Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series

Remote Sensing

Jonas Lambert²  Jean-Philippe Denux²  Jan Verbesselt¹  Gérard Balent⁴  Véronique Cheret²  Lars T. Waser³  Josef Kellndorfer³ 

[1] Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands; E-Mail:;Université de Toulouse, INPT, Ecole d’Ingénieurs de Purpan, UMR 1201 Dynafor, 75 Voie du TOEC, F-31076 Toulouse Cedex 03, France;id="af1-remotesensing-07-03588">1Université de Toulouse, INPT, Ecole d’Ingénieurs de Purpan, UMR 1201 Dynafor, 75 Voie du TOEC, F-31076 Toulouse Cedex 03, Fran;INRA, UMR 1201 Dynafor, F-31326 Castanet-Tolosan, France; E-Mail:

关键词: time series;    MODIS;    NDVI;    BFAST;    forest decline;    clear-cut detection;   

DOI  :  10.3390/rs70403588

来源: mdpi

【 摘 要 】

This paper examines the potential of MODIS-NDVI time series for detecting clear-cuts in a coniferous forest stand in the south of France. The proposed approach forms part of a survey monitoring the status of forest health and evaluating the forest decline phenomena observed over the last few decades. One of the prerequisites for this survey was that a rapid and easily reproducible method had to be developed that differentiates between forest clear-cuts and changes in forest health induced by environmental factors such as summer droughts. The proposed approach is based on analysis of the breakpoints detected within NDVI time series, using the “Break for Additive Seasonal and Trend” (BFAST) algorithm. To overcome difficulties detecting small areas on the study site, we chose a probabilistic approach based on the use of a conditional inference tree. For model calibration, clear-cut reference data were produced at MODIS resolution (250 m). According to the magnitude of the detected breakpoints, probability classes for the presence of clear-cuts were defined, from greater than 90% to less than 3% probability of a clear-cut. One of the advantages of the probabilistic model is that it allows end users to choose an acceptable level of uncertainty depending on the application. In addition, the use of BFAST allows events to be dated, thus making it possible to perform a retrospective analysis of decreases in forest vitality in the study area.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.