Community based forest management systems in developing countries and eligibility for clean development mechanism

Concerns have been raised among the scientific communities about the increased atmospheric concentration of carbon dioxide (CO2). Carbon sequestration rates can be maintained or increased by afforestation, reforestation, avoided deforestation, forest preservation and particular tending and cultural operations on existing forests. Of these, afforestation and reforestation are the only eligible project activities under the Clean Development Mechanism (CDM). Of the three market-based mechanisms of the Kyoto Protocol (KP), CDM is the only one designed for developing countries where, coincidently, community based forest management systems (CBFMS) are becoming the main form of forest management. Under these systems, enhanced natural regeneration, forest preservation and wise utilization through different sets of cultural and tending operations are widely practiced in Africa, Asia and Latin America. These systems are often more compatible with the essence of the Convention on Biological Diversity (CBD) than are alternative management systems. Using Nepal as a case study, this paper highlights the importance of community forests in developing countries and then explains why many of them may not be eligible for CDM project activities. After that, some reasons why enhanced natural regeneration and forest preservation activities should be considered under the CDM project activities will be discussed. If community forests contribute to achieving the main objectives of CDM program as well as providing biodiversity benefits, and if they are the only socially acceptable and economically viable option, then they should be eligible under the CDM project activities. In particular, the CDM forest definition (>10% crown cover) should not be a barrier to their eligibility

Analysis of spacing for spotted gum plantations for maximising merchantable logs' volume in South East Queensland, Australia

Spotted gum (Corymbia citriodora subspecies Variegata) has the potential to be the major hardwood species for large-scale plantations in South East Queensland, Australia, but production research is limited due to the lack of age of research plots. Optimal spacing is a major subject of concern. Based on time series data from a spotted gum experiment site, growth performance is analysed for five spacing levels: ─11.3 m x 11.3 m (78 stems per hectare), 7.4 m x 7.4 m (182 sph), 5.4 m x 5.4 m (343 sph), 3.6 m x 3.6 m (771 sph) and 2.9 m x 2.9 m (1189 sph). The major objective was assumed to be to maximise total merchantable log volume. A growth model was produced, and the mean diameter at breast height (dbh) and total merchantable log volume for each spacing levels at a range of harvesting ages was estimated. From the analysis, the spacing level of 5.4 m x 5.4 m was found to be optimal for maximising merchantable log volume to 10 cm small-end diameter. Further analysis of mean dbh, height and volume of the largest 200 and 250 trees from this spacing level indicates that merchantable log volume could be maximised by retaining the 250 largest trees per hectare. The total financial revenue from the best spacing level in 25 and 30 years are predicted to be $13,637 and$17,779 per hectare, respectively. If full rotation data could be obtained, more reliable models could be produced, and a more accurate financial estimate could be made

Spatial modelling of adaptation strategies for urban built infrastructures exposed to flood hazards

The recent 2010/2011 floods in the central and southern Queensland (Australia) prompted this research to investigate the application of geographical information system (GIS) and remote sensing in modelling the current flood risk, adaptation/coping capacity, and adaptation strategies. Identified Brisbane City as the study area, the study aimed to develop a new approach of formulating adaptation/coping strategies that will aid in addressing flood risk management issues of an urban area with intensive residential and commercial uses. Fuzzy logic was the spatial analytical tool used in the integration of flood risk components (hazard, vulnerability, and exposure) and in the generation of flood risk and adaptation capacity indices. The research shows that 875 ha, 566 ha, and 828 ha were described as areas with relatively low, relatively moderate, and relatively high risk to flooding, respectively. Identified adaptation strategies for areas classified as having relatively low (RL), relatively moderate (RM), relatively high (RH), and likely very high (LVH) adaptation/coping capacity were mitigation to recovery phases, mitigation to response phases, mitigation to preparedness phases, and mitigation phase, respectively. Integrating the results from the flood risk assessment, quantitative description of adaptation capacity, and identification of adaptation strategies, a new analytical technique identified as flood risk-adaptation capacity index-adaptation strategies (FRACIAS) linkage model was developed for this study

Mapping and analysis of changes in the riparian landscape structure of the Lockyer Valley Catchment, Queensland, Australia

[Abstract]: A case study of the Lockyer Valley catchment in Queensland, Australia, was conducted to develop appropriate mapping and assessment techniques to quantify the nature and magnitude of riparian landscape structural changes within a catchment. The study employed digital image processing techniques to produce land cover maps from the 1973 and 1997 Landsat imagery. Fixed and variable width buffering of streams were implemented using a geographic information system (GIS) to estimate the riparian zone and to subsequently calculate the landscape patterns using the Patch Analyst (Grid) program (a FRAGSTATS interface). The nature of vegetation clearing was characterised based on land tenure, slope and stream order. Using the Pearson chi-square test and Cramer’s V statistic, the relationships between the vegetation clearing and land tenure were further assessed. The results show the significant decrease in woody vegetation areas mainly due to conversion to pasture. Riparian vegetation corridors have become more fragmented, isolated and of much smaller patches. Land tenure was found to be significantly associated with the vegetation clearing, although the strength of association was weak. The large proportion of deforested riparian zones within steep slopes or first-order streams raises serious questions about the catchment health and the longer term potential for land degradation by upland clearing. This study highlights the use of satellite imagery and geographic information systems in mapping and analysis of landscape structural change, as well as the identification of key issues related to sensor spatial resolution, stream buffering widths, and the quantification of land transformation processes

Assessing spatial information access, use and sharing for catchment management in Australia

Spatial data plays an important role in many social, environmental, economic and political decisions and is increasingly acknowledged as a national resource essential for sustainable development. One of the potential areas where spatial data can make a positive impact is for improved decision making to support catchment management. Reliable spatial data infrastructure (SDI) is needed to record the environmental, social and economic dimensions of catchment management. By building an appropriate SDI, disparate spatial data can be accessed and utilised to facilitate the exchange and sharing of spatial data between stakeholders across catchment communities. The aim of this paper is to identify the factors/variables contributing to spatial information access, sharing and use across catchment management areas and evaluate the current status of spatial information access, sharing and use among Australian states from a catchment management authority perspective. 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 among Australian states. The key factors which influence sharing and access to spatial information are also explored. We found there is significant for spatial information access, use and sharing to contribute to SDI development

Distance, multimedia and web delivery in surveying and GIS courses at the University Of Southern Queensland

[Abstract]: The University of Southern Queensland has been involved with the distance education of surveying courses for over 25 years. In recent times, staff of the Surveying and Land Information Discipline, and the University as a whole, have embarked on multimedia enhancement and web delivery of curricula. This paper examines some of the initiatives undertaken to enhance the delivery of educational materials and discusses some of the issues involved in the effective delivery of distance education materials. The significant experience in the delivery of traditional educational materials has proven to be an advantage in the repackaging and enhancement of teaching materials. Delivery of education to off-campus students requires a significant support infrastructure which is often not recognised by new entrants into the flexible delivery arena. Traditional support mechanisms such as phone, fax and standard media (eg. videos, audio tapes etc) are being replaced by email, ‘electronic’ discussion groups, CDs and internet resources. These enhancements, when developed professionally, require a significant commitment of resources and expertise and often require a team approach to their design and development. Access by off-campus students to internet services and affordable software packages also require careful consideration in the design and offering of distance education materials

Spatial prioritisation of revegetation sites for dryland salinity management: an analytical framework using GIS

[Abstract]: To address the lack of analytical and modelling techniques in prioritising revegetation sites for dryland salinity management, a case study of the Hodgson Creek catchment in Queensland, Australia, was conducted. An analytical framework was developed, incorporating the use of spatial datasets (Landsat 7 image, DEM, soil map, and salinity map) which were processed using image processing techniques and a geographic information system (GIS). Revegetation sites were mapped and their priority determined based on recharge area, land use/cover and sub-catchment salinity. The analytical framework presented here enhances the systematic use of land information, widens the scope for scenario testing, and improves the testing of alternative revegetation options. The spatial patterns of revegetation sites could provide an additional set of information relevant in the design of revegetation strategies

Crop maturity mapping using a low-cost low-altitude remote sensing system

The objective of this study was to assess the ability of the 'low-cost low-altitude (LCLA) remote sensing system' to map the maturity of a barley crop. Monitoring maturity is important from a frost/pest/disease susceptibility perspective. It also allows harvest to be planned, and in this case, screens varieties for adaptation to potentially tough seasons. The study area, a barley variety trial, was at 'Lundavra' near Goondiwindi in Southern Queensland (-28.056º, 150.087º). The LCLA remote sensing system consisted of digital cameras which, along with controlling electronics, were positioned in an unmanned aerial vehicle (UAV). The range of growth stages present varied from Zadok 43–59. Areas-of-interest were randomly selected from the variety plots, and a statistical package utilised to perform discriminant function analysis of the spectral values. The classification results (when predicting the original 14 classes) indicated that the predictive power was weak, with 23% correctly classified. As each class represents an individual growth stage of the crop, a difference of one in the Zadok scale can mean as little as an extra leaf unfolded on the plant. The accuracy was further improved by broadening the groupings to six secondary growth stages, three principal growth stages, and finally refining the classification to the two primary growth stages i.e. booting (Z40–49) and emergence (Z50–59). This resulted in a classification accuracy of 83.5%. The classification results achieved with the LCLA remote sensing system was quite acceptable, especially considering that the image was taken over a month after the growth stages were recorded

Mapping olive varieties and within-field spatial variability using high resolution QuickBird imagery

[Abstract]: The growth of the Australian olive (Olea europaea L.) industry requires support from research to ensure its profitability and sustainability. To contribute to this goal, our project tested the ability of remote sensing imagery to map olive groves and their attributes. Specifically, this study aimed to: (a) discriminate olives varieties; and to (b) detect and interpret within-field spatial variability. Using high spatial resolution (2.8m) QuickBird multispectral imagery acquired over Yallamundi (southeast Queensland) on 24 December 2003, both visual interpretation and statistical (divergence) measures were employed to discriminate olive varieties. Similarly, the detection and interpretation of within-field spatial variability was conducted on enhanced false colour composite imagery, and confirmed by the use of statistical methods. Results showed that the two olive varieties (i.e. Kalamata and Frantoio) can be visually differentiated and mapped on the enhanced image based on texture. The spectral signature plots showed little difference in the mean spectral reflectance values, indicating that the two varieties have a very low spectral separability. In terms of within-field spatial variability, the presence or absence of Rhodes grass (Chloris gayana) was detected using visual interpretation, corroborated by the results of quantitative statistical measures. Spatial variability in soil properties, caused by the presence of a patch of sandy soil, was also detected visually. Finally, the “imprint” of former cover-type or land-use prior to olive plantation establishment in 1998 was identified. More work is being done to develop image classification techniques for mapping within-field spatial variability in olive varieties, biomass and condition using hyperspectral image data, as well as interpreting the cause of observed variability

Mapping of peanut crops in Queensland, Australia using time-series PROBA-V 100-m normalized difference vegetation index imagery

Mapping of peanut crops is essential in supporting peanut production, yield prediction, and commodity forecasting. While ground-based surveys can be used over small areas, the development of remote-sensing technologies could provide rapid and inexpensive crop area estimates with high accuracy over large regions. Some of these recent earth observation satellite systems, such as the Project for On-Board Autonomy Vegetation (PROBA-V), have the advantage of increased spatial and temporal resolution. With a study area located in the South Burnett region, Queensland, Australia, the primary aim of this study was to assess the ability of time-series PROBA-V 100-m normalized difference vegetation index (NDVI) for peanut crop mapping. Two datasets, i.e., PROBA-V NDVI time-series imagery and the corresponding phenological parameters generated from TIMESAT data analysis technique, were classified using maximum likelihood classification, spectral angle mapper, and minimum distance classification algorithms. The results show that among all methods used, the application of MLC in PROBA-V NDVI time series produced very good overall accuracy, i.e., 92.75%, with producer and user accuracy of each class ≥78.79  %  . For all algorithms tested, the mapping of peanut cropping areas produced satisfactory classification results, i.e., 75.95% to 100%. Our study confirmed that the use of finer resolution 100 m of PROBA-V imagery (i.e., relative to MODIS 250-m data) has contributed to the success of mapping peanut and other crops in the study area