Author

Jack Murphy

Year

2015

Degree Name

BEnvSci Adv Hons

ANZSRC / FoR Code

05 ENVIRONMENTAL SCIENCES, 0502 ENVIRONMENTAL SCIENCE AND MANAGEMENT, 050205 Environmental Management

Department

School of Earth & Environmental Sciences

Advisor(s)

Laurie Chisholm

Abstract

In Australia the sustainability of pastoral lands is a major concern for graziers and land managers (Bennett et al., 2014, Tothill, 1992). Especially, in Sydney’s Drinking Water Catchment where approximately 600,000 hectares of land is classified as livestock grazing land (Sydney Catchment Authority, 2015). Poor management of this pastoral land has the potential to lead to increases in runoff and soil loss as a result of reduced pasture cover. Potentially leading to both extensive land degradation and a severe reduction of water quality in the catchment (Bartley et al., 2012, Bennett et al., 2014, Mcivor et al., 1995). It is therefore necessary that action is taken to manage and monitor the quality and cover of pastoral lands in the catchment. Historically, monitoring has been manually conducted via in-situ field techniques (Menzel et al., 2006); however, advances in technology especially in the field of remote sensing have allowed for modelling to reduce and in some instances completely remove the need for field studies. Allowing for much more time and cost effective monitoring and management (Biondini et al., 1991, Sala et al., 1988). The main aim of this study was to develop a statistical model using MODIS 16 day image composites to detect temporal changes in pasture cover in Sydney’s Drinking Water Catchment. To complete this aim three principal objectives were developed: (1) to determine if temporal change is able to be detected within the catchment via statistical modelling, (2) which vegetation indices are more suited to detecting temporal changes in pasture cover in the catchment and, (3) determine if there is any apparent relationship between sustainable pasture management practices and improved pasture cover in the catchment. Two vegetation indices were tested in the spatiotemporal statistical model developed; the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). Findings from the study-indicated that the spatiotemporal statistical model developed is able to successfully determine change within the catchment using either NDVI or EVI values, as represented by the positive results achieved in the two-way ANOVA test when analysing time periods impact on pasture cover (P-value 5.206e-6 [NDVI] and 0.0134 [EVI]). However, EVI was found to be more sensitive in detecting temporal changes in pasture cover then NDVI when run through the two-way ANOVA test (P-value 0.0009 [EVI] and 0.3627 [NDVI]). Findings from the study also showed a significant relationship between sustainable pasture management practices and pasture cover when EVI values where run through the Mann Whitney U Test (P-value change from 0.0767 to 0.00529 post management implementation). Key implications of this study are that temporal changes in pasture cover in Sydney’s Drinking Water Catchment are able to be quantified and monitored, when modelling temporal changes in pasture cover in Sydney’s Drinking Water Catchment the EVI is the most suitable index to be used and sustainable management practices implemented in the catchment have been successful in improving pasture cover since beginning in 2008.

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