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.

Impact of Conjugated Shear Joint of“X”Type on Macroscopic Karstification on the Basis of the Analysis of Google Earth Images

As viewed from space remote-sensing images （e.g. Google Earth images） of South Guizhou and North Guangxi, the authors found that macroscopic karst landscape on the Earth＇s surface is strongly controlled by the Conjugated shear joint of ＂X＂ type. Joints of this kind constitute a huge infiltration network and act as channel-ways for the permeation of meteoric waters from the surface, thus, leading to the dissolution of carbonate rocks nearby. As a result, the karst landscape is formed, which is dominated by linear karst valleys. An ＂X＂ karst valley network structure appears in the area where horizontal strata are distributed, and a feather-like network structure appears in the area where vertical strata are distributed, respectively. When the water permeates downwards to the underground-water level, it will flow horizontally along the strike of ＂X＂ joints toward the local base level of erosion to form an ＂X＂ network system of underground conduits in the area where horizontal strata are distributed, but it is relatively complex, because of the joining of other joints. This is the first time we have made use of Google Earth images to study the karst environment. Therefore, it has been successful in research on the Earth＇s geomorphology, which could only rely on aerial photos and satellite photos in the past. Google Earth images provide low-cost and applicable imaging materials for the study of Earth＇s geomorphology and karst rocky desertification and its control.

Identifying ephemeral gullies from high-resolution images and DEMs using flow-directional detection

Ephemeral gullies,which are widely developed worldwide and threaten farmlands,have aroused a growing concern.Identifying and mapping gullies are generally considered prerequisites of gully erosion assessment.However,ephemeral gully mapping remains a challenge.In this study,we proposed a flow-directional detection for identifying ephemeral gullies from high-resolution images and digital elevation models(DEMs).Ephemeral gullies exhibit clear linear features in high-resolution images.An edge detection operator was initially used to identify linear features from high-resolution images.Then,according to gully erosion mechanism,the flow-directional detection was designed.Edge images obtained from edge detection and flow directions obtained from DEMs were used to implement the flow-directional detection that detects ephemeral gullies along the flow direction.Results from ten study areas in the Loess Plateau of China showed that ranges of precision,recall,and Fmeasure are 6 o.66%-90.47%,65.74%-94.98%,and63.10%-91.93%,respectively.The proposed method is flexible and can be used with various images and DEMs.However,analysis of the effect of DEM resolution and accuracy showed that DEM resolution only demonstrates a minor effect on the detection results.Conversely,DEM accuracy influences the detection result and is more important than the DEM resolution.The worse the vertical accuracy of DEM,the lower the performance of the flow-directional detection will be.This work is beneficial to research related to monitoring gully erosion and assessing soil loss.