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Remote sensing systems for monitoring and quantifying tropical deforestation in the Huallaga River Valley of Peru
Title
Remote sensing systems for monitoring and quantifying tropical deforestation in the Huallaga River Valley of Peru.
Creator
Echavarria, Fernando R., Florida Atlantic University, Craig, Alan K., Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description
This thesis examines the quantification of tropical deforestation, the use of remote sensing techniques for its scientific measurement, and the many controversies surrounding the problem. Aerial photographs and Landsat-based planimetric maps were used to determine the conversion of montane rain forest in a 1,000 km$\sp2$ sector of Peru's Huallaga River Valley. Between 1963 and 1976, 244 km$\sp2$ of forest (approximately a quarter of the study area) were converted to agricultural and other...
Show moreThis thesis examines the quantification of tropical deforestation, the use of remote sensing techniques for its scientific measurement, and the many controversies surrounding the problem. Aerial photographs and Landsat-based planimetric maps were used to determine the conversion of montane rain forest in a 1,000 km$\sp2$ sector of Peru's Huallaga River Valley. Between 1963 and 1976, 244 km$\sp2$ of forest (approximately a quarter of the study area) were converted to agricultural and other land uses, an apparent deforestation rate of 19 km$\sp2$/yr or approximately 1,872 ha/yr. The method entailed the cutting and weighing of strips of Mylar overlays. Despite the photogrammetric limitations, the results demonstrate an economical and practical technique that is readily applicable to developing countries. The potential of other remote sensing systems and the application of change detection techniques such as digital image subtraction to monitor deforestation is detailed.
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Date Issued
1989
PURL
http://purl.flvc.org/fcla/dt/14538
Subject Headings
Geography, Physical Geography, Environmental Sciences, Remote Sensing
Format
Document (PDF)
Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery
Title
Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery.
Creator
Johnson, Brian A., Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description
Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of...
Show moreFine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods., Since the two dissertation methods used an automated algorithm (Random Forest) for image classification, they are also less subjective and easier to apply to other study areas than most existing multi-scale object-based methods that rely on expert knowledge (i.e. decision rules developed based on detailed visual inspection of image segments) for classifying each type of land cover.
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Date Issued
2012
PURL
http://purl.flvc.org/FAU/3342110
Subject Headings
Image processing, Digital techniques, Remote sensing, Mathematics, Remote-sensing images, Computational intelligence, Cities and towns, Remote sensing, Environmental sciences, Remote sensing, Spatial analysis (Statistics)
Format
Document (PDF)
Remote sensing of evapotranspiration using automated calibration: development and testing in the state of Florida
Title
Remote sensing of evapotranspiration using automated calibration: development and testing in the state of Florida.
Creator
Evans, Aaron H., Obeysekera, Jayantha, Zhang, Caiyun, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description
Thermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and...
Show moreThermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and retrieved surface temperature. Disadvantage of these calibrations are 1) user must manually identify extremely dry and wet pixels in image 2) each calibration is only applicable over limited spatial extent. Producing larger maps is operationally limited due to time required to manually calibrate multiple spatial extents over multiple days. This dissertation develops techniques which automatically detect dry and wet pixels. LANDSAT imagery is used because it resolves dry pixels. Calibrations using 1) only dry pixels and 2) including wet pixels are developed. Snapshots of retrieved evaporative fraction and actual evapotranspiration are compared to eddy covariance measurements for five study areas in Florida: 1) Big Cypress 2) Disney Wilderness 3) Everglades 4) near Gainesville, FL. 5) Kennedy Space Center. The sensitivity of evaporative fraction to temperature, available energy, roughness length and wind speed is tested. A technique for temporally interpolating evapotranspiration by fusing LANDSAT and MODIS is developed and tested. The automated algorithm is successful at detecting wet and dry pixels (if they exist). Including wet pixels in calibration and assuming constant atmospheric conductance significantly improved results for all but Big Cypress and Gainesville. Evaporative fraction is not very sensitive to instantaneous available energy but it is sensitive to temperature when wet pixels are included because temperature is required for estimating wet pixel evapotranspiration. Data fusion techniques only slightly outperformed linear interpolation. Eddy covariance comparison and temporal interpolation produced acceptable bias error for most cases suggesting automated calibration and interpolation could be used to predict monthly or annual ET. Maps demonstrating spatial patterns of evapotranspiration at field scale were successfully produced, but only for limited spatial extents. A framework has been established for producing larger maps by creating a mosaic of smaller individual maps.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004194, http://purl.flvc.org/fau/fd/FA00004194
Subject Headings
Climatic changes, Environmental sciences -- Remote sensing, Evapotranspiration -- Measurement, Geographic information systems, Remote sensing -- Data processing, Spatial analysis (Mathematics)
Format
Document (PDF)