Abstract
Land use/land cover (LULC) classification is a key research field in environmental applications of remote sensing on the earthfs surface. The advent of new high resolution multispectral sensors with unique bands has provided an opportunity to map the spatial distribution of detailed LULC classes over a large fragmented area. The objectives of the present study were: (1) to map LULC classes using multispectral WorldView-2 (WV-2) data and SVM in a fragmented ecosystem; and (2) to compare the accuracy of three WV-2 spectral data sets in distinguishing amongst various LULC classes in a fragmented ecosystem. WV-2 image was spectrally resized to its four standard bands (SB: blue, green, red and near infrared-1) and four strategically located bands (AB: coastal blue, yellow, red edge and near infrared-2). WV-2 image (8bands: 8B) together with SB and AB subsets were used to classify LULC using support vector machines. Overall classification accuracies of 78.0% (total disagreement = 22.0%) for 8B, 51.0% (total disagreement = 49.0%) for SB, and 64.0% (total disagreement = 36.0%) for AB were achieved. There were significant differences between the performance of all WV-2 subset pair comparisons (8B versus SB, 8B versus AB and SB versus AB) as demonstrated by the results of McNemarfs test (Z score .1.96). This study concludes that WV-2 multispectral data and the SVM classifier have the potential to map LULC classes in a fragmented ecosystem. The study also offers relatively accurate information that is important for the indigenous forest managers in KwaZulu-Natal, South Africa for making informed decisions regarding conservation and management of LULC patterns. Keywords : land use/cover classification, fragmented ecosystem, WorldView-2, support vector machines
Highlights
Land use/land cover (LULC) is a fundamental variable that influences and links with many parts of human and physical systems and is a vital data component for many aspects of environmental change (Foody, 2002, Otukei and Blaschke, 2010)
Our study showed that support vector machines (SVM) classifier was unable to fully deal with the high spectral variation inherent in some LULC classes like mature sugarcane and grassland which obtained relatively lower user’s accuracy (UA) and producer’s accuracy (PA).This is a common problem when classifying heterogeneous landscapes using high spatial resolution (WV-2 image) based on per-pixel classification techniques (Lu and Weng, 2007)
The present study shows a successful application of multispectral WV-2 data and the machine learning SVM classifier in mapping eight LULC classes in a fragmented ecosystem
Summary
Land use/land cover (LULC) is a fundamental variable that influences and links with many parts of human and physical systems and is a vital data component for many aspects of environmental change (Foody, 2002, Otukei and Blaschke, 2010). Indigenous forests are fragmented into patches of various sizes and shapes surrounded by a matrix of different LULC classes (Benitez-Malvido, 1998, Cho et al, 2013) In this context, information relating to the dynamics, distribution and productivity of LULC is beneficial to the source of economic security (Eldeen, 2005, van Wyk, 2008), but is needed for fragmented ecosystems inventory, management and monitoring (Cingolani et al, 2004, Pignatti et al, 2009, Cho et al, 2013). It is quite difficult and challenging to produce LULC maps using traditional field survey approaches
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