Redefining Diversity and Dynamics of Natural Resources Management in Asia, Volume 1

Sustainable Natural Resources Management in Dynamic Asia

Ganesh Shivakoti, Ujjwal Pradhan, Helmi Helmi, Ganesh Shivakoti, Ujjwal Pradhan, Helmi Helmi

• Editorial: Elsevier
• Año: 2016
• ISBN:
  9780128104705
• Categoría: Technology & Engineering
• Categoría: Power Resources

Redefining Diversity and Dynamics of Natural Resources Management in Asia, Volumes 1-4 brings together scientific research and policy issues across various topographical area in Asia to provide a comprehensive overview of the issues facing the region.

Sustainable Natural Resources Management in Dynamic Southeast Asia, Volume 1, pulls together regional experts in the field to look specifically at sustainability issues across the region, to see what has been implemented, what the impacts have been, and what other options are available. In the race to be a developed region, many Southeast Asian countries have foregone natural resources through haphazard use. As a result, the people are faced with numerous environmental challenges, particularly deforestation and forest degradation, biodiversity loss and ecosystem degradation, reduction in soil quality, and decreases in the quantity of available water.

Community-based forest management is the involvement of local communities in the protection, conservation and management of public forests to prevent degradation through sustainable practices while still responding to the basic social and economic needs of local populations. When the people who depend on forest resources for their livelihoods are jointly responsible for managing and protecting them, they tend to do so in a more sustainable manner by focusing on the long-term benefits rather than the immediate short-term gains. However, when tenure rights are weak, unclear, or insecure, or offer limited benefits, people are incited in extracting more immediate benefits, resulting in suboptimal forest management and the reduction of carbon stocks.

• Features case studies that cover issues such as rising levels of deforestation, forest degradation, regional food security, ecosystem degradation, biodiversity loss, conflicts over natural resource use, water management issues, and impacts on local communities
• Includes contributions from local researchers who are dealing with these issues first hand, and on a daily basis
• Includes a comparative review on REDD+ implementation in different communities
• Focuses on sustainability issues across the region

Section III

Learning From The Field Cases/Issues

Chapter 7

High Resolution of Three-Dimensional Dataset for Aboveground Biomass Estimation in Tropical Rainforests

W.V.C. Wong*^,†; S. Tsuyuki* ^* University of Tokyo, Tokyo, Japan
^† University of Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia

Abstract

Remote sensing is a highly effective technological tool used in sustainable natural resources management with the capability to assess large forest areas in different periods of time. These capabilities permit the development of a monitoring system for certain forestry purposes such as aboveground biomass (AGB) estimation, where regular updating is essential under the measurement, reporting, and verification (MRV) system of reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks in developing countries (REDD +) scheme. The development of high-resolution three-dimensional data sets of digital photogrammetry and airborne laser scanning (ALS) have enhanced the estimation accuracy for height-related forest variables such as AGB. In this chapter, we discuss the elements of the methodology using a combination of remote sensing data sets and ground-based inventory for AGB estimation. Then, we demonstrate the capability of using aerial photographs and ALS data sets in estimating AGB from a case study conducted in the tropical montane forest environment of Northern Borneo, Malaysia. We also discuss the use of other remote sensing data for the same purpose of AGB estimation, including the potential and limitation in the context of the Southeast Asia region. In the case where there is an existing regular flight campaign of aerial photographs acquisition, it is cost-effective way using an aerial photographs data set in updating and monitoring AGB on a national or subnational scale once a detailed ALS-digital terrain model is available. The research implication of this study demonstrates the capability of using high-resolution three-dimensional data sets with a combination of a ground data set to estimate AGB for the MRV system.

Keywords

Forest carbon; Remote sensing; Aerial photogrammetry; Tropical rain forest

Acknowledgments

This study was supported by the Advanced Carbon Monitoring in Asian Tropical Forest by High Precision Remote Sensing Technology project of the Ministry of Agriculture, Fishery and Forestry (MAFF), Japan. The field data was collected by the members of Forestry and Forest Products Research Institute (FFPRI), Japan, the University of Tokyo, and Universiti Malaysia Sabah (UMS). We are thankful to Sabah Forestry Department (SFD) for the permission, to Sabah Forest Industries (SFI) and villagers of Kampung Long Mio for the field support.

7.1 Introduction

Tropical forests contain high biomass compared to other forest ecosystems, with approximately half of the total living biomass of the world's major ecosystem (Houghton et al., 2009). In the Global Forest Resources Assessment 2010 (FAO, 2010), the total carbon stock in the living forest biomass for Southeast Asia was estimated at 22 Gt C, or approximately 8% of the global total. Indonesia accounted for more than half of the carbon stock with the value of 13.0 Gt C, followed by Malaysia (3.2 Gt C) and Myanmar (1.7 Gt C). Additionally, Slik et al. (2010) reported that aboveground biomass (AGB) per unit area in Borneo island is relatively 60% higher than in the Amazon. However, the forest area in Southeast Asia declined by 31 Mha from 267 Mha in 1990 to 236 Mha in 2010 with a two-thirds majority occurring in insular Southeast Asia where the main drivers were attributed to forest conversion to cash crops plantations, logging, and conversion to forest plantations (Stibig et al., 2014). Concurrently, the Global Forest Resources Assessment 2010 also reported the carbon stock declined by 3.3 Gt C in the same period of 1990–2010 (FAO, 2010). In the Fifth Assessment Report (AR5), activities from forestry and other land use (FOLU) contributed the total greenhouse gases emission by 11% or 5.4 Gt CO₂-eq/year in 2010 (IPCC, 2014).

Recognizing the importance for developing countries along with industrialized countries for the total emissions reductions from all major sources, reducing emission from deforestation and forest degradation and the role of conservation, sustainable management of forests on enhancement of forest carbon stocks in developing countries (REDD +) was introduced and proposed during the 11th session of the Conference of Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) in Montreal, 2005 (UNFCCC, 2005) and adopted in COP 13, Bali, 2007 (UNFCCC, 2007). To implement the REDD + scheme, an estimation and monitoring system of forest biomass with reliable accuracy along with a robust and transparent system is one of the major technical issues under discussion. This activity is discussed mainly under the measurement, reporting, and verification (MRV) system of REDD + (eg, UNFCCC, 2014). Field-based inventory alone will be resource intensive and yield higher uncertainties in the biomass estimation. The development of remote sensing technology with a combination of ground-based inventory approaches for estimating forest carbon stocks and forest area changes was accepted in the methodological guidance for activities relating to REDD +, which contribute to the robust and transparent forest monitoring system or MRV system (Decision 4/CP. 15).

There have been successes in estimating forest biomass on a regional scale (eg, Brown et al., 1993; Saatchi et al., 2011; Baccini et al., 2012; Avitabile et al., 2016); however, the resolutions were coarse of 1 km (Saatchi et al., 2011) or 500 m (Baccini et al., 2012) derived using a low-resolution optical data set such as the moderate resolution imaging spectroradiometer (MODIS). Biomass estimation using only an optical sensor data set (ie, multispectral or hyperspectral data) will yield an estimation accuracy problem, especially for high biomass stands, and it is recommended it be combined with other types of remote sensing data sets (Koch, 2010). Recently, the use of a high- resolution three-dimensional data set (ie, airborne laser scanning (ALS) and structure from motion (SfM) photogrammetry) have been demonstrated to yield good estimation, especially with height-related forest variables such as stem volumes, stand height, and biomass (eg, Gobakken et al., 2015; Ioki et al., 2014; Ota et al., 2015). This type of high-resolution three-dimensional data set offers great improvement on estimation accuracy and reliability and reduces uncertainties for forest biomass estimation in accordance with the Intergovernmental Panel on Climate Change (IPCC)'s Tier 3 for the land use, land-use change and forestry (LULUCF) sector (IPCC, 2006). In addition to the accuracy issue, a cost-effective system is also a major consideration when developing a biomass monitoring system for the national or subnational level.

Thus, in this chapter, we discuss the technical issues in estimating forest biomass for tropical rainforest using a combination of a remote sensing data set and ground samples. We also present an example of a case study in estimating forest biomass using a high-resolution three-dimensional data set of ALS and an aerial photogrammetry data set in tropical montane forest in northern Borneo. We then discuss technical challenges, large-scale applications, and how integrating this method can contribute to forest biomass estimation in an effective way for the Southeast Asia region.

7.2 Estimating Aboveground Biomass Using a Combination of Remote Sensing Data Sets and Ground Samples

The interest in forest biomass studies in Southeast Asia can be tracked back to the late 1980s (eg. Brown et al., 1989; Yamakura et al., 1986; Yoneda et al., 1990). Since then, studies in many aspects of AGB such as allometric equation (eg, Yamakura et al., 1986; Brown et al., 1989; Ketterings et al., 2001), biomass dynamic (eg, Nakagawa et al., 2012; Toma et al., 2005), estimation approach (eg, Okuda et al., 2004; Ioki et al., 2014), and regional estimation (eg, Brown et al., 1993; Langner et al., 2015) studies have been developed.

AGB is one of the major components of carbon pools in forestland together with below- ground biomass (BGB), dead organic matter, and soil organic matter. Estimating AGB is rather straightforward compared to other components of carbon pools, although a default value of 0.37 for the ratio of BGB to AGB can be employed to estimate BGB for tropical rainforest as recommended by IPCC (2006).

The remote sensing technology with a combination of ground samples has enabled wall-to-wall estimation of AGB. There are several guidelines that have been published in estimating forest biomass using a remote sensing data set such as can be found in REDD + Cookbook (Hirata et al., 2012) or “Integrating remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests” (GFOI, 2013). In many of the guidelines and research studies, the technical aspects, which are still undergoing research and development and discussion, are the allometric equation, ground sample, remote sensing data set, a...