Guifang Liu

More than carbon stocks: A case study of ecosystem-based benefits of REDD+ in Indonesia

During the 15th Conference of the Parties (COP 15), Parties agreed that reducing emissions from deforestation and forest degradation and enhancing ‘removals of greenhouse gas emission by forests’ (REDD+) in developing countries through positive incentives under the United Nations Framework Convention on Climate Change (UNFCCC) was capable of dealing with global emissions. As REDD+ seeks to lower emissions by stopping deforestation and forest degradation with an international payment tier according to baseline scenarios, opportunities for ecosystem benefits such as slowing habitat fragmentation, conservation of forest biodiversity, soil conservation may be also part of this effort. The primary objective of this study is to evaluate ecosystem-based benefits of REDD+, and to identify the relationships with carbon stock changes. To achieve this goal, high resolution satellite images are combined with Normalized Difference Vegetation Index (NDVI) to identify historical deforestation in study area of Central Kalimantan, Indonesia. The carbon emissions for the period of 2000–2005 and 2005–2009 are 2.73 × 105 t CO2 and 1.47 × 106 t CO2 respectively, showing an increasing trend in recent years. Dring 2005–2009, number of patches (NP), patch density (PD), mean shape index distribution (SHAPE_MN) increased 30.8%, 30.7% and 7.6%. Meanwhile, largest patch index (LPI), mean area (AREA_MN), area-weighted mean of shape index distribution (SHAPE_AM), neighbor distance (ENN_MN) and interspersion and juxtaposition index (IJI) decreased by 55.3%, 29.7%, 15.8%, 53.4% and 21.5% respectively. The area regarding as positive correlation between carbon emissions and soil erosion was approximately 8.9 × 103 ha corresponding to 96.0% of the changing forest. These results support the view that there are strong synergies among carbon loss, forest fragmentation and soil erosion in tropical forests. Such mechanism of REDD+ is likely to present opportunities for multiple benefits that fall outside the scope of carbon stocks.

Recent Observations of Human-induced Asymmetric Effects on Climate in Very High-Altitude Area

Like urban heat islands (UHI), human-induced land degradation (HLD) is a phenomenon attributed to human activities, but this phenomenon occurs in non-urban areas. Although a large body of work has demonstrated that land-cover change influences local climate systems, little work has been done on separating the impact of HLD from naturally-occurring fluctuations in very high-altitude areas. We developed an innovative NDVI-difference method in order to evaluate HLD effects upon the climate system in the central Tibet Plateau. The results show that the minimum temperature increased at a significantly faster pace than the maximum temperature in the growing season at HLD meteorological stations, but this was reversed at stations with natural forces only. Further analysis revealed that abrupt changes of minimum temperature occurred five years earlier and amplitudes of these changes were 1.4 times larger than at stations with natural forces only. Therefore, our results complement other evidence that points to the fact that local effects from UHI contribute to climatic asymmetry observed between minimum and maximum temperature trends. Accordingly, we stress the need for consideration of non-urban factors from anthropogenic activities, such as human-induced land degradation, in understanding these asymmetric diurnal changes.

Carbon, soil, and ecological benefits of REDD+ policies in Southwest China

Taking the Xishuangbanna district in Southwest China as the study region, we made a systematic and comprehensive evaluation of the carbon benefits and ecological benefits of the programme for reducing emissions from deforestation and forest degradation (REDD+) while enhancing forest carbon sequestration capacity in developing countries. It was found that carbon emissions in the study region increased and the landscape tended to fragment. Furthermore, the average carbon emissions of areas with severe soil erosion were more than 6 times higher than that of areas with minor erosion. These results indicate that REDD+ not only reduces carbon emissions caused by deforestation and forest degradation, but also provides other ecological benefits, such as mitigating forest fragmentation, preserving biodiversity, and conserving soil and water. From this perspective, REDD+ provides sustainable forest management and ecological benefits.

A novel approach to assess and monitor forests for REDD

Reducing emissions from deforestation and forest degradation, plus conservation of forest carbon stocks, sustainable management of forests, and enhancement of forest carbon stocks, is a set of steps designed to use market and financial incentives in order to reduce the emissions of greenhouse gases. The paper addresses the role of satellite remote sensing technologies as a tool for monitoring, assessment, reporting and verification of carbon credits. In particular, image fusion techniques were used to assess and monitor forests based on moderate resolution images of MODIS and TM data. The result showed that satellite image fusion could provide more spatial details and better spectral information compared with the original image and thus prove to be an excellent tool for monitoring carbon storage change for REDD.

Current remote sensing options for monitoring carbon emissions

Estimating carbon emissions from deforestation and forest degradation requires the expertise of forestry science, ecological modeling, statistics, remote sensing, and field measurement. In particular, remote sensing technology is capable of measurements of carbon contents of forest types, if supported by field information such as sample plots to calibrate the technology. In this paper current remote sensing options for monitoring carbon emissions were presented. We concluded that the combined application of optical and microwave remote sensing data is expected to provide an opportunity to estimate emissions both on a global scale and specifically in places where existing information is not reliable or consistent by overcoming their own current disadvantages.

Evaluation of land use classification accuracy based upon TM and CBERS-02B HR data fusion

Data fusions from SAR and TM, SPOT and TM, ASTER and TM, MODIS and ETM, etc are the common methods. But that from TM and CBERS-02B is rare. With HR camera working in September 19th 2007, Chinese-Brazil Earth Resources Satellite 02B (CBERS-02B) became the first civilian high-resolution satellite in China. It could provide 2.36m panchromatic image which is better to Landsat TM. Meanwhile the spectral resolution of TM is better than CBERS-02B. So its a good idea to take advantage of benefits from CBERS-02B HR and TM through data fusion. In this study, images of TM and CBERS-02B HR in 2007 were used as data sources. After image registration and noiseremoval process, data fusion methods of IHS and PCA were adopted. Then unsupervised classification and supervised classification were used for land use classification. Finally, classification accuracy between original image and fusion image was compared and evaluated. The result shows: (1) Compared with original TM or CBERS-02B HR image, the fusion image not only retains abundance spectrum but also enhances the object details. Residential texture, lake morphological, the relative position between roads, industrial and mining sites, etc, was identified easily. (2) Results from IHS and PCA are different. IHS image had higher spatial resolution but more spectral distortion. Spectral differences between some objects became smaller and classification accuracy was lower. Supervised classification accuracy assessment shows that overall Kappa index and overall land use classification accuracy decreased by 0.237 and 11% respectively. Meanwhile PCA image not only had high spatial resolution, but also smaller spectral distortion. Different land use / cover types can be better distinguished. (3) Disadvantages of low spatial resolution in TM and single color in CBERS-02B HR image are overcome in PCA fusion image to a certain extent. In this research under supervised classification in PCA image Kappa index of farm land, forest land and bare land increased by 0.097, 0.176 and 0.242 respectively. Overall Kappa index and overall land use classification accuracy were improved by 0.092 and 7.24% respectively.

Statistics Analysis on SPOT 5 Classification Accuracy of Different Data Fusion Methods

In recent years, data fusion has become a very popular method in remote sensing image enhancement. In this paper, a comparative study was conducted on data fusion methods based upon SPOT5 image. First land types of forest, paddy field, dry land, water and building was selected through field survey. Then supervised classification and non-supervised classification were used upon original image and four fusion images (HIS, PCA, high-pass filtering (HPF) and Brovery) respectively. Land change area, change rate and classification accuracy were calculated. Finally suitable SPOT5 fusion method for every land types was presented. The result showed: (1) In all four fusing methods PCA held the highest land change rate, being average 49.0% for non-supervised classification and average 18.9% for supervised classification. So visual interpretation was a better way for PCA fusion image. (2) HIS produced some distortion to the original spectrum and made flaky features into pieces. This method was suitable to extract small features in complicated urban areas because of its high spatial resolution. In the research, building change rate in HIS fusing image under supervised classification was lowest, only 3.32%. (3) For HPF land change rate was low for no matter non-supervised classification or supervised classification, being average 16.3% and 11.2% respectively. This fusion method held low distortion and more high-frequency spectrum. It was suitable to be used as the basic image data for both supervised classification and non-supervised classification. In our research image classification accuracy of urban areas in HPF fusion image was 93.1%.

Virtual Huanghe River System: key technologies and their applications

Virtual Reality provides a new approach for geographical research. In this paper, a framework of the Virtual Huanghe River System was first presented, including four main modules - data sources module, 3D terrain database module, 3D model database module and 3D simulation implementation module. Then the key technologies of Virtual Huanghe River System and their applications were discussed in detail: 1) OpenGL technology, the 3D graphics developing tool, was employed in Virtual Huanghe River System to realize the function of dynamic real-time navigation. 2) MultiGen Creator was used to create the 3D model with real texture. 3) OpenGL and MO were used to make the mutual response between 3D scene and 2D electronic map available. The advantages of visualization, reality and locality in 3D scene and macroscopic view, integrality and conciseness in 2D electronic map were integrated. And at the same time the disadvantages of the losing direction in 3D scene and abstract and ambiguity in 2D electronic map were overcome.