Is ground cover a useful indicator of grazing land condition?

2021 ◽  
Vol 43 (1) ◽  
pp. 55
Author(s):  
Terrence S. Beutel ◽  
Robert Shepherd ◽  
Robert A. Karfs ◽  
Brett N. Abbott ◽  
Teresa Eyre ◽  
...  
Keyword(s):  
Ground Cover ◽  
Remotely Sensed ◽  
Ordinal Scale ◽  
Substantial Contribution ◽  
Remotely Sensed Data ◽  
Grazing Land ◽  
Global Trends ◽  
Main Challenge ◽  
Land Condition ◽  
Rangeland Health

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.

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
Keyword(s):  
Landscape Heterogeneity ◽  
Ground Cover ◽  
Remotely Sensed ◽  
Grazing Pressure ◽  
Temporal Database ◽  
Pixel Resolution ◽  
Grazing Effects ◽  
Multi Temporal ◽  
Land Condition ◽  
Change In Mean

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.

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

Sample size for ground and remotely sensed data

1986 ◽  
Vol 20 (1) ◽  
pp. 31-41 ◽  
Author(s):  
P.J. Curran ◽  
H.D. Williamson
Keyword(s):  
Sample Size ◽  
Remotely Sensed ◽  
Remotely Sensed Data
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