Geography

Permanent URI for this communityhttp://hdl.handle.net/1903/2242

Browse

End date: 2009Subject: search.filters.subject.remote sensing

Search Results

Now showing 1 - 8 of 8
  • Thumbnail Image
    Item
    mapping photosynthetically active radiation (PAR) using multiple remote sensing data
    (2007-07-11) zheng, tao; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Incident Photosynthetically Active Radiation (PAR) is an important parameter for terrestrial ecosystem models. Presently, deriving PAR using remotely sensed data is the only practical approach to meet the needs for large scale ecosystem modeling. The usefulness of the currently available PAR products is constricted by their limited spatial and temporal resolution. In addition, the applicability of the existing algorithms for deriving PAR using remotely sensed data are limited by their requirements for external atmospheric information. This study develops new algorithms to estimate incident PAR using remotely sensed data from MODIS (Moderate Resolution Imaging Spectroradiometer), GOES (Geostationary Operational Environmental Satellite), and AVHRR (Advanced Very High Resolution Radiometer). The new PAR algorithms differ from existing algorithms in that the new algorithms derive surface properties and atmospheric optical properties using time-series of at-sensor radiance without external atmospheric information. First, a new PAR algorithm is developed for MODIS visible band data. The validity of the algorithm's underpinning theoretical basis is examined and associated errors are analyzed in light of their impact on PAR estimation accuracy. Second, the MODIS PAR algorithm is adapted to AVHRR in order to take advantage of the long data acquisition record of AVHRR. In addition, the scaling of remote sensing derived instantaneous PAR to daily PAR is addressed. Last, the new algorithm is extended to GOES visible band data. Two major improvements of GOES PAR algorithm over that of MODIS and AVHRR are the inclusion of the bi-directional reflectance distribution function for deriving surface reflectance, and the procedure for excluding cloud-shadowed pixels in searching for observations made under clear skies. Furthermore, the topographic impact on PAR is accessed and corrected. To assess the effectiveness of the newly developed PAR algorithms, validation efforts have been made using ground measurements made at FLUXNET sites. The validations indicate that the new PAR algorithms for MODIS, GOES, and AVHRR are capable of reaching reasonably high accuracy with no need for external atmospheric information. This work is the first attempt to develop a unified PAR estimation algorithm for both polar-orbiting and geostationary satellite data. The new algorithms developed in this study have been used to produce incident PAR over North America routinely to support the North America Carbon Program.
  • Thumbnail Image
    Item
    Fire Dynamics and Woody Cover Changes in the Serengeti-Mara Ecosystem 2000 to 2005 - A Remote Sensing Approach
    (2007-01-21) Dempewolf, Jan; DeFries, Ruth; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The Serengeti-Mara savanna environment in East Africa is characterized by changing levels of woody cover and a dynamic fire regime. The relative proportion of woodland to grassland savanna affects animal habitat, biodiversity, and carbon storage, and is regulated by factors such as the fire regime (frequency, intensity, seasonality), and precipitation. The main objectives of this dissertation are to determine recent changes in woody cover at a regional scale and identify fire regimes and climate associated with these changes. Understanding these relationships is important for the assessment of future trajectories of woody cover under changing climate. Required spatially coherent data layers can only be obtained at the regional scale through the analysis of remote sensing data. Woody cover changes between 2000 and 2005 were derived from field data and a time series of MODIS satellite imagery at 500 m spatial resolution. Data layers on the controlling variables (fire frequency, seasonality, intensity and rainfall) were developed using a combination of remote sensing and model-based approaches. Burned areas were mapped using daily MODIS imagery at 250 m resolution. Outputs were used to make the requisite layers depicting fire frequency and seasonality. Fire intensity was derived using a model based on empirical relationships, mainly estimating fire fuel load as a function of rainfall and grazing. The combined data layers were analyzed using regression and decision tree techniques. Results suggest woody cover in central and northern Serengeti National Park continued to increase after 2000. Woody cover decreases were strongest in the wider Maswa Game Reserve area (MSW) under low precipitation conditions and late season burning. Woody cover losses in burned areas were also higher in the low fire frequency region of the Maasai Mara National Reserve (MNR). Fire seasonality was the most important fire regime parameter controlling woody cover in burned woodland savanna areas while fire intensity was most relevant for grassland savanna areas. Continued late season burning in drought years might cause further decrease of woody cover in MSW. MNR is expected to continue to be dominated by grassland savanna at similar fire frequency and browsing levels.
  • Thumbnail Image
    Item
    An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery
    (Institute of Electrical and Electronics Engineers, 2001-02) Liang, Shunlin
    Land surface temperature (LST) and emissivity are important components of land surface modeling and applications. The only practical means of obtaining LST at spatial and temporal resolutions appropriate for most modeling applications is through remote sensing. While the popular split-window method has been widely used to estimate LST, it requires known emissivity values. Multispectral thermal infrared imagery provides us with an excellent opportunity to estimate both LST and emissivity simultaneously, but the difficulty is that a single multispectral thermal measurement with bands presents equations in + 1 unknowns ( spectral emissivities and LST). In this study, we developed a general algorithm that can separate land surface emissivity and LST from any multispectral thermal imagery, such as moderate-resolution imaging spectroradiometer (MODIS) and advanced spaceborne thermal emission and reflection radiometer (ASTER). The central idea was to establish empirical constraints, and regularization methods were used to estimate both emissivity and LST through an optimization algorithm. It allows us to incorporate any prior knowledge in a formal way. The numerical experiments showed that this algorithm is very effective (more than 43.4% inversion results differed from the actual LST within 0.5 , 70.2% within 1 and 84% within 1.5 ), although improvements are still needed.
  • Thumbnail Image
    Item
    A Direct Algorithm for Estimating Land Surface Broadband Albedos From MODIS Imagery
    (Institute of Electrical and Electronics Engineers, 2003-01) Liang, Shunlin
    Land surface albedo is a critical variable needed in land surface modeling. The conventional methods for estimating broadband albedos rely on a series of steps in the processing chain, including atmospheric correction, surface angular modeling, and narrowband-to-broadband albedo conversions. Unfortunately, errors associated with each procedure may be accumulated and significantly impact the accuracy of the final albedo products. In an earlier study, we developed a new direct procedure that links the top-of-atmosphere spectral albedos with land surface broadband albedos without performing atmospheric correction and other processes. In this paper, this method is further improved in several aspects and implemented using actual Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Several case studies indicated that this new method can predict land surface broadband albedos very accurately and eliminate aerosol effects effectively. It is very promising for global applications and is particularly suitable for nonvegetated land surfaces. Note that a Lambertian surface has been assumed in the radiative transfer simulation in this paper as a first-order approximation; this assumption can be easily removed as long as a global bidirectional reflectance distribution function climatology is available,
  • Thumbnail Image
    Item
    Special Issue on Global Land Product Validation
    (Institute of Electrical and Electronics Engineers, 2006-07) Liang, Shunlin; Baret, Frédéric; Morisette, Jeffrey T.
    Overview of the Special Issue on Global Land Product Validation: In parallel with the recent bloom of sensors providing frequent medium-resolution observations (Fig. 1), global land products have been increasingly developed and released within the community. The raw data acquired by these sensors are transformed into higher level products that can be more easily exploited by the user community. In many cases, multiple products are developed from each sensor and similar products derived from different sensors. With this, users need access to quantitative information on product uncertainties to help them assess the most suitable product, or combination of products for their specific needs. As remote sensing observations are generally merged with other sources of information or assimilated within process models, evaluation of product accuracy is required. Making quantified accuracy information available to the user can ultimately provide developers the necessary feedback for improving the products, and can possibly provide methods for their fusion to construct a consistent long-term series of surface status.
  • Thumbnail Image
    Item
    An Improved Atmospheric Correction Algorithm for Hyperspectral Remotely Sensed Imagery
    (Institute of Electrical and Electronics Engineers, 2004-04) Liang, Shunlin; Fang, Hongliang
    There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra.
  • Thumbnail Image
    Item
    ENDANGERED DRY DECIDUOUS FORESTS OF UPPER MYANMAR (BURMA): A MULTI-SCALE APPROACH FOR RESEARCH AND CONSERVATION
    (2006-09-11) Songer, Melissa A.; DeFries, Ruth S.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Tropical dry forests are critically endangered and largely unprotected ecosystem. I used a multi-scale research approach to study Upper Myanmar's dry deciduous forests. At the broad scale I assessed how well existing land cover data can be used to map and monitor dry forests, and estimated the extent, distribution, and level of protection of these forests. At the landscape level I assessed spatial and temporal dynamics of deforestation in and around a dry forest protected area, Chatthin Wildlife Sanctuary (CWS), investigated land use pressures driving these changes, and evaluated effectiveness of protection efforts within the sanctuary. At the local scale I studied the degree to which people rely on dry forests for subsistence and the socioeconomic variables correlated with dependence on forest products. Using MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to delineate remaining dry deciduous forests, I found that only 24,000 km2 of this forest type remain in Upper Myanmar--only 4% inside protected areas. At 81% accuracy, this map scored higher than existing global and regional land cover classifications for predicting dry forest. Employing satellite images covering the landscape in and around CWS (Landsat MSS, TM, ETM+ and ASTER) between the years 1973-2005 , I found that 62% of forest was lost (1.93% annual rate) primarily from agricultural conversion and hydroelectric development. Sanctuary protection has been effective in slowing decline: loss rates inside CWS were 0.49% annually (16% total). However, forest inside the sanctuary is still declining at a rate above the global average and shows evidence of impact from forest product extraction around the boundaries. Based on interviews with 784 people living in 28 subsistence-based agricultural communities located in and around CWS, I found virtually all survey respondents depended on CWS for food, medicine, housing materials, and, above all, fuelwood. Poverty and socioeconomic limitations drive extractive activities. While CWS has been effective in slowing deforestation rates, alternative use strategies that benefit people will improve prospects for long-term conservation in the area. My results demonstrate that a multi-scaled research approach is essential for understanding the drivers impacting the rapidly-declining dry forests of Upper Myanmar.
  • Thumbnail Image
    Item
    Monitoring land degradation in Southern Africa by assessing changes in primary productivity.
    (2005-06-15) Wessels, Konrad; Prince, Stephen D.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Land degradation is one of the most serious environmental problems of our time. Land degradation describes circumstances of reduced biological productivity. The fundamental goal of this thesis was to develop land degradation monitoring approaches based on remotely sensed estimates of vegetation production, which are capable of distinguishing human impacts from the effects of natural climatic and spatial variability. Communal homelands in South Africa (SA) are widely regarded to be severely degraded and the existence adjacent, non-degraded areas with the same soils and climate, provides a unique opportunity to test regional land degradation monitoring methods. The relationship between 1km AVHRR, growth season sumNDVI and herbaceous biomass measurements (1989-2003) was firstly tested in Kruger National Park, SA. The relationship was moderately strong, but weaker than expected. This was attributed to the fact that the small areas sampled at field sites were not representative of the spatial variability within 1x1km. The sumNDVI adequately estimated inter-annual changes in vegetation production and should therefore be useful for monitoring land degradation. Degraded areas mapped by the National-Land-Cover in north-eastern SA were compared to non-degraded areas in the same land capability units. The sumNDVI of the degraded areas was consistently lower, regardless of large variations in rainfall. However, the ecological stability and resilience of the degraded areas, as measured by the annual deviations from each pixel's mean sumNDVI, were no different to those of non-degraded areas. This suggests that the degraded areas may be in an alternative, but stable ecological state. To monitor human-induced land degradation it is essential to control for the effects of rainfall on vegetation production. Two methods were tested (i) Rain-Use Efficiency (RUE=NPP/Rainfall) and (ii) negative trends in the differences between the observed sumNDVI and the sumNDVI predicted by the rainfall using regressions calculated for each pixel (RESTREND). RUE had a strong negative correlation with rainfall and did not provide a reliable index of degradation. The RESTREND method identified areas in and around the degraded communal lands that exhibit negative trends in production per unit rainfall. This research made a significant contribution to the development of remote sensing based land degradation monitoring methods.