For decades now, land degradation has been identified as one of the most pressing problems facing the planet [...]

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Land Degradation Assessment with Earth Observation

Deforestation is thought of as a huge threat to carbon neutrality and the development of contemporary society and it has brought wide interest and attention in the science community to develop new methods to identify and quantify the occurrence and extent of forest loss. Understanding the forest-loss patterns is essential for forest management and protection. With the help of a high-spatial-resolution remote-sensing dataset on forest loss, the spatial and temporal dynamics of deforestation patterns in forests of Southwest China (SWC) have been investigated. The major findings of this study indicated that small-scale (<5 ha) deforestation raised pervasively in the region from 2001 to 2019, and the number of large patches of forest loss (>5 ha) has decreased significantly during the same period. Moreover, the mean size of forest-loss patches showed an increase from 0.34 ha to 0.61 ha over time. With the alarming trend of increasing deforestation in the southern region of our study area, the growth of emerging forest-loss hotspots was clearly observed in Chongqing and Sichuan Province. The results promoted an indepth understanding of forest-loss patterns in SWC and can help provide more coherent guidance for further forest monitoring and conservation.

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Spatial–Temporal Dynamics of Forest Extent Change in Southwest China in the Recent 20 Years

Active incentives or compensation measures plus conservation, sustainable management of forests, and enhancement of forest carbon stocks (denoted together as “REDD+”) should be adopted in developing countries to reduce the greenhouse gas emissions caused by deforestation and forest degradation. Identification and analysis of the driving forces behind carbon stocks are crucial for the implementation of REDD+. In this study, the principal component model and the stepwise linear regression model were used to analyze the social and economic driving forces of stocks in three important types of forest change: deforestation, forestland transformation, and forest degradation in China’s tropical rainforests of Xishuangbanna, based on the combination of satellite imagery and the normalized difference vegetation index. The findings show that there are different key driving forces that lead to carbon stock changes in the forest land conversion of Xishuangbanna. In particular, the agricultural development level is the main cause of emissions from deforestation, whereas poor performance of protection policies is the main cause of emissions from forest degradation. In contrast, the rural economic development interventions are significantly positive for emissions from forestland transformation. It is crucial to pay attention to distinguishing the driving forces behind carbon stock changes from forest degradation, deforestation, and transformation for optimizing REDD+ implementation and ensuring the effectiveness of REDD+.

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Identification of Socio-Economic Impacts as the Main Drivers of Carbon Stocks in China’s Tropical Rainforests: Implications for REDD+

The United Nations’ expanded program for Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to mobilize capital from developed countries in order to reduce emissions from these sources while enhancing the removal of greenhouse gases (GHGs) by forests. To achieve this goal, an agreement between the Parties on reference levels (RLs) is critical. RLs have profound implications for the effectiveness of the program, its cost efficiency, and the distribution of REDD+ financing among countries. In this paper, we introduce a methodological framework for setting RLs for REDD+ applications in tropical forests in Xishuangbanna, China, by coupling the Good Practice Guidance on Land Use, Land Use Change, and Forestry of the Intergovernmental Panel on Climate Change and land use scenario modeling. We used two methods to verify the accuracy for the reliability of land classification. Firstly the accuracy reached 84.43%, 85.35%, and 82.68% in 1990, 2000, and 2010, respectively, based on high spatial resolution image by building a hybrid matrix. Then especially, the 2010 Globeland30 data was used as the standard to verify the forest land accuracy and the extraction accuracy reached 86.92% and 83.66% for area and location, respectively. Based on the historical land use maps, we identified that rubber plantations are the main contributor to forest loss in the region. Furthermore, in the business-as-usual scenario for the RLs, Xishuangbanna will lose 158,535 ha (158,535 × 104 m2) of forest area in next 20 years, resulting in approximately 0.23 million t (0.23 × 109 kg) CO2e emissions per year. Our framework can potentially increase the effectiveness of the REDD+ program in Xishuangbanna by accounting for a wider range of forest-controlled GHGs.

Kazuhiro Harada

Papers

Framework for Accounting Reference Levels for REDD+ in Tropical Forests: Case Study from Xishuangbanna, China