Comparison of methods for land-use classification incorporating remote sensing and GIS inputs|
Offer Rozenstein, and Arnon Karnieli
Over the last few decades, dramatic land-use changes have occurred throughout Israel.
Previously-grazed areas have been afforested, converted to irrigated or rain-fed agriculture, turned into natural reserves, often used as
large military training sites, converted to rural and urban settlements, or left unused. Land-use maps provided by the Israeli government
are more detailed for agricultural and urban land-use classes than for others. While rangelands still account for a substantial part of the
northern Negev, their extent today is not well defined. In light of continuous land-use changes and lack of regard to rangelands in existing
land-use maps, there is a need for creating a current land-use information data base, to be utilized by planners, scientists, and decision
makers. Remote-sensing (RS) data are a viable source of data from which land-use maps could be created and updated efficiently. The
purpose of this work is to explore low-cost techniques for combining current satellite RS data together with data from the Israeli Geographic
Information System (GIS) in order to create a relatively accurate and current land-use map for the northern Negev. Several established
methods for land-use classification from RS data were compared. In addition, ancillary land-use data were used to update and improve the
RS classification accuracy within a GIS framework. It was found that using a combination of supervised and unsupervised training classes
produces a more accurate product than when using either of them separately. It was also found that updating this product using ancillary data
and GIS techniques can improve the product accuracy by up to 10%. The final product overall accuracy was 81%. It is suggested that applying
the presented technique for more RS images taken at different times can facilitate the creation of a database for land-use changes.