A study of habitat fragmentation in Southeastern Brazil using remote sensing and geographic information systems (GIS)

The purpose of this work was to study fragmentation of forest formations (mesophytic forest, riparian woodland and savannah vegetation (cerrado)) in a 15,774-ha study area located in the Municipal District of Botucatu in Southeastern Brazil (São Paulo State). A land use and land cover map was made from a color composition of a Landsat-5 thematic mapper (TM) image. The edge effect caused by habitat fragmentation was assessed by overlaying, on a geographic information system (GIS), the land use and land cover data with the spectral ratio. The degree of habitat fragmentation was analyzed by deriving: 1. mean patch area and perimeter; 2. patch number and density; 3. perimeter-area ratio, fractal dimension (D), and shape diversity index (SI); and 4. distance between patches and dispersion index (R). In addition, the following relationships were modeled: 1. distribution of natural vegetation patch sizes; 2. perimeter-area relationship and the number and area of natural vegetation patches; 3. edge effect caused by habitat fragmentation, the values of R indicated that savannah patches (R = 0.86) were aggregated while patches of natural vegetation as a whole (R = 1.02) were randomly dispersed in the landscape. There was a high frequency of small patches in the landscape whereas large patches were rare. In the perimeter-area relationship, there was no sign of scale distinction in the patch shapes, In the patch number-landscape area relationship, D, though apparently scale-dependent, tends to be constant as area increases. This phenomenon was correlated with the tendency to reach a constant density as the working scale was increased, on the edge effect analysis, the edge-center distance was properly estimated by a model in which the edge-center distance was considered a function of the to;al patch area and the SI. (C) 1997 Elsevier B.V. B.V.

Applications of remote sensing and geographic information systems in geomorphological studies: Safaga - El Quseir area, Red Sea coast, Egypt as an example

In den letzten drei Jahrzehnten sind Fernerkundung und GIS in den Geowissenschaften zunehmend wichtiger geworden, um die konventionellen Methoden von Datensammlung und zur Herstellung von Landkarten zu verbessern. Die vorliegende Arbeit befasst sich mit der Anwendung von Fernerkundung und geographischen Informationssystemen (GIS) für geomorphologische Untersuchungen. Durch die Kombination beider Techniken ist es vor allem möglich geworden, geomorphologische Formen im Überblick und dennoch detailliert zu erfassen. Als Grundlagen werden in dieser Arbeit topographische und geologische Karten, Satellitenbilder und Klimadaten benutzt. Die Arbeit besteht aus 6 Kapiteln. Das erste Kapitel gibt einen allgemeinen Überblick über den Untersuchungsraum. Dieser umfasst folgende morphologische Einheiten, klimatischen Verhältnisse, insbesondere die Ariditätsindizes der Küsten- und Gebirgslandschaft sowie das Siedlungsmuster beschrieben. Kapitel 2 befasst sich mit der regionalen Geologie und Stratigraphie des Untersuchungsraumes. Es wird versucht, die Hauptformationen mit Hilfe von ETM-Satellitenbildern zu identifizieren. Angewandt werden hierzu folgende Methoden: Colour Band Composite, Image Rationing und die sog. überwachte Klassifikation. Kapitel 3 enthält eine Beschreibung der strukturell bedingten Oberflächenformen, um die Wechselwirkung zwischen Tektonik und geomorphologischen Prozessen aufzuklären. Es geht es um die vielfältigen Methoden, zum Beispiel das sog. Image Processing, um die im Gebirgskörper vorhandenen Lineamente einwandfrei zu deuten. Spezielle Filtermethoden werden angewandt, um die wichtigsten Lineamente zu kartieren. Kapitel 4 stellt den Versuch dar, mit Hilfe von aufbereiteten SRTM-Satellitenbildern eine automatisierte Erfassung des Gewässernetzes. Es wird ausführlich diskutiert, inwieweit bei diesen Arbeitsschritten die Qualität kleinmaßstäbiger SRTM-Satellitenbilder mit großmaßstäbigen topographischen Karten vergleichbar ist. Weiterhin werden hydrologische Parameter über eine qualitative und quantitative Analyse des Abflussregimes einzelner Wadis erfasst. Der Ursprung von Entwässerungssystemen wird auf der Basis geomorphologischer und geologischer Befunde interpretiert. Kapitel 5 befasst sich mit der Abschätzung der Gefahr episodischer Wadifluten. Die Wahrscheinlichkeit ihres jährlichen Auftretens bzw. des Auftretens starker Fluten im Abstand mehrerer Jahre wird in einer historischen Betrachtung bis 1921 zurückverfolgt. Die Bedeutung von Regentiefs, die sich über dem Roten Meer entwickeln, und die für eine Abflussbildung in Frage kommen, wird mit Hilfe der IDW-Methode (Inverse Distance Weighted) untersucht. Betrachtet werden außerdem weitere, regenbringende Wetterlagen mit Hilfe von Meteosat Infrarotbildern. Genauer betrachtet wird die Periode 1990-1997, in der kräftige, Wadifluten auslösende Regenfälle auftraten. Flutereignisse und Fluthöhe werden anhand von hydrographischen Daten (Pegelmessungen) ermittelt. Auch die Landnutzung und Siedlungsstruktur im Einzugsgebiet eines Wadis wird berücksichtigt. In Kapitel 6 geht es um die unterschiedlichen Küstenformen auf der Westseite des Roten Meeres zum Beispiel die Erosionsformen, Aufbauformen, untergetauchte Formen. Im abschließenden Teil geht es um die Stratigraphie und zeitliche Zuordnung von submarinen Terrassen auf Korallenriffen sowie den Vergleich mit anderen solcher Terrassen an der ägyptischen Rotmeerküste westlich und östlich der Sinai-Halbinsel.

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 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.

Analysis of Thematic Classified Aerial Images Trough Multispectral and LIDAR Data

The application of thematic maps obtained through the classification of remote images needs the obtained products with an optimal accuracy. The registered images from the airplanes display a very satisfactory spatial resolution, but the classical methods of thematic classification not always give better results than when the registered data from satellite are used. In order to improve these results of classification, in this work, the LIDAR sensor data from first return (Light Detection And Ranging) registered simultaneously with the spectral sensor data from airborne are jointly used. The final results of the thematic classification of the scene object of study have been obtained, quantified and discussed with and without LIDAR data, after applying different methods: Maximum Likehood Classification, Support Vector Machine with four different functions kernel and Isodata clustering algorithm (ML, SVM-L, SVM-P, SVM-RBF, SVM-S, Isodata). The best results are obtained for SVM with Sigmoide kernel. These allow the correlation with others different physical parameters with great interest like Manning hydraulic coefficient, for their incorporation in a GIS and their application in hydraulic modeling.

Application of remote sensing and GIS in mineral resource mapping - An overview

Remote sensing, as a direct adjunct to field, lithologic and structural mapping, and more recently, GIS have played an important role in the study of mineralized areas. A review on the application of remote sensing in mineral resource mapping is attempted here. It involves understanding the application of remote sensing in lithologic, structural and alteration mapping. Remote sensing becomes an important tool for locating mineral deposits, in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithologic mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. In addition to these, understanding the use of hyperspectral remote sensing is crucial as hyperspectral data can help identify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, GIS forms the perfect tool in integrating and analyzing various georeferenced geoscience data in selecting the best sites of mineral deposits or rather good candidates for further exploration.

Forest cover monitoring in the Bara district (Nepal) with remote sensing and geographic information systems

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

Drought Climatology of Indo-Gangetic Region of India Using Remote Sensing and Crop Growth Simulation Models

Department of Atmospheric Sciences, Cochin University of Science and Technology

Mapping the spatial distribution of geomorphological processes in the Okstindan area of northern Norway, using Geomorphic Process Units as derived from remote sensing and ground survey

The delineation of Geomorphic Process Units (GPUs) aims to quantify past, current and future geomorphological processes and the sediment flux associated with them. Five GPUs have been identified for the Okstindan area of northern Norway and these were derived from the combination of Landsat satellite imagery (TM and ETM+) with stereo aerial photographs (used to construct a Digital Elevation Model) and ground survey. The Okstindan study area is sub-arctic and mountainous and is dominated by glacial and periglacial processes. The GPUs exclude the glacial system (some 37% of the study area) and hence they are focussed upon periglacial and colluvial processes. The identified GPUs are: 1. solifluction and rill erosion; 2. talus creep, slope wash and rill erosion; 3. accumulation of debris by rock and boulder fall; 4. rockwalls; and 5. stable ground with dissolved transport. The GPUs have been applied to a ‘test site’ within the study area in order to illustrate their potential for mapping the spatial distribution of geomorphological processes. The test site within the study area is a catchment which is representative of the range of geomorphological processes identified.

Performance Evaluation Of SMOS Soil Moisture Retrieval Parameters For Hydrological Application

Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarisation is implemented with modifications, which includes the effective temperatures simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R2 =0.359 for approach 4 (WRF-NOAH LSM based LST with optimized roughness parameters) followed by the approach 2 (optimized roughness parameters and MODIS based LST) (R2 =0.293), approach 3 (WRF-NOAH LSM based LST with no optimization) (R2 =0.267) and approach 1(MODIS based LST with no optimization) (R2 =0.163). Similarly, during the validation a highest performance is reported by approach 4. The other approaches are also following a similar trend as calibration. All the performances are depicted through Taylor diagram which indicates that the H polarisation using ECMWF based LST is giving a better performance for SMD estimation than the original SMOS L2 products at a catchment scale.

Meteomap: Generation Of Meteorological Variables For Distributed Physically-Based Hydrological Modeling

Distributed energy and water balance models require time-series surfaces of the meteorological variables involved in hydrological processes. Most of the hydrological GIS-based models apply simple interpolation techniques to extrapolate the point scale values registered at weather stations at a watershed scale. In mountainous areas, where the monitoring network ineffectively covers the complex terrain heterogeneity, simple geostatistical methods for spatial interpolation are not always representative enough, and algorithms that explicitly or implicitly account for the features creating strong local gradients in the meteorological variables must be applied. Originally developed as a meteorological pre-processing tool for a complete hydrological model (WiMMed), MeteoMap has become an independent software. The individual interpolation algorithms used to approximate the spatial distribution of each meteorological variable were carefully selected taking into account both, the specific variable being mapped, and the common lack of input data from Mediterranean mountainous areas. They include corrections with height for both rainfall and temperature (Herrero et al., 2007), and topographic corrections for solar radiation (Aguilar et al., 2010). MeteoMap is a GIS-based freeware upon registration. Input data include weather station records and topographic data and the output consists of tables and maps of the meteorological variables at hourly, daily, predefined rainfall event duration or annual scales. It offers its own pre and post-processing tools, including video outlook, map printing and the possibility of exporting the maps to images or ASCII ArcGIS formats. This study presents the friendly user interface of the software and shows some case studies with applications to hydrological modeling.

Forest harvest, residue treatment, reforestation and protection of water quality /

Tables.

Remote sensing and the private sector : issues for discussion.

Mode of access: Internet.