Dynamic analysis of Robinia pseudoacacia forest health levels from 1995 to 2013 in the Yellow River Delta, China using multitemporal Landsat imagery

Abstract

ABSTRACTThe Robinia pseudoacacia forest in the Yellow River Delta (YRD), China, was planted in the 1970s and has continuously suffered dieback and mortality since the 1990s. Timely and accurate information on forest growth and forest condition and its dynamic change as well is essential for assessing and developing effective management strategies. In this study, multitemporal Landsat imagery was used to analyze and monitor changes of the R. pseudoacacia forest in the YRD from 1995 to 2013. To do so, Landsat image band reflectance, three fraction images calculated by using a multiple endmember spectral mixture analysis (MESMA) method, and four vegetation indices (VIs) were used to discriminate three health levels of R. pseudoacacia forest in years 1995, 2007, and 2013 with a random forest (RF) classifier. The four VIs include a difference infrared index (DII) developed in this study, normalized difference vegetation index, soil-adjusted vegetation index, and normalized difference infrared index (NDII), all of which were computed from Landsat Thematic Mapper and Operational Land Imager multispectral (MS) bands. The dynamic changes of the forest health levels during the periods of 1995–2007 and 2007–2013 were analysed. The analysis results demonstrate that three fraction images created by MESMA method and four VIs were powerful in separating the three forest health levels. In addition to the Landsat MS bands, the additional three fraction images increased the classification accuracy by 14−20%; if coupled with the four VIs, the overall accuracy was further increased by 5−6%. According to the importance values calculated by RF classifier for all input features, the DII vegetation index was the second effective feature, outperforming NDII. From 1995 to 2013, a total of 2615 ha of forest in the study area suffered from mortality or loss.