Improved Characterization of Dryland Degradation Using Trends in Vegetation/ Rainfall Sequential Linear Regression (Sergs-Trend)

Land degradation in drylands has been investigated extensively over recent decades and several remote sensing based techniques attempt to decouple the human influence from the natural climate variability, but are contested in literature. We introduce a novel approach termed SeRGS-TREND that is designed to monitor land degradation by suppressing the impact from climate variability and highlight vegetation disturbances may it be human or climate-induced. SeRGS-TREND is based on the interpretation of the slope of a linear regression analysis within a sequentially moving window along the temporal axis of the time series of remote sensing data. The use of a moving window increases the probability of a statistically significant linear vegetation-rainfall relationship (VRR), which in turn provides an improved statistical basis for the results produced and thereby confidence in the assessment of degradation. We test and compare SeRGS- Trend and the commonly used RESTREND by simulating different degradation scenarios and find that SeRGS reveals both, more significant and more exact information about degradation events (e.g. starting and end point) while keeping the VRR correlation coefficients high, thus rendering results more reliable.

[1]  Michael E. Schaepman,et al.  Desertification in the Sahel: Towards better accounting for ecosystem dynamics in the interpretation of remote sensing images , 2011 .

[2]  F. van den Bergh,et al.  Limits to detectability of land degradation by trend analysis of vegetation index data , 2012 .

[3]  Rasmus Fensholt,et al.  Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers , 2012 .

[4]  A. Giannini.  Hydrology: Climate change comes to the Sahel , 2015 .

[5]  J. Evans,et al.  Discrimination between climate and human-induced dryland degradation. , 2004 .

[6]  Le Houérou,et al.  Rain use efficiency: a unifying concept in arid-land ecology , 1984 .

[7]  Jan Verbesselt,et al.  Revealing turning points in ecosystem functioning over the Northern Eurasian agricultural frontier , 2016, Global change biology.

[8]  Compton J. Tucker,et al.  Thirty-two Years of Sahelian Zone Growing Season Non-Stationary NDVI3g Patterns and Trends , 2014, Remote. Sens..

[9]  Yi Y. Liu,et al.  Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) , 2017 .

[10]  L. Hein,et al.  Desertification in the Sahel: a reinterpretation , 2006 .

[11]  Laurent Tits,et al.  Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006 , 2017, Remote. Sens..

[12]  Heiko Balzter,et al.  Land Degradation Assessment Using Residual Trend Analysis of GIMMS NDVI3g, Soil Moisture and Rainfall in Sub-Saharan West Africa from 1982 to 2012 , 2015, Remote. Sens..

[13]  J. Ardö,et al.  A recent greening of the Sahel—trends, patterns and potential causes , 2005 .

[14]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[15]  Stephen D. Prince,et al.  Where Does Desertification Occur? Mapping Dryland Degradation at Regional to Global Scales , 2016 .

[16]  Rasmus Fensholt,et al.  Analysis of trends in the Sahelian `rain-use efficiency' using GIMMS NDVI, RFE and GPCP rainfall data , 2011 .

[17]  Rasmus Fensholt,et al.  Assessing Land Degradation/Recovery in the African Sahel from Long-Term Earth Observation Based Primary Productivity and Precipitation Relationships , 2013, Remote. Sens..