A hierarchical approach coupled with coarse DEM information for improving the efficiency and accuracy of forest mapping over very rugged terrains

Forest mapping over mountainous terrains is difficult because of high relief Although digital elevation models (DEMs) are often useful to improve mapping accuracy, high quality DEMs are seldom available over large areas, especially in developing countries

Caracterização do uso do solo e das áreas de preservação permanente, visando a adequação ambiental

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Status, trends and drivers of kelp forests in Europe: an expert assessment

A comprehensive expert consultation was conducted in order to assess the status, trends and the most important drivers of change in the abundance and geographical distribution of kelp forests in European waters. This consultation included an on-line questionnaire, results from a workshop and data provided by a selected group of experts working on kelp forest mapping and eco-evolutionary research. Differences in status and trends according to geographical areas, species identity and small-scale variations within the same habitat where shown by assembling and mapping kelp distribution and trend data. Significant data gaps for some geographical regions, like the Mediterranean and the southern Iberian Peninsula, were also identified. The data used for this study confirmed a general trend with decreasing abundance of some native kelp species at their southern distributional range limits and increasing abundance in other parts of their distribution (Saccharina latissima and Saccorhiza polyschides). The expansion of the introduced species Undaria pinnatifida was also registered. Drivers of observed changes in kelp forests distribution and abundance were assessed using experts’ opinions. Multiple possible drivers were identified, including global warming, sea urchin grazing, harvesting, pollutionand fishing pressure, and their impact varied between geographical areas. Overall, the results highlight major threats for these ecosystems but also opportunities for conservation. Major requirements to ensure adequate protection of coastal kelp ecosystems along European coastlines are discussed, based on the local to regional gaps detected in the study.

Status, trends and drivers of kelp forests in Europe: an expert assessment

A comprehensive expert consultation was conducted in order to assess the status, trends and the most important drivers of change in the abundance and geographical distribution of kelp forests in European waters. This consultation included an on-line questionnaire, results from a workshop and data provided by a selected group of experts working on kelp forest mapping and eco-evolutionary research. Differences in status and trends according to geographical areas, species identity and small-scale variations within the same habitat where shown by assembling and mapping kelp distribution and trend data. Significant data gaps for some geographical regions, like the Mediterranean and the southern Iberian Peninsula, were also identified. The data used for this study confirmed a general trend with decreasing abundance of some native kelp species at their southern distributional range limits and increasing abundance in other parts of their distribution (Saccharina latissima and Saccorhiza polyschides). The expansion of the introduced species Undaria pinnatifida was also registered. Drivers of observed changes in kelp forests distribution and abundance were assessed using experts’ opinions. Multiple possible drivers were identified, including global warming, sea urchin grazing, harvesting, pollutionand fishing pressure, and their impact varied between geographical areas. Overall, the results highlight major threats for these ecosystems but also opportunities for conservation. Major requirements to ensure adequate protection of coastal kelp ecosystems along European coastlines are discussed, based on the local to regional gaps detected in the study.

Vegetation mapping in an area of Ombrophilous Dense Forest at Parque Estadual da Serra do Mar, São Paulo State, Brazil, and floristic composition of the tree component of some physiognomies*

This study aimed to map phytophysiognomies of an area of Ombrophilous Dense Forest at Parque Estadual da Serra do Mar and characterize their floristic composition. Photointerpretation of aerial photographs in scale of 1:35,000 was realized in association with field work. Thirteen physiognomies were mapped and they were classified as Montane Ombrophilous Dense Forest, Alluvial Ombrophilous Dense Forest or Secondary System. Three physiognomies identified at Casa de Pedra streamlet's basin were studied with more details. Riparian forest (RF), valley forest (VF), and hill forest (HF) presented some floristic distinction, as confirmed by Detrended Correspondence Analysis (DCA) and Indicator Species Analysis (ISA) conducted here. Anthropic or natural disturbances and heterogeneity of environmental conditions may be the causes of physiognomic variation in the vegetation of the region. The results presented here may be useful to decisions related to management and conservation of Núcleo Santa Virgínia forests, in general.

Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management.

Clasificación de una imagen de alta resolución "Quickbird" con la técnica de análisis de imágenes en base a objetos

Estimation and mapping of foliar chemical levels in a coastal eucalypt forest using CASI data

Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference

Mapping and Assessing Fire Damage on Broadleaved Forest Communities in Big Cypress National Preserve

Within Big Cypress National Preserve (BICY), oak-dominated forests and woodlands as well as tropical and temperate hardwood hammocks are integral components of the landscape and are biodiversity hotpots for both flora and fauna. These broadleaved forest communities serve as refugia for many of the Preserve’s wildlife species during prolonged flooding and fires. However, both prolonged flooding and severe fires, which are important and necessary disturbance vectors within this landscape, can have deleterious effects on these forested communities. This is particularly true in the case of fires, which under extreme conditions associated with drought and elevated fuel loads, can burn through these forested communities consuming litter and understory vegetation and top killing most, if not all, of the trees present.

Burned Area Mapping in the North American Boreal Forest Using Terra-MODIS LTDR (2001–2011): A Comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 Products

An algorithm based on a Bayesian network classifier was adapted to produce 10-day burned area (BA) maps from the Long Term Data Record Version 3 (LTDR) at a spatial resolution of 0.05° (~5 km) for the North American boreal region from 2001 to 2011. The modified algorithm used the Brightness Temperature channel from the Moderate Resolution Imaging Spectroradiometer (MODIS) band 31 T31 (11.03 μm) instead of the Advanced Very High Resolution Radiometer (AVHRR) band T3 (3.75 μm). The accuracy of the BA-LTDR, the Collection 5.1 MODIS Burned Area (MCD45A1), the MODIS Collection 5.1 Direct Broadcast Monthly Burned Area (MCD64A1) and the Burned Area GEOLAND-2 (BA GEOLAND-2) products was assessed using reference data from the Alaska Fire Service (AFS) and the Canadian Forest Service National Fire Database (CFSNFD). The linear regression analysis of the burned area percentages of the MCD64A1 product using 40 km × 40 km grids versus the reference data for the years from 2001 to 2011 showed an agreement of R2 = 0.84 and a slope = 0.76, while the BA-LTDR showed an agreement of R2 = 0.75 and a slope = 0.69. These results represent an improvement over the MCD45A1 product, which showed an agreement of R2 = 0.67 and a slope = 0.42. The MCD64A1, BA-LTDR and MCD45A1 products underestimated the total burned area in the study region, whereas the BA GEOLAND-2 product overestimated it by approximately five-fold, with an agreement of R2 = 0.05. Despite MCD64A1 showing the best overall results, the BA-LTDR product proved to be an alternative for mapping burned areas in the North American boreal forest region compared with the other global BA products, even those with higher spatial/spectral resolution

Mapping of shallow coastal groundwaters, their hydrology and environmental geochemistry : Pumicestone catchment, Southeast Queensland

Blooms of the toxic cyanobacterium majuscula Lyngbya in the coastal waters of southeast Queensland have caused adverse impacts on both environmental health and human health, and on local economies such as fishing and tourism. A number of studies have confirmed that the main limiting nutrients (“nutrients of concern”) that contribute to these blooms area Fe, DOC, N, P and also pH. This study is conducted to establish the distribution of these parameters in a typical southeast Queensland coastal setting. The study maps the geochemistry of shallow groundwater in the mainland Pumicestone catchment with an emphasis on the nutrients of concern to understand how these nutrients relate to aquifer materials, landuse and anthropogenic activities. The results of the study form a GIS information layer which will be incorporated into a larger GIS model being produced by Queensland Department of Environment and Resource Management (DERM) to support landuse management to avoid/minimize blooms of Lyngbya in Moreton Bay, southeast Queensland, and other similar settings. A total of 38 boreholes were established in the mainland Pumicestone region and four sampling rounds of groundwater carried out in both dry and wet conditions. These groundwater samples were measured in the field for physico-chemical parameters, and in the laboratory analyses for the nutrients of concern, and other major and minor ions. Aquifer materials were confirmed using the Geological Survey of Queensland digital geology map, and geomaterials were assigned to seven categories which are A (sands), B (silts, sandy silts), C (estuarine mud, silts), D (humid soils), E (alluvium), F (sandstone) and G (other bedrock). The results of the water chemistry were examined by use of the software package AquaChem/AqQA, and divided into six groundwater groups, based on groundwater chemical types and location of boreholes. The type of aquifer material and location, and proximity to waterways was found to be important because they affected physico-chemical properties and concentrations of nutrients of concern and dissolved ions. The analytical results showed that iron concentrations of shallow groundwaters were high due to acid sulfate soils, and also mud and silt, but were lower in sand materials. DOC concentrations of these shallow groundwaters in the sand material were high probably due to rapid infiltration. In addition, DOC concentrations in some boreholes were high because they were installed in organic rich wetlands. The pH values of boreholes were from acidic to near neutral; some boreholes with pH values were low (< 4), showing acid sulfate soils in these boreholes. Concentrations of total nitrogen and total phosphorus of groundwaters were generally low, and the main causes of elevated concentrations of total nitrogen and total phosphorus are largely due to animal and human wastes and tend to be found in localized source areas. Comparison of the relative percentage of nitrogen species (NH3/NH4< Org-N, NO3-N and NO2-N) demonstrated that they could be related to sources such as animal waste, residential and agricultural fertilizers, forest and vegetation, mixed residents and farms, and variable setting and vegetation covers. Total concentrations of dissolved ions in sampling round 3 (dry period) were higher than those in sampling round 2 (wet period) due to both evaporation of groundwater in the dry period and the dilution of rainfall in the wet period. This showed that the highest concentrations of nutrients of concern were due to acid sulfate soils, aquifer materials, landuse and anthropogenic activities and were typically in aquifer materials of E (alluvium) and C (estuarine muds) and locations of Burpengary, Caboolture, and Glass Mountain catchments.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.

Constrained Linear Spectral Unmixing Technique for Regional Land Cover Mapping Using MODIS Data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up-to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat-TM/ETM+, IRS-ICID LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (similar to 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 in), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end-members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications. Index Terms-Remote sensing, digital

Spatial model of site index based on Y-ray spectrometry and a digital elevation model for two Pinus species in Tuan Toolara State Forest, Queensland, Australia

Site index prediction models are an important aid for forest management and planning activities. This paper introduces a multiple regression model for spatially mapping and comparing site indices for two Pinus species (Pinus elliottii Engelm. and Queensland hybrid, a P. elliottii x Pinus caribaea Morelet hybrid) based on independent variables derived from two major sources: g-ray spectrometry (potassium (K), thorium (Th), and uranium (U)) and a digital elevation model (elevation, slope, curvature, hillshade, flow accumulation, and distance to streams). In addition, interpolated rainfall was tested. Species were coded as a dichotomous dummy variable; interaction effects between species and the g-ray spectrometric and geomorphologic variables were considered. The model explained up to 60% of the variance of site index and the standard error of estimate was 1.9 m. Uranium, elevation, distance to streams, thorium, and flow accumulation significantly correlate to the spatial variation of the site index of both species, and hillshade, curvature, elevation and slope accounted for the extra variability of one species over the other. The predicted site indices varied between 20.0 and 27.3 m for P. elliottii, and between 23.1 and 33.1 m for Queensland hybrid; the advantage of Queensland hybrid over P. elliottii ranged from 1.8 to 6.8 m, with the mean at 4.0 m. This compartment-based prediction and comparison study provides not only an overview of forest productivity of the whole plantation area studied but also a management tool at compartment scale.