The Land Monitor Project

The Land Monitor Project is providing information over the southwest agricultural region of WA. It is assembling and processing sequences of Landsat TM data, a new highresolution digital elevation model (DEM) and other spatial data to provide monitoring information on the area of salt-affected land, and on changes in the area and status of perennial vegetation over the period 1988-2000. Land Monitor is a multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust. The Project will also providing estimates of areas at risk from secondary or future salinisation, based on the historical salinity maps and a set of landform variables derived from the high resolution DEM. Sequences of calibrated Landsat Thematic Mapper satellite images integrated with landform information derived from height data, ground truthing and other existing mapped data are used as the basis for monitoring changes in salinity and woody vegetation. Procedures for accurate registration and calibration were developed by CSIRO Mathematical and Information Sciences (CMIS), as were the data integration procedures for salinity mapping and prediction. For the DEM, heights are derived on a 10m grid from stereo aerial photography flown at 1:40,000 scale, using soft-copy automatic terrain extraction (image correlation) techniques. Land Monitor products include: high resolution DEMs; calibrated sequences of Landsat imgery; present and historical salinity maps; predicted salinity maps; maps of change in vegetation status and spectral/temporal statistics. These products are available in a range of formats and scales, from paddock to catchment and shire scales to suit customer needs

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

A multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust

Land Monitor is a multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust. It will provide land managers and administrators with baseline salinity and vegetation data for monitoring changes over time, and land height data from which contours accurate to two metre intervals can be produced. The Project will also provide estimates of areas at risk from secondary or future salinisation. Land Monitor will cover the 18 million hectares of agricultural area of south-west, Western Australia. Sequences of calibrated Landsat Thematic Mapper satellite images integrated with landform information derived from height data, ground truthing and other existing mapped data sets are used as the basis for monitoring changes in salinity and woody vegetation. Heights are derived on a 10m grid from stereo aerial photography flown at 1:40,000 scale, using soft-copy automatic terrain extraction (image correlation) techniques. Proposed Land Monitor products include salinity maps, predicted salinity maps, enhanced imagery, vegetation status maps and spectral / temporal statistics. These products will be available in a range of formats and scales, from paddock, farm to catchment and shire scales to suit customer needs

Mapping the broad habitats of the Burren using satellite imagery

Teagasc acknowledges the support of the Research Stimulus Fund of the Department of Agriculture and Food, funded by the Irish Government under the National Development Plan 2000 – 2006.End of project reportThis project has successfully used satellite imagery to survey and map the extent and spatial distribution of broad habitat types within the Burren, and we have represented this information on a digitised habitat map. this information on a digitised habitat map. This map is the first to show the distribution of the broad habitats of the Burren and will be an important tool in aiding future decisions as to how the habitats of the Burren should be managed to the benefit of both the farmer and the environment. The map provides the first estimate of the area of the Burren affected by scrub encroachment – this being one of the most significant threats to the EU priority habitats in the region. On a particularly challenging area with a high diversity and complexity of habitats, remote sensing appears to offer a very effective and cost-efficient alternative to broad-scale habitat mapping on a field-by-field basis. The use of high-resolution imagery and ground-truthing should be adopted to complete a detailed national survey of habitats and land use in Ireland. This would support more effective implementation of both the Agriculture sector’s obligations under the Habitats Directive, and agri-environmental schemes with wildlife objectives. The outputs provided by such mapping approaches could inform the targeting of agri-environmental objectives, and increase the efficiency of detecting areas of high conservation value for monitoring by more conventional methods. The detailed land use descriptions offered by such imagery are also of high relevance to modelling approaches and risk assessment for implementation of land use policies such as the Water Framework Directive and Nitrates Directive.Department of Agriculture, Food and the Marin

TaLAM: Mapping Land Cover in Lowlands and Uplands with Satellite Imagery

End-of-Project ReportThe Towards Land Cover Accounting and Monitoring (TaLAM) project is part of Ireland’s response to creating a national land cover mapping programme. Its aims are to demonstrate how the new digital map of Ireland, Prime2, from Ordnance Survey Ireland (OSI), can be combined with satellite imagery to produce land cover maps

A Methodology for Natural Resources Analysis Appropriate for County Level Planning

In this thesis a methodology for developing an integrated cumulative analysis of sensitive natural resources was developed. Themes of natural resources-waterways, wetlands, forested lands, prime agricultural soils, and steep slopes-were brought together in a GIS system, in a grid format, in a manner so that each cell of the grid accumulated value according to the increasing presence of resource themes. For example, an area (30 meter x 30 meter grid cell) containing only one of the above themes is given a value of l, whereas an area containing slopes, streams, and forests might, after weighting factors, have a value of 5. The result is a map that demonstrates the cumulative value of sensitivity of a given area and its relative relation to the landscape under analysis. The methodology uses off-the-shelf GIS software and available GIS data sources, and is designed to require a minimum of technical and financial resources. This methodology is particularly useful for counties in Tennessee in meeting the requirements of Public Chapter 1101, the Growth Policy Act. The case study for this thesis reveals that much development does, in fact, occur in sensitive natural areas and that, therefore, this tool could be well utilized by planners to inform the public and to assist in the development of policy aimed toward the protection of sensitive areas from activities that would reduce their capacity to serve their natural functions

Assessing Interactions between Estuary Water Quality and Terrestrial Land Cover in Hurricane Events with Multi-sensor Remote Sensing

Estuaries are environmentally, ecologically and environmentally important places as they act as a meeting place for land, freshwater and marine ecosystems. They are also called nurseries of the sea as they often provide nesting and feeding habitats for many aquatic plants and animals. These estuaries also withstand the worst of some natural disasters, especially hurricanes. The estuaries as well as the harbored ecosystems undergo significant changes in terms of water quality, vegetation cover etc. and these components are interrelated. When hurricane makes landfall it is necessary to assess the damages as quickly as possible as restoration and recovery processes are time-sensitive. However, assessment of physical damages through inspection and survey and assessment of chemical and nutrient component changes by laboratory testing are time-consuming processes. This is where remote sensing comes into play. With the help of remote sensing images and regression analysis, it is possible to reconstruct water quality maps of the estuary affected. The damage sustained by the vegetation cover of the adjacent coastal watershed can be assessed using Normalized Difference Vegetation Index (NDVI) The water quality maps together with NDVI maps help observe a dynamic sea-land interaction due to hurricane landfall. The observation of hurricane impacts on a coastal watershed can be further enhanced by use of tasseled cap transformation (TCT). TCT plots provide information on a host of land cover conditions with respect to soil moisture, canopy and vegetation cover. The before and after TCT plots help assess the damage sustained in a hurricane event and also see the progress of recovery. Finally, the use of synthetic images obtained by use of data fusion will help close the gap of low temporal resolution of Landsat satellite and this will create a more robust monitoring system

Remote sensing and GIS in support of sustainable agricultural development

Over the coming decades it is expected that the vast amounts of area currently in agricultural production will face growing pressure to intensify as world populations continue to grow, and the demand for a more Western-based diet increases. Coupled with the potential consequences of climate change, and the increasing costs involved with current energy-intensive agricultural production methods, meeting goals of environmental and socioeconomic sustainability will become ever more challenging. At a minimum, meeting such goals will require a greater understanding of rates of change, both over time and space, to properly assess how present demand may affect the needs of future generations. As agriculture represents a fundamental component of modern society, and the most ubiquitous form of human induced landscape change on the planet, it follows that mapping and tracking changes in such environments represents a crucial first step towards meeting the goal of sustainability. In anticipation of the mounting need for consistent and timely information related to agricultural development, this thesis proposes several advances in the field of geomatics, with specific contributions in the areas of remote sensing and spatial analysis: First, the relative strengths of several supervised machine learning algorithms used to classify remotely sensed imagery were assessed using two image analysis approaches: pixel-based and object-based. Second, a feature selection process, based on a Random Forest classifier, was applied to a large data set to reduce the overall number of object-based predictor variables used by a classification model without sacrificing overall classification accuracy. Third, a hybrid object-based change detection method was introduced with the ability to handle disparate image sources, generate per-class change thresholds, and minimize map updating errors. Fourth, a spatial disaggregation procedure was performed on coarse scale agricultural census data to render an indicator of agricultural development in a spatially explicit manner across a 9,000 km2 watershed in southwest Saskatchewan for three time periods spanning several decades. The combination of methodologies introduced represents an overall analytical framework suitable for supporting the sustainable development of agricultural environments