Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas

Abstract

Numerous large-area, multiple image-based, multiple sensor land cover mapping programs exist or have been proposed, often within the context of national forest monitoring, mapping and modelling initiatives, worldwide. Common methodological steps have been identified that include data acquisition and preprocessing, map legend development, classification approach, stratification, incorporation of ancillary data and accuracy assessment. In general, procedures used in any large-area land cover classification must be robust and repeatable; because of data acquisition parameters, it is likely that compilation of the maps based on the classification will occur with original image acquisitions of different seasonality and perhaps acquired in different years and by different sensors. This situation poses some new challenges beyond those encountered in large-area single image classifications. The objective of this paper is to review and assess general medium spatial resolution satellite remote sensing land cover classification approaches with the goal of identifying the outstanding issues that must be overcome in order to implement a large-area, land cover classification protocol.

A comparative evaluation of endemic and non-endemic region of visceral leishmaniasis (Kala-azar) in India with ground survey and space technology

“…The physiographical characteristics of the surface environment derived from the land cover data play an important role in the dynamics of leishmaniasis transmission (Defries & Townshend 1999). Different land cover classification approaches vary in their potential for discernment and consequently in their utility for meeting specific needs (e.g., the identification of all examples of a particular habitat) (Rogers 1991, Patza et al 2000, Franklin & Wulder 2002. FA analysis results corroborated breeding preferences of P. argentipes by landscape elements.…”

A Survey of Remote Sensing Image Classification Approaches

“…Generally speaking, image classification approaches can be grouped as supervised and unsupervised, or parametric and nonparametric, or hard and soft classification, which is detailed in [10,11,28,29].…”

Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

“…Review papers on the use of remote sensing for land use land cover classification are provided in Bastiaanssen (1998), Smits et al (1999), Mucher et al (2000), Cihlar (2000), Franklin and Wulder (2002), Thenkabail et al (2009b), and García-Mora et al (2012).…”