Next Article in Journal
Solid Wood Properties Assessed by Non-Destructive Measurements of Standing European Larch (Larix decidua Mill.): Environmental Effects on Variation within and among Trees and Forest Stands
Next Article in Special Issue
The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest
Previous Article in Journal
Predicting Growing Stock Volume of Scots Pine Stands Using Sentinel-2 Satellite Imagery and Airborne Image-Derived Point Clouds
Previous Article in Special Issue
Development of a GPS Forest Signal Absorption Coefficient Index
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Forests 2018, 9(5), 275; https://doi.org/10.3390/f9050275

Estimation of Forest Aboveground Biomass and Leaf Area Index Based on Digital Aerial Photograph Data in Northeast China

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
4
School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei 230036, China
*
Author to whom correspondence should be addressed.
Received: 11 April 2018 / Revised: 17 May 2018 / Accepted: 17 May 2018 / Published: 18 May 2018
Full-Text   |   PDF [9577 KB, uploaded 30 May 2018]   |  

Abstract

Forest aboveground biomass (AGB) and leaf area index (LAI) are two important parameters for evaluating forest growth and health. It is of great significance to estimate AGB and LAI accurately using remote sensing technology. Considering the temporal resolution and data acquisition costs, digital aerial photographs (DAPs) from a digital camera mounted on an unmanned aerial vehicle or light, small aircraft have been widely used in forest inventory. In this study, the aerial photograph data was acquired on 5 and 9 June, 2017 by a Hasselblad60 digital camera of the CAF-LiCHy system in a Y-5 aircraft in the Mengjiagang forest farm of Northeast China, and the digital orthophoto mosaic (DOM) and photogrammetric point cloud (PPC) were generated from an aerial overlap photograph. Forest red-green-blue (RGB) vegetation indices and textural factors were extracted from the DOM. Forest vertical structure features and canopy cover were extracted from normalized PPC. Regression analysis was carried out considering only DOM data, only PPC data, and a combination of both. A recursive feature elimination (RFE) method using a random forest was used for variable selection. Four different machine-learning (ML) algorithms (random forest, k-nearest neighbor, Cubist and supporting vector machine) were used to build regression models. Experimental results showed that PPC data alone could estimate AGB, and DOM data alone could estimate LAI with relatively high accuracy. The combination of features from DOM and PPC data was the most effective, in all the experiments considered, for the estimation of AGB and LAI. The results showed that the height and coverage variables of PPC, texture mean value, and the visible differential vegetation index (VDVI) of the DOM are significantly related to the estimated AGB (R2 = 0.73, RMSE = 20 t/ha). The results also showed that the canopy cover of PPC and green red ratio index (GRRI) of DOM are the most strongly related to the estimated LAI, and the height and coverage variables of PPC, the texture mean value and visible atmospherically resistant index (VARI), and the VDVI of DOM followed (R2 = 0.79, RMSE = 0.48). View Full-Text
Keywords: digital aerial photograph; aboveground biomass; leaf area index; photogrammetric point cloud; recursive feature elimination; machine-learning digital aerial photograph; aboveground biomass; leaf area index; photogrammetric point cloud; recursive feature elimination; machine-learning
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, D.; Gu, X.; Pang, Y.; Chen, B.; Liu, L. Estimation of Forest Aboveground Biomass and Leaf Area Index Based on Digital Aerial Photograph Data in Northeast China. Forests 2018, 9, 275.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top