Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China

Our latest article is not located in the Sahel, however, the method to assess the future persistence of vegetation trends is highly interesting in the context of ecosystem stability and resistance. The article is open access and freely available.

Tong, Xiaowei; Wang, Kelin; Brandt, Martin; Yue, Yuemin; Liao, Chujie; Fensholt, Rasmus. 2016. “Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China.” Remote Sens. 8, no. 5: 357.


To alleviate the severe rocky desertification and improve the ecological conditions in Southwest China, the national and local Chinese governments have implemented a series of Ecological Restoration Projects since the late 1990s. In this context, remote sensing can be a valuable tool for conservation management by monitoring vegetation dynamics, projecting the persistence of vegetation trends and identifying areas of interest for upcoming restoration measures.

In this study, we use MODIS satellite time series (2001–2013) and the Hurst exponent to classify the study area (Guizhou and Guangxi Provinces) according to the persistence of future vegetation trends (positive, anti-persistent positive, negative, anti-persistent negative, stable or uncertain). The persistence of trends is interrelated with terrain conditions (elevation and slope angle) and results in an index providing information on the restoration prospects and associated uncertainty of different terrain classes found in the study area.

The results show that 69% of the observed trends are persistent beyond 2013, with 57% being stable, 10% positive, 5% anti-persistent positive, 3% negative, 1% anti-persistent negative and 24% uncertain. Most negative development is found in areas of high anthropogenic influence (low elevation and slope), as compared to areas of rough terrain. We further show that the uncertainty increases with the elevation and slope angle, and areas characterized by both high elevation and slope angle need special attention to prevent degradation. Whereas areas with a low elevation and slope angle appear to be less susceptible and relevant for restoration efforts (also having a high uncertainty), we identify large areas of medium elevation and slope where positive future trends are likely to happen if adequate measures are utilized.

The proposed framework of this analysis has been proven to work well for assessing restoration prospects in the study area, and due to the generic design, the method is expected to be applicable for other areas of complex landscapes in the world to explore future trends of vegetation.


The Hurst exponent, a measure of the persistence of a time series, can be easily calculated on a raster time series in R:


# set working directory with the raster files, here in TIF format, 
# and load the files in a rasterbrick

gsn = brick(list.files(pattern='*.tif'))
# test on the average of the study area

g=cellStats(gsn, stat='mean')
gsn.ts = ts(g, start=c(2001,1), end=c(2013,1), frequency=1)


# run the function on the rasterbrick gsn

fun=function(x) { 
  if ([1])){ NA } else
  gsn.ts = ts(v, start=c(2001,1), end=c(2013,1), frequency=1)
  x=hurstexp(gsn.ts, display=F)$Hs 
h <- calc(gsn, fun)


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