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Remote Sens. 2018, 10(3), 438; https://doi.org/10.3390/rs10030438

Object-Based Mapping of Aboveground Biomass in Tropical Forests Using LiDAR and Very-High-Spatial-Resolution Satellite Data

1
Principal Research Director, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Japan
2
Hokkaido Research Center, Forestry and Forest Products Research Institute, 7 Hitsujigaoka, Toyohiraku, Sapporo 062-8516, Japan
3
Department of Forest Management, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Japan
4
Forestry Administration, No 40, Preah Norodom Blvd, Phnom Penh 12205, Cambodia
5
Ministry of Environment, No 503, Tonle Bassac, Chamkarmon, Phnom Penh 12301, Cambodia
6
Forest-Wildlife Research and Development Institute, Forestry Administration, Khan Sen Sok, Phnom Penh 12157, Cambodia
7
Faculty of Agriculture, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-8580, Japan
8
Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581, Japan
*
Author to whom correspondence should be addressed.
Received: 9 December 2017 / Revised: 6 March 2018 / Accepted: 7 March 2018 / Published: 11 March 2018
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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Abstract

Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0%, versus 92.9% for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R2 = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R2 = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus. View Full-Text
Keywords: aboveground biomass; airborne LiDAR; emission factor; object-based approach; REDD-plus; tropical forest; very-high-spatial-resolution satellite data aboveground biomass; airborne LiDAR; emission factor; object-based approach; REDD-plus; tropical forest; very-high-spatial-resolution satellite data
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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).
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Hirata, Y.; Furuya, N.; Saito, H.; Pak, C.; Leng, C.; Sokh, H.; Ma, V.; Kajisa, T.; Ota, T.; Mizoue, N. Object-Based Mapping of Aboveground Biomass in Tropical Forests Using LiDAR and Very-High-Spatial-Resolution Satellite Data. Remote Sens. 2018, 10, 438.

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