Studying long-term vegetation dynamics using digital processing of historical aerial photographs

Ronen Kadmon*, Ruthie Harari-Kremer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

174 Scopus citations

Abstract

Plant ecologists have long recognized the importance of aerial photographs as a data source for studies of vegetation dynamics. Recent advances in computer-aided technology (digital photogrammetry, computerized image processing, and geographical information systems) have opened new possibilities for the extraction of data on vegetation changes from aerial photographs. In this study we describe a computer-based approach for studying landscape-scale, long-term vegetation dynamics, using historical aerial photographs as a major data source. The method we employ consists of four main steps: 1) image scanning and preprocessing (rectification, georeferencing, spectral corrections and mosaicking), 2) image classification and construction of vegetation maps, 3) field validation, and 4) statistical analysis of vegetation changes. We applied our approach by analyzing changes in tree cover over a period of 32 years in a mountainous landscape dominated by Mediterranean maquis in northern Israel and discuss the main limitations and potential error sources of each stage of our analysis. We conclude that digital processing of historical aerial photographs may serve as a powerful tool for the detection, quantification, and analysis of landscape-scale patterns of vegetation dynamics. This conclusion is important because aerial photographs provide the largest source of information available today for research of long-term vegetation dynamics, and are the only source of information on vegetation dynamics that combines high spatial resolution, large spatial extent, and long-term coverage.

Original languageAmerican English
Pages (from-to)164-176
Number of pages13
JournalRemote Sensing of Environment
Volume68
Issue number2
DOIs
StatePublished - May 1999
Externally publishedYes

Bibliographical note

Funding Information:
We would like to thank A. Ben-Nun, J. Alpert, D. Malkinson, and T. El-Hai for their technical support with the GIS, and to H. Leschner and Y. Sapir for their help in the field. We also thank three anonymous reviewers for their valuable comments on a previous version of this article. The study was supported by the GIS Center of the Hebrew University of Jerusalem.

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