The impact of varying statutory arrangements on spatial data sharing and access in regional NRM bodies

Spatial information plays an important role in many social, environmental and economic decisions and increasingly acknowledged as a national resource essential for wider societal and environmental benefits. Natural Resource Management is one area where spatial information can be used for improved planning and decision making processes. In Australia, state government organisations are the custodians of spatial information necessary for natural resource management and regional NRM bodies are responsible to regional delivery of NRM activities. The access and sharing of spatial information between government agencies and regional NRM bodies is therefore as an important issue for improving natural resource management outcomes. The aim of this paper is to evaluate the current status of spatial information access, sharing and use with varying statutory arrangements and its impacts on spatial data infrastructure (SDI) development in catchment management sector in Australia. Further, it critically examined whether any trends and significant variations exist due to different institutional arrangements (statutory versus non-statutory) or not. A survey method was used to collect primary data from 56 regional natural resource management (NRM) bodies responsible for catchment management in Australia. Descriptive statistics method was used to show the similarities and differences between statutory and non-statutory arrangements. The key factors which influence sharing and access to spatial information are also explored. The results show the current statutory and administrative arrangements and regional focus for natural resource management is reasonable from a spatial information management perspective and provides an opportunity for building SDI at the catchment scale. However, effective institutional arrangements should align catchment SDI development activities with sub-national and national SDI development activities to address catchment management issues. We found minor differences in spatial information access, use and sharing due to varying institutional environment (statutory versus non-statutory). The non-statutory group appears to be more flexible and self-sufficient whilst statutory regional NRM bodies may lack flexibility in their spatial information management practices. We found spatial information access, use and sharing has significant impacts on spatial data infrastructure development in catchment management sector in Australia

Selecting Site Suitable for Animal Waste Application using a Vector GIS

Due to the increase in the number and size of intensive animal industries (IAI) in many parts of the world including Australia, the disposal of animal waste has become a pressing environmental problem. Frequently the wastes generated at IAI are conveniently, favourably, and cost-effectively applied to the nearby agricultural fields to recycle manure nutrients. However, excessive application of wastes in the nearby fields without due consideration of site-specific factors (eg. slope, soil, and watercourses) has resulted in the run-off and leaching losses of manure nutrients causing agricultural non-point source (NPS) pollution (He and Shi, 1998). The agricultural NPS pollution has contributed significantly to the eutrophication and toxic blue green algae blooms in many river systems including Murray-Darling, where world's largest toxic riverine algal bloom was recorded in 1991 (Kuhn, 1993). Hence it has become crucial to develop an animal waste application guide (i.e. a site suitability map) by considering biophysical and socio-economic factors to minimise the environmental hazards. Developing such a map requires consideration of many factors and their spatial variability. Geographic information system (GIS)offers site suitability analysis techniques that are capable of processing large volumes of spatial data (Davis, 1996). The objective of this study is to develop a suitability map using a vector GIS, and to evaluate the factor sensitivity and aptness of this technique in selecting suitable sites for animal waste application

Quantifying landscape fragmentation in the Lockyer Valley Catchment, Queensland: 1973 – 1997

Fragmentation has become a central issue in landscape ecology and conservation. The breaking up of large land areas into smaller patches is known to influence many ecological patterns and processes. Thus, landscape fragmentation needs to be assessed and monitored. In this study of the Lockyer Creek Catchment in Queensland, landscape fragmentation was quantified using 1973 and 1997 Landsat images and other thematic layers. Landscape metrics (focusing on the size, shape, density, and isolation of woody vegetation) were calculated using the Patch Analyst (Grid) extension of ArcView GIS. The nature of fragmentation was further characterised based on landscape features including land use/cover, tenure, slope, as well as distance to roads and streams

Relating Satellite Imagery with Grain Protein Content

Satellite images, captured during the growing seasons of barley, sorghum and wheat were analysed to establish a relationship between the spectral response and the harvested grain protein content. This study was conducted near Jimbour (approx. 151 degrees 10'E and 27 degrees 05'S) in southern Queensland. Grain protein contents of the geo-referenced samples, collected manually during the harvest, were determined using a laboratory-based near-infrared spectrophotometer. Grain protein contents in grain varied between 7.4 - 15.2% in barley, 6.2 - 10.6% in sorghum and 13.1 - 15.6% in wheat. The Landsat images of 18 September 1999 (a week after barley flowering), 5 March 2000 (three weeks before sorghum harvest), and 15 August 2001 (two weeks before wheat flowering) were analysed. Additionally, an ASTER image of 24 September 2001 (three weeks after wheat flowering) was also examined. Digital numbers, extracted from raw image bands and derived indices, were correlated with grain protein contents. The grain protein content in barley was correlated strongly (r>0.80) with bands 2, 4 and 5 of the Landsat scene, first principal component, and the tasselled cap brightness and greenness indices. Similarly, wheat protein content was well correlated (r>0.75) with the near infrared band (band 4) of the Landsat scene, first principal component, and the tasselled cap brightness, greenness and wetness indices. The band 3 (near infrared band) of the ASTER image, captured well after flowering, was moderately correlated (

Comparison of three models of population density estimation for Central American red brocket deer (Mazama temama)

The deer Mazama temama has a wide range in the tropical rain forest of Mexico, but the IUCN classifies it as Data Deficient, and information is urgently need for management and conservation. Here we assess which population density estimation model is more appropriate among those by Tyson (1959), Mandujano and Jones (2005) and Crego and Macri (2009). We compare them with field data from Tepetla (Puebla, Mexico), from 2015 to 2017, with three replicates in the wet season and three in dry season. An ANOVA indicated that the three methods produce equivalent results

Prioritising carbon sequestration areas in southern Queensland using time series MODIS net primary productivity (NPP) imagery

The aim of this study was to develop a method that will use satellite imagery to identify areas of high forest growth and productivity, as a primary input in prioritising revegetation sites for carbon sequestration. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, this study analysed the annual net primary production (NPP) values (gC/m2) of images acquired from 2000 to 2013, covering the Condamine Catchment in southeast Queensland, Australia. With the analysis of annual rainfall data during the same period, three transitions of 'normal to dry' years were identified to represent the future climate scenario considered in this study. The difference in the corresponding NPP values for each year was calculated, and subsequently averaged to the get the 'Mean of Annual NPP Difference' (MAND) map. This layer identified the areas with increased net primary production despite the drought condition in those years. Combined with key thematic maps (i.e. regional ecosystems, land use, and tree canopy cover), the priority areas were mapped. The results have shown that there are over 42 regional ecosystem (RE) types in the study area that exhibited positive vegetation growth and productivity despite the decrease in annual rainfall. However, seven (7) of these RE types represents the majority (79%) of the total high productivity area. A total of 10,736 ha were mapped as priority revegetation areas. This study demonstrated the use of MODIS-NPP imagery to map vegetation with high carbon sequestration rates necessary in prioritising revegetation sites

Appraisal of Potato Production Practices in the Adamawa and West regions of Cameroon. Baseline Survey Report

We conducted a baseline survey to appraise the current potato practices and farming systems in the Adamawa and West regions of Cameroon where a GIZ-funded project (ONE WORLD – No Hunger, or SEWOH) titled "Green Innovation Centers for the Agricultural and Food Sector" (ProCISA) is being implemented. We employed a mix of qualitative and quantitative methods to gather and analyze data/information. This study focused on potato farmers that have been into potato production for at least two years. We developed a structured questionnaire which was administered to small- and medium-sized potato growers based on a literature review and consultation with key stakeholders. The questionnaire was pretested through ten in-depth interviews with selected farmers in the Adamawa and West regions. Local enumerators were recruited in each region and trained to administer the questionnaire in the field. In total, 341 questionnaires were completed (141 in Adamawa and 200 in the West region) in 133 villages (35 in Adamawa and 98 in the West region) using a modified systematic random sampling technique to ensure even representation. The collected data was then coded and analyzed. Farmers in both regions cultivate an area typically not more than 1 hectare (ha), but in Adamawa, farmers grow only one round of potatoes, while in the West they cultivate two in a year (i.e., during the rainy season from March to October). Top challenges to farmers in our survey area include low access to quality seed, poor management and agronomic practices, and limited access to credit, fertilizers and pesticides. In Adamawa, just over half of the farmers (51%) reported having received no agricultural training in the past, compared to just 43% in the West region. Thus, interest in and willingness to participate in future trainings are high

Spectral discrimination of bulloak (Allocasuarina luehmannii) and associated woodland for habitat mapping of the endangered bulloak jewel butterfly (Hypochrysops piceata) in southern Queensland

The bulloak jewel butterfly (Hypochrysops piceata) is an endangered species due to a highly restricted distribution and complex life history, yet little is known of the availability of suitable habitat for future conservation. The aim of this study was to examine the potential of hyperspectral reflectance data for the discrimination of woodland species in support of bulloak jewel butterfly’s habitat mapping. Sites from known butterfly sightings in Leyburn, Southern Queensland, Australia, were examined using hyperspectral scanning and vegetation species discrimination. Reflectance data of eight woodland vegetation species (Allocasuarina luehmannii, Eucalyptus crebra, Eucalyptus populnea, Callitris glauca, Corymbia maculata, Angophora leicarpa, Acacia sparsiflora, and Jacksonia scoparia) were collected at the leaf and canopy levels using a full-range (350 to 2500 nm) hand-held nonimaging spectroradiometer. Partial least square (PLS) regression was used to interpret the bulloak tree spectra against other vegetation species. The PLS results indicated high-prediction accuracies ranging from 78% to 95% and 52% to 5% for canopy and leaf levels, respectively. The highest spectral separability was observed at the near-infrared bands (approximately at 700 to 1355 nm), followed by selected ranges in the short-wave infrared band where separability peaked at 1670 and 2210 nm. The results confirmed the feasible use of hyperspectral sensing for discriminating vegetation species and its potential use for habitat mapping of the endangered bulloak jewel butterfly

Monitoring rice growth status in the Mekong Delta, Vietnam using multitemporal Sentinel-1 data

Rice is one of the world’s most dominant staple foods, and hence rice farming plays a vital role in a nation’s economy and food security. To examine the applicability of synthetic aperture radar (SAR) data for large areas, we propose an approach to determine rice age, date of planting (dop), and date of harvest (doh) using a time series of Sentinel-1 C-band in the entire Mekong Delta, Vietnam. The effect of the incidence angle of Sentinel-1 data on the backscatter pattern of paddy fields was reduced using the incidence angle normalization approach with an empirical model developed in this study. The time series was processed further to reduce noise with fast Fourier transform and smoothing filter. To evaluate and improve the accuracy of SAR data processing results, the classification outcomes were verified with field survey data through statistical metrics. The findings indicate that the Sentinel-1 images are particularly appropriate for rice age monitoring with R2  =  0.92 and root-mean-square error (RMSE) = 7.3 days (n  =  241) in comparison to in situ data. The proposed algorithm for estimating dop and doh also shows promising results with R2  =  0.92 and RMSE  =  6.2 days (n  =  153) and R2  =  0.70 and RMSE  =  5.7 days (n  =  88), respectively. The results have indicated the ability of using Sentinel-1 data to extract growth parameters involving rice age, planting and harvest dates. Information about rice age corresponding to the growth stages of rice fields is important for agricultural management and support the procurement and management of agricultural markets, limiting the negative effects on food security. The results showed that multitemporal Sentinel-1 data can be used to monitor the status of rice growth. Such monitoring system can assist many countries, especially in Asia, for managing agricultural land to ensure productivity

Student Performance Predictions for Advanced Engineering Mathematics Course With New Multivariate Copula Models

Engineering Mathematics requires that problem-solving should be implemented through ongoing assessments; hence the prediction of student performance using continuous assessments remains an important task for engineering educators, mainly to monitor and improve their teaching practice. This paper develops probabilistic models to predict weighted scores ( WS , or the overall mark leading to a final grade) for face-to-face (on-campus) and web-based (online) Advanced Engineering Mathematics students at an Australian regional university over a 6-year period (2013–2018). We fitted parametric and non-parametric D-vine copula models utilizing multiple quizzes, assignments and examination score results to construct and validate the predicted WS in independently test datasets. The results are interpreted in terms of the probability of whether a student’s continuous performance (i.e., individually or jointly with other counterpart assessments) is likely to lead to a passing grade conditional upon joint performance in students’ quizzes and assignment scores. The results indicate that the newly developed D-vine model, benchmarked against a linear regression model, can generate accurate grade predictions, and particularly handle the problem of low or high scores (tail dependence) compared with a conventional model for both face-to-face, and web-based students. Accordingly, the findings advocate the practical utility of joint copula models that capture the dependence structure in engineering mathematics students’ marks achieved. This therefore, provide insights through learning analytic methods to support an engineering educator’s teaching decisions. The implications are on better supporting engineering mathematics students’ success and retention, developing evidence-based strategies consistent with engineering graduate requirements through improved teaching and learning, and identifying/addressing the risk of failure through early intervention. The proposed methods can guide an engineering educator’s practice by investigating joint influences of engineering problem-solving assessments on their student’s grades