandy land, perennial plants and herbaceous plants were classified successfully on thehigh-resolution orthomosaic (10 cm/pixel) of ESFOGE-Plot. The VCF calculated from UAV imagery agreed wellwith the results estimated from the crown diameters of all individuals measured in the ground survey. The validated
results showed that the coverage fractions of perennial plants calculated from UAV imagery and that estimated fromground survey have a stable linear relationship over different spatial scale (slope = 0.76, 0.77, 0.80, and 0.77, R2 =0.62, 0.25, 0.12 and 0.20, at 100 m×100 m, 50 m×50 m, 25 m×25 m and 12.5 m×12.5 m plot-levels, respectively; P< 0.001). Furthermore, we found the relationship between investigated quadrats number n and side length l can bedescribed as n=727 / (l + 7) (R2= 0.996, P<0.0001) at desired confidence level of 95%. The number and area ofquadrats can be estimated from this function, which reduces workload in the field survey while preserving theaccuracy and reliability required.
Our results suggest that UAV-HIRAP can be a powerful tool to classify vegetation and calculate VCF at the landscapelevel with UAV platform, and demonstrate the potential of UAV to monitor vegetation in ecology and environment
management.

Keywords: dryland vegetation, machine learning, sparse forest grassland, orthoimages