Tourism: an alternative to development?: reconsidering farming, tourism and conservation incentives in Northwest Yunnan mountainous communities

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 tourism's 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

A GIS approach towards estimating tourist's off-road use in a mountainous protected area of Northwest Yunnan, China

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. Naismith's 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

Relating spatial pattern of forest cover to accessibility

Urban planning for optimal provision of recreational forests is not only concerned with how much space is needed, but equally with how this could be arranged in the landscape in order to make these forests accessible to many potential visitors. The present study sought to establish relationships between the spatial pattern of forest cover and these forests’ accessibility – either on foot or by bike – for short walks. This question was approached in an experimental way using landscape structure metrics. A factor analysis identified the common axes of spatial pattern. The first five factors explained 82.2% of the variation of the original data set. The first factor is related to forested area and number of forest patches, the second is related to shape complexity. The third factor quantifies contiguity, and the fourth measures the clumpiness of forests. The fifth refers to variability in forest shape. Only the factors related to forested area, forest shape complexity and clumpiness, show a significant correlation with recreational provision. A higher forest coverage and more forests should thus lead to a higher provision for short walking trips. However, when a small afforestation budget is available, high shape complexity, low forest contiguity and a high landscape shape index (LSI) should take priority. Shape indices make the most important contribution to single out patterns that offer recreation possibilities to a high number of people. The findings show the potential of using landscape structure metrics for the modelling of forest recreational provision

Estimation of winter wheat green leaf area index from field spectroscopic measurements using a semi-deterministic model.

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Multi-date Sentinel1 SAR image textures discriminate perennial agroforests in a tropical forest-savannah transition landscape

Synthetic Aperture Radar (SAR) provides consistent information on target land features; especially in tropical conditions that restrain penetration of optical imaging sensors. Because radar response signal is influenced by geometric and di-electrical properties of surface features’, the different land cover may appear similar in radar images. For discriminating perennial cocoa agroforestry land cover, we compare a multi-spectral optical image from RapidEye, acquired in the dry season, and multi-seasonal C-band SAR of Sentinel 1: A final set of 10 (out of 50) images that represent six dry and four wet seasons from 2015 to 2017. We ran eight RF models for different input band combinations; multi-spectral reflectance, vegetation indices, co-(VV) and cross-(VH) polarised SAR intensity and Grey Level Co-occurrence Matrix (GLCM) texture measures. Following a pixel-based image analysis, we evaluated accuracy metrics and uncertainty Shannon entropy. The model comprising co- and cross-polarised texture bands had the highest accuracy of 88.07 % (95 % CI: 85.52–90.31) and kappa of 85.37; and the low class uncertainty for perennial agroforests and transition forests. The optical image had low classification uncertainty for the entire image; but, it performed better in discriminating non-vegetated areas. The measured uncertainty provides reliable validation for comparing class discrimination from different image resolution. The GLCM texture measures that are crucial in delineating vegetation cover differed for the season and polarization of SAR image. Given the high accuracies of mapping, our approach has value for landscape monitoring; and, an improved valuation of agroforestry contribution to REDD+ strategies in the Congo basin sub-region