Canopy reflectance modelling of semiarid vegetation

Three different types of remote sensing algorithms for estimating vegetation amount and other land surface biophysical parameters were tested for semiarid environments. These included statistical linear models, the Li-Strahler geometric-optical canopy model, and linear spectral mixture analysis. The two study areas were the National Science Foundation's Jornada Long Term Ecological Research site near Las Cruces, NM, in the northern Chihuahuan desert, and the HAPEX-Sahel site near Niamey, Niger, in West Africa, comprising semiarid rangeland and subtropical crop land. The statistical approach (simple and multiple regression) resulted in high correlations between SPOT satellite spectral reflectance and shrub and grass cover, although these correlations varied with the spatial scale of aggregation of the measurements. The Li-Strahler model produced estimated of shrub size and density for both study sites with large standard errors. In the Jornada, the estimates were accurate enough to be useful for characterizing structural differences among three shrub strata. In Niger, the range of shrub cover and size in short-fallow shrublands is so low that the necessity of spatially distributed estimation of shrub size and density is questionable. Spectral mixture analysis of multiscale, multitemporal, multispectral radiometer data and imagery for Niger showed a positive relationship between fractions of spectral endmembers and surface parameters of interest including soil cover, vegetation cover, and leaf area index

Improved canopy reflectance modeling and scene inference through improved understanding of scene pattern

The Li-Strahler reflectance model, driven by LANDSAT Thematic Mapper (TM) data, provided regional estimates of tree size and density within 20 percent of sampled values in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). Trees are treated as simply shaped objects, and multispectral reflectance of a pixel is assumed to be related only to the proportions of tree crown, shadow, and understory in the pixel. These, in turn, are a direct function of the number and size of trees, the solar illumination angle, and the spectral signatures of crown, shadow and understory. Given the variance in reflectance from pixel to pixel within a homogeneous area of woodland, caused by the variation in the number and size of trees, the model can be inverted to give estimates of average tree size and density. Because the inversion is sensitive to correct determination of component signatures, predictions are not accurate for small areas

Estimation of grass photosynthesis rates in mixed-grass prairie using field and remote sensing approaches

With the increase in atmospheric CO2 concentrations, and the resulting potential for climate change, there has been increasing research devoted to understanding the factors that determine the magnitude of CO2 fluxes and the feedback of ecosystem fluxes on climate. This thesis is an effort to investigate the feasibility of using alternate methods to measure and estimate the CO2 exchange rates in the northern mixed grass prairie. Specifically, the objectives are to evaluate the capability of using ground-level hyperspectral, and satellite-level multispectral data in the estimation of mid-season leaf CO2 exchange rates as measured with a chamber, in and around Grasslands National Park (GNP), Saskatchewan. Data for the first manuscript was collected during June of 2004 (the approximate period for peak greenness for the study area). Spectral reflectance and CO2 exchange measurements were collected from 13 sites in and around GNP. Linear regression showed that the Photochemical Reflectance Index (PRI) calculated from hyperspectral ground-level data explained 46% of the variance seen in the CO2 exchange rates. This indicates that the PRI, which has traditionally been used only in laboratory conditions to predict CO2 exchange, can also be applied at the canopy level in grassland field conditions. The focus of the second manuscript is to establish if the relationship found between ground-level hyperspectral data and leaf CO2 exchange is applicable to satellite-level derived vegetation indices. During June of 2005, biophysical and CO2 exchange measurements were collected from 24 sites in and around GNP. A SPOT satellite image was obtained from June 22, midway through the field data collection. Cubic regression showed that Normalized Difference Vegetation Index (NDVI) explained 46% of the variance observed in the CO2 exchange rates. To our knowledge, this is the first time that a direct correlation between satellite images and leaf CO2 fluxes has been shown within the grassland biome

Assessing responses of grasslands to grazing management using remote sensing approaches

Grazing caused grassland degradation has occurred worldwide in recent decades. In spite of numerous efforts that have been invested to explore the mechanism of grassland responses to grazing management, the major challenge remains monitoring the responses over large area. This research evaluates the synthetic use of remote sensing data and the Milchunas-Sala-Lauenroth (MSL) model for grazing impact assessment, aiming to explore the potential of remotely sensed data to investigate the responses of grasslands to various grazing intensities across different grassland types. By combining field collected biophysical parameters, ground hyperspectral data and satellite imagery with different resolutions, this research concluded that 1) sampling scale played an important role in vegetation condition assessment. Adjusted transformed soil-adjusted vegetation index (ATSAVI) derived from remote sensing imagery with 10m or 20m spatial resolution was suitable for measuring leaf area index (LAI) changes in post-grazing treatment in the grazing experimental site; 2) canopy height and the ratio of photosynthetically to non-photosynthetically active vegetation cover were identified as the most sensitive biophysical parameters to reflect vegetation changes in mixed grasslands under light to moderate grazing intensities; 3) OSAVI (Optimised soil adjusted vegetation index) derived from Landsat Thematic Mapper (TM) image can be used for grassland production estimation under various grazing intensities in three types of grasslands in Inner Mongolia, China, with an accuracy of 76%; and 4) Grassland production predicted by NCI (Normalized canopy index) showed significant differences between grazed and ungrazed sites in years with above average and average growing season precipitation, but not in dry years, and 75% of the variation in production was explained by growing season precipitation (April-August) for both grazed and ungrazed sites