Xiaokun Ou

Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China

Mapping of vegetation using remote sensing in mountainous areas is considerably hampered by topographic effects on the spectral response pattern. A variety of topographic normalization techniques have been proposed to correct these illumination effects due to topography. The purpose of this study was to compare six different topographic normalization methods (Cosine correction, Minnaert correction, C-correction, Sun-canopy-sensor correction, two-stage topographic normalization, and slope matching technique) for their effectiveness in enhancing vegetation classification in mountainous environments. Since most of the vegetation classes in the rugged terrain of the Lancang Watershed (China) did not feature a normal distribution, artificial neural networks (ANNs) were employed as a classifier. Comparing the ANN classifications, none of the topographic correction methods could significantly improve ETM+ image classification overall accuracy. Nevertheless, at the class level, the accuracy of pine forest could be increased by using topographically corrected images. On the contrary, oak forest and mixed forest accuracies were significantly decreased by using corrected images. The results also showed that none of the topographic normalization strategies was satisfactorily able to correct for the topographic effects in severely shadowed areas.

Tourism: An Alternative to Development? Reconsidering Farming, Tourism, and Conservation Incentives in Northwest Yunnan Mountain Communities

Abstract In the last decade, tourism has developed rapidly in the mountainous areas of northwest Yunnan. This growth has led to substantial economic and social changes, with resulting environmental consequences. This article uses a case study to illustrate how local farmers involved in tourism changed their agricultural practices as a result of the transformations that took place in the area. The aim was to examine tourisms expected benefits of poverty alleviation and conservation incentives. Tourism investments were found to have been adopted only by households with available cash and labor, whereas they remained inaccessible for the poor, small landowners who most needed a new source of income and used their land more exhaustively. Relatively rich, large landowners did not take the opportunity to reduce their agricultural activities. Instead, they used supplementary incomes earned from tourism to hire external labor to cultivate their land more intensely. Tourism development failed to generate real incentives for mountain farmers to adopt more conservation measures and prevent soil erosion and nonpoint source agricultural water pollution, which currently constitute serious environmental problems for mountain environments in Yunnan. This article presents recommendations based on the conclusions of the study.

Mapping dominant vegetation communities at Meili Snow Mountain, Yunnan Province, China using satellite imagery and plant community data

Mapping dominant vegetation communities is important work for vegetation scientists. It is very difficult to map dominant vegetation communities using multispectral remote sensing data only, especially in mountain areas. However plant community data contain useful information about the relationships between plant communities and their environment. In this paper, plant community data are linked with remote sensing to map vegetation communities. The Bayesian soft classifier was used to produce posterior probability images for each class. These images were used to calculate the prior probabilities. One hundred and eighty plant plots at Meili Snow Mountain, Yunnan Province, China were used to characterize the vegetation distribution for each class along altitude gradients. Then, the frequencies were used to modify the prior probabilities of each class. After stratification in a vegetation part and a non-vegetation part, a maximum-likelihood classification with equal prior probabilities was conducted, yielding an overall accuracy of 82.1% and a kappa accuracy of 0.797. Maximum-likelihood classification with modified prior probabilities in the vegetation part, conducted with a conventional maximum-likelihood classification for the non-vegetation part, yielded an overall accuracy of 87.7%, and a kappa accuracy of 0.861.

Integration of satellite imagery, topography and human disturbance factors based on canonical correspondence analysis ordination for mountain vegetation mapping : a case study in Yunnan, China

The integration between vegetation data, human disturbance factors, and geo-spatial data (Digital Elevation Model (DEM) and image data) is a particular challenge for vegetation mapping in mountainous areas. The present study aimed to incorporate the relationships between species distribution (or vegetation spatial distribution pattern) and topography and human disturbance factors with remote sensing data, to improve the accuracy of mountain vegetation maps. Two different mountainous areas located in Lancang (Mekong) watershed served as study sites. An Artificial Neural Network (ANN) architecture classification was used as image classification protocol. In addition, canonical correspondence analysis (CCA) ordination was applied to address the relationships between topography and human disturbance factors with the spatial distribution of vegetation patterns. We used ordinary kriging at unobserved locations to predict the CCA scores. The CCA ordination results showed that the vegetation spatial distribution patterns are strongly affected by topography and human disturbance factors. The overall accuracy of vegetation classification was significantly improved by incorporating DEM or four CCA axes as additional channels in both the northern and southern study areas. However, there was no significant difference between using DEM or four CCA axes as extra channels in the northern steep mountainous areas because of a strong redundancy between CCA axes and DEM data. In the southern lower mountainous areas, the accuracy was significantly higher using four CCA axes as extra bands, compared to using DEM as an extra band. In the southern study area, the variance of vegetation data explained by human disturbance factors was larger than the variance explained by topographic attributes.

A GIS Approach to Estimating Tourists' Off-road Use in a Mountainous Protected Area of Northwest Yunnan, China

Abstract To address the environmental impacts of tourism in protected areas, park managers need to understand the spatial distribution of tourist use. Standard monitoring measures (tourist surveys and counting and tracking techniques) are not sufficient to accomplish this task, in particular for off-road travel. This article predicts tourists′ spatial use patterns through an alternative approach: park accessibility measurement. Naismiths rule and geographical information system′s anisotropic cost analysis are integrated into the modeling process, which results in a more realistic measure of off-road accessibility than that provided by other measures. The method is applied to a mountainous United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Site in northwest Yunnan Province, China, where there is increasing concern about potential impacts of unregulated tourist use. Based on the assumption that accessibility tends to attract more tourists, a spatial pattern of predicted off-road use by tourists is derived. This pattern provides information that can help park managers develop strategies that are effective for both tourism management and species conservation.

Nature Conservation versus Scenic Quality: A GIS Approach towards Optimized Tourist Tracks in a Protected Area of Northwest Yunnan, China

Development of appropriate tourism infrastructure is important for protected areas that allow public access for tourism use. This is meant to avoid or minimize unfavourable impacts on natural resources through guiding tourists for proper use. In this paper, a GIS-based method, the least-cost path (LCP) modelling, is explored for planning tourist tracks in a World Heritage site in Northwest Yunnan (China), where tourism is increasing rapidly while appropriate infrastructure is almost absent. The modelling process contains three steps: 1) selection of evaluation criteria (physical, biological and landscape scenic) that are relevant to track decision; 2) translation of evluation criteria into spatially explicit cost surfaces with GIS, and 3) use of Dijkstra’s algorithm to determine the least-cost tracks. Four tracks that link main entrances and scenic spots of the study area are proposed after optimizing all evaluation criteria. These tracks feature low-environmental impacts and high landscape qualities, which represent a reasonable solution to balance tourist use and nature conservation in the study area. In addtion, the study proves that the LCP modelling can not only offer a structured framwork for track planning but also allow for different stakeholders to participate in the planning process. It therefore enhances the effectivenss of tourism planning and managemnt in protected areas.

Association of Non-native Plant Species With Recreational Roads in a National Park in the Eastern Himalayas, China

Although the eastern Himalayas have high plant biodiversity, we know very little about plant invasions in the region. This study is the first to examine non-native plant distribution in a popular eastern Himalayan national park. A total of 61 non-native plant species were found in roadside plant communities, which are frequently disturbed by hikers, pack animals, and recreational vehicles. These species were annual or biennial herbs, most of which originated in America or Europe. Non-native plant richness varied with the degree of anthropogenic disturbance. Specifically, greater numbers of non-native species were found at road heads and ends, which are generally subject to intense human activity. The average number of non-native species also varied according to the type of road and road use, with more present along motor roads and horse-riding trails than along hiking trails. These results highlight the role of vehicles and pack animals as dispersal vectors and provide a foundation for future invasion management decisions. To prevent the spread of non-native plants from park roads to the adjacent landscape, we also recommend the development of educational and monitoring programs that encourage tourist participation in conservation efforts.

Spatial-Temporal Monitoring of Urban Growth: A Case in Kunming, Southwest China

With the rapid growth of urban population and economic development, the urban growth has also accelerated dramatically. The paper monitored the urban growth of Kunming by detecting the land use change after supervised classification and analyzing urban expansion rate and intensity index in 1974–2013. The result shows the urban has experienced rapid expansion. Moreover, since 1992 the spatial extension has speed up. The main source of land expansion were farmland, woodland and grassland. And the urban expansion is expanding rapidly to the southeast, northwest and northeast with the old city as the core in Kunming. The urban growth is mainly affected by the natural terrain, economy, population and administrative factors. The study summarized the regularity of expansion and the driving force factors of the city growth, and provide a basis theory for future urban healthy development and provide experience for relevant government and scholars.