Abstract
A major threat to biodiversity in North Dakota is the conversion of forested land to cultivable land, especially those that act as riparian buffers. To reverse this trend of transformation, a validation and prediction model is necessary to assess the change. Spatial prediction within a Geographic Information System (GIS) using Kriging is a popular stochastic method. The objective of this study was to predict spatial and temporal transformation of a small agricultural watershed—Pipestem Creek in North Dakota; USA using satellite imagery from 1976 to 2015. To enhance the difference between forested land and non-forested land, a spectral transformation method—Tasseled-Cap’s Greenness Index (TCGI) was used. To study the spatial structure present in the imagery within the study period, semivariograms were generated. The Kriging prediction maps were post-classified using Remote Sensing techniques of change detection to obtain the direction and intensity of forest to non-forest change. TCGI generated higher values from 1976 to 2000 and it gradually reduced from 2000 to 2011 indicating loss of forested land.
Highlights
Sustainable use of riverine systems and riparian habitats are directly affected by changing land use patterns [1]
The images generated for Normalized Difference Vegetation Index (NDVI) (Figure 5) for years 1976 to 2015 showed similar results to Tasseled-Cap’s Greenness Index (TCGI) when compared visually, but the spectral separability analysis generated low standard deviation for TCGI which is indicative of data clustering around the mean, implying data reliability
TCGI was selected to describe the spatial surface patterns since it produced the best contrast in terms of separability among the spectral indices
Summary
Sustainable use of riverine systems and riparian habitats are directly affected by changing land use patterns [1]. Modeling land use patterns is an important technique for the projection of alternative pathways into the future [2] [3] [4]. Satellite data is cost effective and the information obtained from them can be used as inputs to build land use and land cover datasets [8]. To elucidate the optimal use of land and to provide input data for watershed models, it is necessary to have information on existing LULC change patterns [10]
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