Abstract
Vegetation distribution maps from remote sensors play an important role in urban planning, environmental protecting and related policy making. The normalized difference vegetation index (NDVI) is the most popular approach to generate vegetation maps for remote sensing imagery. However, NDVI is usually used to generate lower resolution vegetation maps, and particularly the threshold needs to be chosen manually for extracting required vegetation information. To tackle this threshold selection problem for IKONOS imagery, a fixed-threshold approach is developed in this work, which integrates with an extended Tasseled Cap transformation and a designed image fusion method to generate high-resolution (1-meter) vegetation maps. Our experimental results are promising and show it can generate more accurate and useful vegetation maps for IKONOS imagery.
Highlights
In the past decades, a considerable number of new technologies and methods to generate vegetation maps from remote sensing imagery had been developed, including a variety of sensors cooperating with different scale imagery that is interesting and important to urban planners and land managers [1].To generate a vegetation index (VI) using the spectrum characteristics of sensors, Jordan [2] used the ratio of near infrared to red to estimate leaf biomass
To cope with this problem, we propose a new method to generate a high-resolution and better visual-interpretation vegetation map for IKONOS imagery with a fixed threshold
In contrast with normalized difference vegetation index (NDVI), the image of NDVI and its histogram are shown in Figures 2(d) and (e), respectively
Summary
A considerable number of new technologies and methods to generate vegetation maps from remote sensing imagery had been developed, including a variety of sensors cooperating with different scale imagery that is interesting and important to urban planners and land managers [1]. After IKONOS was launched and started to provide high-resolution imagery (4-meter multispectral and 1-meter panchromatic), most details of buildings, individual trees, and vegetation structural variations can be well detected with 1-meter spatial resolution images. It provides a new data source for monitoring agricultural production, and giving information for the development of crops during the growing season [7]. A proper threshold is not easy to be decided for extracting vegetation information To cope with this problem, we propose a new method to generate a high-resolution and better visual-interpretation vegetation map for IKONOS imagery with a fixed threshold. The experimental results demonstrate that the proposed approach can generate a high-resolution and more discriminable vegetation map for IKONOS imagery efficiently
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