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Abstract: Forest mapping over mountainous terrains is difficult because of high relief. Although digital elevation models (DEMs) are often useful to improve mapping accuracy, high quality DEMs are seldom available over large areas, especially in developing countries. In this study, a hierarchical approach coupled with topographic information derived from coarse DEM was developed to improve the efficiency and accuracy of forest mapping over mountainous areas. The overall idea of increasing mapping accuracy over large mountainous areas is to reduce spectral variety over areas to be mapped. The approach consists of three major steps. The first step is to partition a large mountainous area into several small mapping zones. Forest mapping is then conducted in each zone independently. At the second step, forest areas are separated from non-forest areas through a semi-automatic binary classification procedure. At the third step, forested areas are then further classified into detailed forest types by coupling Landsat ETM+ imagery and two topographic variables derived from a coarse DEM (extracted from 1:250,000 digital elevation contour layer, which are readily available). This hierarchical approach was illustrated and evaluated through a case study in Northwest Yunnan, China, a very rugged terrain in the world. Forests and non-forests were separated accurately and efficiently (the overall accuracy is 0.97 and Khat value is 0.94 of whole area). It was found that the inclusion of the coarse topographic data improved the mapping accuracy significantly (overall accuracy from 0.74 to 0.84, from 0.76 to 0.89, from 0.78 to 0.84 in three test areas, respectively), and that the difference in accuracy between the use of coarse DEM data and the use of fine DEM data for the study area is not significant (overall accuracy from 0.84 to 0.86). The results indicate that the hierarchical approach, coupled with coarse DEM information, is effective in increasing the accuracy of forest mapping over very rugged terrains when high quality digital elevation models are not available. [Copyright &y& Elsevier]

Numerous restoration programs have been launched worldwide in recent years, but the effectiveness of such programs for biodiversity conservation remains unclear. Additionally, priority areas of restoration need to be identified in a region where resources are limited. Habitat availability combines habitat amounts with interpatch connectivity, governing whether a landscape can shelter biological populations in the long term. Consequently, restoration efforts should focus on enhancing habitat availability to promote biological flows. In this study, we proposed a multi-scale approach to evaluate the effectiveness of restoration program and to set priority areas for forest restoration. The evaluations of effectiveness highlighted the contributions of forest cover change to habitat availability. The restoration priorities of landscapes were ranked by three indicators which are also tightly associated with habitat availability: relative urgency of intervention, efficiency of restoration, and connectivity importance for broader-scale biotic flows. We applied the approach in the Eastern Himalaya region of China, a global biodiversity conservation hotspot. Forest cover has been increasing by 3.4% in this region since 2000 due to the implementation of the Grain-for-Green Program, but not all the new forest areas were available as species habitat and/or stepping stones. By the method we developed, 14% of the areas are ranked as the high restoration priorities aiming to enhance available habitat for multiple species with different dispersal abilities. Our approach can significantly improve the outcome of a restoration program. It is flexible and can aid policy makers in optimizing restoration efforts given the biodiversity conservation goals and available resources. • Restoration effectiveness was evaluated in terms of habitat availability. • Grain-for-Green Program increased forest cover rather than habitat availability. • Priority areas can benefit multiple species with different dispersal abilities. • Our framework can promote the efficiency of a restoration program. • The method is flexible and not restricted by biological data deficiencies. [ABSTRACT FROM AUTHOR]

International biodiversity conservation prioritization efforts often focus on biodiversity hotspots or valuable species. However, for most parts of the world, comprehensive species data with acceptable quality are still scarce to support regional priority evaluations. To model the factors that favor a high/important degree of biodiversity and threats; in this study, we provide an alternative conservation priority approach to use when species data are insufficient. Based on a Landsat-derived forest cover map of 2010 of the Eastern Himalaya of China, we defined forest nodes, measured and delineated their importance with the connectivity metric dPC at regional and sub-regional scales. Based on a deforestation map of 2000 to 2010, we simulated deforestation from 2010 to 2030 using the Dinamica EGO software at multiple scales, and calculated the threatened degree of each forest node at an optimal scale. We then ranked the conservation priorities by coupling the measurements of the connectivity importance values and simulated threatened degree of each important forest node. Six forest patches (2.5% of remaining forest in 2010) were ranked as conservation priority patch-I and II. The unprotected parts are recommended to be expanded into or established as new nature reserves. Although species information was not used, the identified forest patches accommodated existing nature reserves (48% overlapped) in this region. As a fast and efficient assessment approach, with outcomes that are valuable for regional conservation planning, this method could be widely used for any forest dominant regions when field data is insufficient to identify conservation priorities at a fine scale. [ABSTRACT FROM AUTHOR]

Abstract: Forest cover change is one of the most important land cover change processes globally, and old-growth forests continue to disappear despite many efforts to protect them. At the same time, many countries are on a trajectory of increasing forest cover, and secondary, plantation, and scrub forests are a growing proportion of global forest cover. Remote sensing is a crucial tool for understanding how forests change in response to forest protection strategies and economic development, but most forest monitoring with satellite imagery does not distinguish old-growth forest from other forest types. Our goal was to measure changes in forest types, and especially old-growth forests, in the biodiversity hotspot of northwest Yunnan in southwest China. Northwest Yunnan is one of the poorest regions in China, and since the 1990s, the Chinese government has legislated strong forest protection and fostered the growth of ecotourism-based economic development. We used Landsat TM/ETM+ and MSS images, Support Vector Machines, and a multi-temporal composite classification technique to analyze change in forest types and the loss of old-growth forest in three distinct periods of forestry policy and ecotourism development from 1974 to 2009. Our analysis showed that logging rates decreased substantially from 1974 to 2009, and the proportion of forest cover increased from 62% in 1990 to 64% in 2009. However, clearing of high-diversity old-growth forest accelerated, from approximately 1100 hectares/year before the logging ban (1990 to 1999), to 1550 hectares/year after the logging ban (1999 to 2009). Paradoxically, old-growth forest clearing accelerated most rapidly where ecotourism was most prominent. Despite increasing overall forest cover, the proportion of old-growth forests declined from 26% in 1990, to 20% in 2009. The majority of forests cleared from 1974 to 1990 returned to either a non-forested land cover type (14%) or non-pine scrub forest (66%) in 2009, and our results suggest that most non-pine scrub forest was not on a successional trajectory towards high-diversity forest stands. That means that despite increasing forest cover, biodiversity likely continues to decline, a trend obscured by simple forest versus non-forest accounting. It also means that rapid development may pose inherent risks to biodiversity, since our study area arguably represents a “best-case scenario” for balancing development with maintenance of biodiversity, given strong forest protection policies and an emphasis on ecotourism development. [Copyright &y& Elsevier]

Abstract: The Hainan gibbon (Nomascus hainanus) is one of the most endangered primates in the world, confined to mature natural forest in Hainan Island, China. We assessed changes in habitat condition on the island between 1991 and 2008, using vegetation maps generated by remote-sensing images. We defined forest suitable for gibbons based on composition, tree size and canopy cover. During the 17-year period, the area of suitable gibbon forest decreased by 540km2 (35%) across the whole island, and by 6.3km2 (7%) in the locality of the sole remaining gibbon population at Bawangling National Nature Reserve. The forest patches large enough (>1km2) to support a gibbon group decreased from 754km2 to 316km2 in total area, and from 92 to 64 in number. Suitable natural forest was mainly replaced by plantations below 760m, or degraded by logging, grazing and planting of pines above 760m. Meanwhile, forests in former confirmed gibbon areas became more fragmented: mean area of patches decreased by 53%. We mapped the patches of natural forest in good condition which could potentially support gibbons. We recommend a freeze on further expansion of plantations between core patches at Bawangling, Jiaxi-Houmiling and Yinggeling Nature Reserves in accordance with forest protection regulations; establishment of nature reserves in currently unprotected natural forest patches elsewhere in line with the local government’s nature reserve expansion policy; and active natural-forest restoration between remaining fragments at Bawangling. [Copyright &y& Elsevier]