Scale dependence of forest fragmentation and its climate sensitivity in a semi-arid mountain:Comparing Landsat,Sentinel and Google Earth data

Landscape fragmentation is generally viewed as an indicator of environmental stresses or risks,but the fragmentation intensity assessment also depends on the scale of data and the definition of spatial unit.This study aimed to explore the scale-dependence of forest fragmentation intensity along a moisture gradient in Yinshan Mountain of North China,and to estimate environmental sensitivity of forest fragmentation in this semi-arid landscape.We developed an automatic classification algorithm using simple linear iterative clustering(SLIC)and Gaussian mixture model(GMM),and extracted tree canopy patches from Google Earth images(GEI),with an accuracy of 89.2%in the study area.Then we convert the tree canopy patches to forest category according to definition of forest that tree density greater than 10%,and compared it with forest categories from global land use datasets,FROM-GLC10 and GlobeLand30,with spatial resolutions of 10 m and 30 m,respectively.We found that the FROM-GLC10 and GlobeLand30 datasets underestimated the forest area in Yinshan Mountain by 16.88%and 21.06%,respectively;and the ratio of open forest(OF,10%<tree coverage<40%)to closed forest(CF,tree coverage>40%)areas in the underestimated part was 2:1.The underestimations concentrated in warmer and drier areas occupied mostly by large coverage of OFs with severely fragmented canopies.Fragmentation intensity of canopies positively correlated with spring temperature while negatively correlated with summer precipitation and terrain slope.When summer precipitation was less than 300 mm or spring temperature higher than 4℃,canopy fragmentation intensity rose drastically,while the forest area percentage kept stable.Our study suggested that the spatial configuration,e.g.,sparseness,is more sensitive to drought stress than area percentage.This highlights the importance of data resolution and proper fragmentation measurements for forest patterns and environmental interpretation,which is the base of reliable ecosystem predictions with regard to the future climate scenarios.

Land Use Structure in Wuhai City on Basis of Ecological Green Equivalence

The paper took Wuhai District as an example, bases on the contrast be-tween the land use condition in 2005 and 2010, and applied the ecological green equivalence to establish a mathematic model of ecological optimization of land use structure, to establish the ecology green equivalent mathematical model and survey the value of region green equivalent, and then assess the ecological environment situation. The results show that the ecological environment has been deteriorated in Wuhai from 2005 to 2010, so the ecological environment was poor. In order to in-crease eco-efficiency of land use, garden, urban green space and woodland area should be raised in the optimization program.

A New Indicator for Modern Forestry Development: Forest Thickness

Modern forestry focusing on ecological dimension needs some new indicators different from those designed for the traditional forestry. On the basis of the evaluation indicators for traditional forestry, such as forest coverage, total standing stock volume and forest volume per unit area, the paper came up with a new indicator called "forest thickness" (FT). FT refers to the thickness which can be got if using total stock volume of forest and trees to cover the total land in a given area, and it is charged by mm. This indicator can be used to solve the long-term problem that the forest coverage can not reflect in a comprehensive way the level of forest ecological development, and provide the easy and clear approach to measuring and comparing the levels of forest ecological development in different periods at different regions. When the indicator value is over 9 mm, the ecosystem is indicated as excellent; when in 6-9 mm, the ecosystem is indicated as good; when in 3-6 mm, the ecosystem is indicated as moderately good; when below 3 mm, the ecosystem is indicated as bad. The indicator values of FT are calculated for the world, 17 countries, and different historic periods and different regions of China, based on which the forestry ecological status in these areas are compared and evaluated.