Multitemporal analysis of satellite imagery: implementation of a land evolution model in ALCS (ATSR LAND CLASSIFICATION SYSTEM)

In this report it was designed an innovative satellite-based monitoring approach applied on the Iraqi Marshlands to survey the extent and distribution of marshland re-flooding and assess the development of wetland vegetation cover. The study, conducted in collaboration with MEEO Srl , makes use of images collected from the sensor (A)ATSR onboard ESA ENVISAT Satellite to collect data at multi-temporal scales and an analysis was adopted to observe the evolution of marshland re-flooding. The methodology uses a multi-temporal pixel-based approach based on classification maps produced by the classification tool SOIL MAPPER ®. The catalogue of the classification maps is available as web service through the Service Support Environment Portal (SSE, supported by ESA). The inundation of the Iraqi marshlands, which has been continuous since April 2003, is characterized by a high degree of variability, ad-hoc interventions and uncertainty. Given the security constraints and vastness of the Iraqi marshlands, as well as cost-effectiveness considerations, satellite remote sensing was the only viable tool to observe the changes taking place on a continuous basis. The proposed system (ALCS – AATSR LAND CLASSIFICATION SYSTEM) avoids the direct use of the (A)ATSR images and foresees the application of LULCC evolution models directly to „stock‟ of classified maps. This approach is made possible by the availability of a 13 year classified image database, conceived and implemented in the CARD project (http://earth.esa.int/rtd/Projects/#CARD).The approach here presented evolves toward an innovative, efficient and fast method to exploit the potentiality of multi-temporal LULCC analysis of (A)ATSR images. The two main objectives of this work are both linked to a sort of assessment: the first is to assessing the ability of modeling with the web-application ALCS using image-based AATSR classified with SOIL MAPPER ® and the second is to evaluate the magnitude, the character and the extension of wetland rehabilitation.

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

Urban land use change analysis and modelling: a case study of Setubal-Sesimbra, Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

Finland: the land of a thousand lakes a new holiday resort - [Cover]

Helsinki : The tourist society in Finland 1909 : Hull
Tarkemmat teoskuvat osoitteessa http://urn.fi/URN:NBN:fi-fd2011-pp00001592

Geotechnology applied to land use and sealing in urban areas

Through the geotechnology's use, the aim of this study was to characterize the urban occupation interference and occurrence of floods in the upstream area of watershed from the stream Wenzel (Rio Claro-SP/Brazil). Urbanized watersheds are composed of a variety of features and the development of cartographic material allowed the analysis of the evolution of land used for 1958 and 2006 scenarios. The thematic maps were generated using software Spring 4.3.3, wherein it got the separation of matters from vegetation cover and other intra urban features. Procedures of digital processes and classification of surface cover allowed quantifying the occupied area by each coverage type: woody vegetation, grass, grass with bare soil, bare soil, building, asphaltic sheets and exposed soil. Quantification of the different covers' occupied areas allowed relating the parameter Curve Number (Soil Conservation Service) as efficient methodology for runoff values estimative. The results indicate vegetation cover's reduction, intensive surface's sealing and suppression of water bodies. These factors imply changes of hydrological dynamics of the source, increasing flow and transfer of larger volumes of water and flood peaks to downstream sectors. The use of geotechnology allowed analyzing the evolution of urbanization and it permits also to infer about trends for future or inadequate occupancy to hydrological and environmental point of view. © 2013 IEEE.

An Intercomparison of Regional Atmospheric Circulation and the Melt Season Loss of Arctic Snow Cover and Sea Ice Extent Across the Land-Ocean Boundary

This study is designed to compare the monthly continental snow cover and sea ice extent loss in the Arctic with regional atmospheric conditions including: mean sea level pressure, 925 hPa air temperature, and mean wind direction among others during the melt season (March-August) over the 29-year study period 1979-2007. Little research has gone into studying the concurrent variations in the annual loss of continental snow cover and sea ice extent across the land-ocean boundary, since these data are largely stored in incompatible formats. However, the analysis of these data, averaged spatially over three autonomous study regions located in Siberia, North America, and Western Russia, reveals a distinct difference in the response of snow and sea ice to the atmospheric forcing. On average, sea ice extent is lost earlier in the year, in May, than snow cover, in June, although Arctic sea ice is located farther north than continental snow in all three study regions. Once the loss of snow and ice extent begins, snow cover is completely removed sooner than sea ice extent, even though ice loss begins earlier in the melt season. Further, the analysis of the atmospheric conditions surrounding loss of snow and ice cover over the independent study regions indicates that conditions of cool temperatures with strong northeasterly winds in the later melt season months are effective at removing sea ice cover, likely through ice divergence, as are warmer temperatures via southerly winds directly forcing melt. The results of this study set the framework for further analysis of the direct influence of snow cover loss on later melt season sea ice extents and the predictability of snow and sea ice extent responses to modeled future climate conditions

GIS based Watershed Classification in Lao PDR

Every inclined land surface has a potential for soil and water degradation, the seriousness depends on a multitude of parameters such as slope, soil type, geomorphology, rainfall, land use and natural vegetation cover. In Laos this intensified land use leads to reduced vegetation cover, to increased soil erosion, decreasing yield, and finally is likely to influence the hydrological regime. Against this background the Mekong River Commission (MRC) elaborated a spatial explicit Watershed Classification (WSC) for the Lower Mekong Basin. Based on topographic factors derived from a high-resolution Digital Terrain Model, five watershed classes are calculated, giving indication about the sensitivity to resource degradation by soil erosion. The WSC allows spatial priority setting for watershed management and generally supports informed decision making on reconnaissance level. In the conclusions focus is laid on general considerations when GIS techniques are used for spatial decision support in a development context.

Comparing remote sensing and ground-based methods of quantifying cover type and carbon storage in an urban forest

Understanding the canopy cover of an urban environment leads to better estimates of carbon storage and more informed management decisions by urban foresters. The most commonly used method for assessing urban forest cover type extent is ground surveys, which can be both timeconsuming and expensive. The analysis of aerial photos is an alternative method that is faster, cheaper, and can cover a larger number of sites, but may be less accurate. The objectives of this paper were (1) to compare three methods of cover type assessment for Los Angeles, CA: handdelineation of aerial photos in ArcMap, supervised classification of aerial photos in ERDAS Imagine, and ground-collected data using the Urban Forest Effects (UFORE) model protocol; (2) to determine how well remote sensing methods estimate carbon storage as predicted by the UFORE model; and (3) to explore the influence of tree diameter and tree density on carbon storage estimates. Four major cover types (bare ground, fine vegetation, coarse vegetation, and impervious surfaces) were determined from 348 plots (0.039 ha each) randomly stratified according to land-use. Hand-delineation was better than supervised classification at predicting ground-based measurements of cover type and UFORE model-predicted carbon storage. Most error in supervised classification resulted from shadow, which was interpreted as unknown cover type. Neither tree diameter or tree density per plot significantly affected the relationship between carbon storage and canopy cover. The efficiency of remote sensing rather than in situ data collection allows urban forest managers the ability to quickly assess a city and plan accordingly while also preserving their often-limited budget.

Land classification ....

Mimeographed.

Farm appraisal : classification and valuation of farm land and buildings /

Includes bibliographies.

Farm appraisal : classification and valuation of farm land and buildings /

Includes bibliographies.

Missouri River Basin investigations; land planning and classification reports relative to public domain lands. With a preliminary report.

Field investigations and reports completed by the Missouri River Basin staff of the Bureau of Land Management, Area 3.