IA-SDSS: A GIS-based land use decision support system with consideration of carbon sequestration

Abstract

Land use, land use change and forestry (LULUCF) can play a positive role in mitigating global warming by sequestering carbon from the atmosphere into vegetation and soils. Local entities (e.g. local government, community, stockholders) have been making great efforts in enhancing carbon sequestration (CS) of local forests for mitigating global climate change and participating in international carbon-trade promoted by the Kyoto Protocol. Approaches and tools are needed to assess the enhancement of CS through land use changes and proper policy decisions. This paper presents an integrated assessment framework and a spatial decision support system (IA-SDSS) as a tool to support land-use planning and local forestry development with consideration of CS. The IA-SDSS integrates two process-based carbon models, a spatial decision (EMDS) module, a spatial cost-benefit analysis (CBA) module, and the analytic hierarchy process (AHP) module. It can provide spatially explicit CS information as well as CS-induced economic benefits under various scenarios of the carbon credit market. A case study conducted in Liping County, Guizhou Province, China demonstrated that the IA-SDSS developed in this study is applicable in supporting decision-making on ‘where’ and ‘how’ to adopt forestry land use options in favor of CS.

Introduction

As major indicators of human disturbances, land use changes significantly influence the carbon (C) fixation and flux (Post and Kwon, 2000). Appropriate land use changes and forestry activities have often been considered to be opportunities to reduce net CO₂ emissions to the atmosphere or to increase the net uptake of C from the atmosphere (IPCC, 1996, IPCC, 2007). The Kyoto Protocol under the United Nations Framework Convention on Climate Change (UNFCCC) agreed by more than 150 nations confirms that the eligible land use, land use change, and forestry (LULUCF) activities in the forms of reforestation and afforestation as parts of the Clean Development Mechanism (CDM) could provide C credits for offsetting for green house gas emission of developed countries (UNFCCC, 1997, art.3.3 and 3.4).

CS through appropriate forestry land use practices (e.g. reforestation and afforestation) is regarded as a win–win strategy with benefits to the global climate change mitigation, environmental conservation and rural poverty alleviation (FAO, 1999, Lal, 2002, Tschakert, 2004, Ponce-Hernandez, 2004; Lipper and Cavatassi, 2004, Pfaff et al., 2007). Reforestation and afforestation refer to the direct human-induced conversions of non-forested to forested land that was forested but has previously been converted to non-forested land and that has not been forested for a period of at least 50 years (UNFCCC, 1997, UNFCCC, 2001).

The implementation of CS through reforestation and afforestation activities needs land-use planning considering benefits of CS. In the past decade, efforts have been made to assess the CS potential of terrestrial ecosystems (Lal, 2005, Shrestha and Lal, 2006, Roxburgh et al., 2006, Schaldach and Alcamo, 2006) and to analyze the relevant eco-environmental and economic issues (De Jong et al., 2000, Kerr et al., 2003, Wise and Cacho, 2005, Olschewski and Benitez, 2005, Antle et al., 2007, Caldwell et al., 2007, Perez et al., 2007). C balance has been taken as a principal criterion for sustainable forest management (Chertov et al., 2005), and the linkage of CS with local and regional sustainable development has been proposed (IPCC, 1996, Gundimeda, 2004, Yin et al., 2007). Ponce-Hernandez (2004) extended conventional land evaluation guidelines published by FAO with CS to a new land suitability assessment process for crop selection. Palma et al. (2007) took CS as one of criteria in the environmental assessment. CS has significant effects on local land-use planning. From the local perspective, the CS potential will be an important criterion in land resource utilization evaluation and decision-making with ongoing C markets on voluntary basis.

Reliable, robust and cost-effective methods are required for accurately quantifying CS potential in terrestrial ecosystems (Lal, 2005, Zhao and Zhou, 2005, Niu and Duiker, 2006). Many methodologies have been developed for this purpose (Lal, 2005, Shrestha and Lal, 2006, Roxburgh et al., 2006, Schaldach and Alcamo, 2006). Models are currently acting as effective tools for assessing regional and global C cycles and their dynamics (Leith, 1975, Liu et al., 1997, Liu et al., 2002, Chen et al., 1999, Feng et al., 2007). They can also been used to predict the further trends of CS by terrestrial ecosystems under projected future climates and atmospheric CO₂ concentration (Cao and Woodward, 1998, Ju et al., 2007). Process-based models (Daly et al., 2000, Running and Coughlan, 1988) may be more reliable than statistical and parametric-based ones because of their foundation on mechanisms of C cycling in terrestrial ecosystems (Liu et al., 1997).

On the other hand, spatially explicit approaches have been developed for producing geo-referenced estimates of the CS potential (Liu et al., 1997, Chen et al., 2003, Ponce-Hernandez, 2004). The geographical information system (GIS) method is one of indispensable research approaches that can be combined with remote sensing and ecological models to assess C dynamics at different spatial scales (Lal, 2002). The characteristics of a few representative studies using GIS for estimating C potential are summarized in Table 1. In these studies, GIS is usually employed to process model inputs (climate, land cover and soil texture) and to visualize results (Ardö and Olsson, 2003, Ponce-Hernandez, 2004, Schaldach and Alcamo, 2006). However, no studies fully integrate process-based C models with GIS to estimate CS of terrestrial ecosystems affected by climate, atmospheric CO₂ concentration and forest age and to conduct land-use planning spatially considering the economic and ecological benefits from CS.

Since late 1970s, China has been engaging in forest plantations on large scales, and the total forest area increased from 122 million ha in late 1970s to 159 million ha in 2001 (China Year Book, 2002, SFA, 2006). These activities have had considerable consequences in C budget of China's terrestrial ecosystems (Wang et al., 2007, Thomas et al., 2007, Cao, 2008). At the local level (county, township, village), the land-use planning is generally closely associated with the need for economic development. The incentives for adopting land use strategies enhancing CS would result from market realization of the C value. This market force, if established fully in the near future, would dramatically alter the land use pattern at the grassroot level. A land use decision support tool that has the capability of estimating the CS potential and including the C value in the economic analysis would be urgently needed for improving CS by terrestrial ecosystems through proper land-use planning practices.

To satisfy this goal, an integrated land use assessment and spatial decision support system (IA-SDSS) was developed. This system was constructed on the ArcGIS 9.0 platform to spatially estimate CS potential using process-based C models, to compare ecological and economic benefits of different forest type and species options, to assess the suitability of different locations for CS land use activities. In this paper, the major characteristics of this system and its strategies to integrate different components are firstly highlighted. Then, functions of its components are described. Finally, a case study in Liping County, Guizhou Province, China was conducted to test the applicability of this system in land use assessment with consideration of CS benefits at a local scale.