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

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

An evaluation of satellite remote sensing for crop area estimation in the west bank, Palestine

This thesis investigates the use of field and satellite data for crop area estimation in the northern part of the West Bank, Palestine. The satellite data were obtained by the SPOT HRV on 19 May 1994. The satellite data were geometrically corrected to the Palestine Grid using 1: 50,000 Israeli topographic maps. The study investigated the ability of SPOT HRV data to produce accurate crop area estimation of the northern part of the West Bank that is characterised with small field sizes and complex physical environment. A land cover classification scheme appropriate to the study area was designed. Twenty-three land cover classes were produced from the SPOT HRV classification. Land cover classes were developed to produce thematic land use classes. The classification accuracy obtained from SPOT HRV image classification was 81%. Classification results were assessed by using the known land use information obtained from the field during the training stage and the field sampling survey. The study area was divided into five strata and the field survey was conducted by applying a stratified random sampling methodology. Seventy three 1 km(^2) sample units were randomly chosen and surveyed by the author using maps, aerial photographs, satellite photographs, a questionnaire, camera photographs, and sketches. The field area measurements were taken and the final hectarage estimates were obtained for each crop type. The SPOT HRV and the field data were combined in regression analysis using a double sampling method and a hectarage estimate was produced for each crop in the study area. The results obtained showed that the regression estimator was more efficient than the field estimator and a gain in precision was achieved. The results were analysed on stratum and crop type basis. Remote sensing and thematic agricultural perspectives were used in the analysis. Results of the study suggest that it is possible to improve image classification accuracy by using better spatial and spectral resolution imagery and the integration of remote sensing data with agricultural data using the Geographical Information Systems (GIS)

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Land Cover Mapping and Change Analysis at the Tensleep Preserve in Wyoming

Mapping land cover and land cover change are important, especially for land managers who protect natural lands and generate restoration projects. Accurate land cover assessment of rangelands can be difficult because the spectral difference between plant species may be minimal. The goal of this research is to map the land cover in the Tensleep Preserve and highlight change that has occurred over the past twenty-three years using the Feature Analyst extension. The land cover change map will highlight significant changes and Feature Analyst will accurately identify different land covers using historical aerial photographs and ground truthing data collected in 2013. Owned by the Nature Conservancy, the Tensleep Preserve includes 10,088 acres of mixed ecosystems in the foothills of Wyoming\u27s Big Horn Mountains and has a unique floral and faunal history. Ungulates use the property as a corridor for migration routes and Canyon Creek provides fresh water along a twelve mile stretch. This rangeland is rich in biodiversity because its remarkable topography offers abundant habitats. Understanding the land cover trends that have occurred over time is needed to restore natural habitats and protect endemic plant species. The final analysis will document change over the past two decades and give management a decision making tool for current and future projects

Land use change in the high mountain belts of the central Apennines led to marked changes of the grassland mosaic

Aims High mountain pastures are hotspots of biodiversity, but grazing cessation and climate change are causing tall-grass encroachment and expansion of scrublands and forests. As part of biodiversity conservation efforts, grassland variation needs to be investigated at different spatial scales. We aimed to assess the landscape mosaic variation that occurred between 1988 and 2015 in the higher Mediterranean mountains. We investigated the recovery or land-degradation processes related to land use change, the effects of site condition, the impacts on grassland mosaic heterogeneity, and the threats to biodiversity. Location Sibillini Mountains (central Italy), over 1,650 m a.s.l. Methods We used two-step object-based supervised classification on Landsat 5 and 8 satellite images to analyze changes in landscape patterns and vegetation cover on formerly low-intensity pastures, by assessing the Normalized Difference Vegetation Index variation between 1988 and 2015. Twenty percent of the polygons obtained from segmentation were visually interpreted and assigned to five land cover classes. We generated a land use transition matrix and used Fourier Transforms to detect trends in variation of landscape mosaics and fragmentation. Results We observed prominent dynamics of the grassland mosaic leading to the homogenization of its structure through decreasing patch heterogeneity, especially on south-facing slopes. Grasslands shifted from open communities to dense pastures, with a reduction of scree and spread of tall grasses. The former trend could be understood as a recovery process reverting screes to conditions in equilibrium with local landform and climatic features, while the invasion of tall grasses is a land-degradation process that might lead to local species extinction and loss of habitat connectivity. Conclusions Pronounced changes in the large-scale landscape characteristics, mainly due to land use changes, of which scientists and managers of protected areas are not fully aware, are underway in the top mountain sectors of the study area

Change Detection of Land Cover in Yangon Using Remote Sensing and Machine Learning

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Estimating farm dam storage using SPOT imagery

Includes abstract.Includes bibliographical references.The objective of this study is to establish a methodology in which remote sensing can be used to support the monitoring of water resources. SPOT XS imagery and object-oriented classification was used to identify farm dams and their surface area. Two equations applied to determining the capacity of dams were used to convert surface area to volume. The results showed a similarity between fieldwork and object-oriented classification data for surface area. Overall, there appears to be a strong positive correlation between object-oriented classification and unsupervised classification. The correlation between object-oriented classification and supervised classification ranged from strong positive association to little or no association. This study concludes that remote sensing is a useful tool in identifying water bodies and generating an estimate of volume stored

Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

The detection of wetlands using remote sensing in Qoqodala, Eastern Cape

Bibliography: leaves 66-68.This dissertation aims to establish the possibilities of mapping wetlands in Qoqodala, Eastern Cape Province, South Africa, using Landsat and/or Aster imagery. The methodology for mapping wetlands using Landsat imagery, proposed by Thompson, Marneweck, Bell, Kotze, Muller, Cox and Smith (2002) is adapted and applied to the study area. The same methodology is modified for use with Aster imagery and applied to the study area. In addition, the possibilities of treating Aster as a hyperspectral image are investigated, and a methodology using hyperspectral processing techniques is implemented