Remotely-sensed analysis of ground-cover change in Queensland’s rangelands, 1988–2005

2014 ◽  
Vol 36 (2) ◽  
pp. 191 ◽  
Author(s):  
G. Bastin ◽  
R. Denham ◽  
P. Scarth ◽  
A. Sparrow ◽  
V. Chewings

A dynamic reference-cover method and remotely-sensed ground cover were used to determine the change in the state of ~640 000 km2 of rangelands in Queensland at a sub-bioregional scale between 1988 and 2005. The method is based on persistence of ground cover in years of lower rainfall and objectively separates grazing effects on ground cover from those due to inter-annual variation in rainfall. The method is applied only to areas where trees and shrubs were not cleared. An indicator of rangeland state was derived, at Landsat-TM pixel resolution, by subtracting automatically-calculated reference ground cover from actual ground cover and then spatially averaging these deviations across the area of each sub-bioregion. Landscape heterogeneity may affect reference cover but, because it is stable over time, change in mean cover deficit between sequences of dry years reliably indicates change due to grazing. All 34 sub-regions analysed had similar or increased levels of seasonally-adjusted ground cover at the end of the analysis period, which was either 2003 or 2005. Allowing for possible landscape heterogeneity effects on assessed condition, the Einasleigh Uplands bioregion was comparatively in a better state and those analysed parts of the Mulga Lands bioregion in poorer state at the first assessment in 1988. Most sub-regions of the Cape York Peninsula, Brigalow Belt North, Desert Uplands, Gulf Plains and Mitchell Grass Downs bioregions lay between these two end-states. Simulated levels of pasture utilisation based on modelled pasture growth and statistically-based grazing pressure supported the results of this regional assessment of land condition. The dynamic reference-cover method will allow the Queensland Government to monitor future grazing effects on rangeland ground cover between sequences of drier years – quantitatively and efficiently across the entire state. The method can potentially be adapted to other rangeland jurisdictions where a suitable multi-temporal database of remotely sensed ground cover exists. The results from further analyses of remotely sensed ground cover will be reported through the Australian Collaborative Rangelands Information System.

2021 ◽  
Vol 43 (1) ◽  
pp. 55
Author(s):  
Terrence S. Beutel ◽  
Robert Shepherd ◽  
Robert A. Karfs ◽  
Brett N. Abbott ◽  
Teresa Eyre ◽  
...  

Remotely sensed ground cover data play an important role in Australian rangelands research development and extension, reflecting broader global trends in the use of remotely sensed data. We tested the relationship between remotely sensed ground cover data and field-based assessments of grazing land condition in the largest quantitative analysis of its type to date. We collated land condition data from 2282 sites evaluated between 2004 and 2018 in the Burdekin and Fitzroy regions of Queensland. Condition was defined using the Grazing Land Management land condition framework that ranks grazing land condition on a four point ordinal scale based on dimensions of vegetation composition, ground cover level and erosion severity. Nine separate ground cover derived indices were then calculated for each site. We found that all indices significantly correlated with grazing land condition on corresponding sites. Highest correlations occurred with indices that benchmarked ground cover at the site against regional ground cover assessed over several years. These findings provide some validation for the general use of ground cover data as an indicator of rangeland health/productivity. We also constructed univariate land condition models with a subset of these indices. Our models predicted land condition significantly better than random assignment though only moderately well; no model correctly predicted land condition class on >40% of sites. While the best models predicted condition correctly at >60% of A and D condition sites, condition at sites in B and C condition sites was poorly predicted. Several factors limit how well ground cover levels predict land condition. The main challenge is modelling a multidimensional value (grazing land condition) with a unidimensional ground cover measurement. We suggest that better land condition models require a range of predictors to address this multidimensionality but cover indices can make a substantial contribution in this context.


2010 ◽  
Vol 10 (11) ◽  
pp. 2235-2240 ◽  
Author(s):  
D. G. Hadjimitsis

Abstract. The aim of this study is to quantify the actual urbanization activity near the catchment area in the urban area of interest located in the vicinity of the Agriokalamin River area of Kissonerga Village in Paphos District. Remotely sensed data such as aerial photos, Landsat-5/7 TM/ETM+ and Quickbird image data have been used to track the urbanization activity from 1963 to 2008. In-situ GPS measurements have been used to locate in-situ the boundaries of the catchment area. The results clearly illustrate that tremendous urban development has taken place ranging from 0.9 to 33% from 1963 to 2008, respectively. A flood risk assessment and hydraulic analysis were also performed.


2017 ◽  
Vol 10 (21) ◽  
Author(s):  
Saeed Ojaghi ◽  
Farshid Farnood Ahmadi ◽  
Hamid Ebadi ◽  
Raechel Bianchetti

Author(s):  
Ali Ben Abbes ◽  
Imed Riadh Farah

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).


2015 ◽  
Vol 77 (2) ◽  
pp. 959-985 ◽  
Author(s):  
Fajar Yulianto ◽  
Parwati Sofan ◽  
Any Zubaidah ◽  
Kusumaning Ayu Dyah Sukowati ◽  
Junita Monika Pasaribu ◽  
...  

2006 ◽  
Vol 6 (2) ◽  
pp. 327-336 ◽  
Author(s):  
John A. Ludwig ◽  
Robert W. Eager ◽  
Adam C. Liedloff ◽  
Gary N. Bastin ◽  
Vanessa H. Chewings
Keyword(s):  

2018 ◽  
Vol 159 ◽  
pp. 56-65 ◽  
Author(s):  
T. Heyer ◽  
H. Hiesinger ◽  
D. Reiss ◽  
G. Erkeling ◽  
H. Bernhardt ◽  
...  

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