Highforest - forest parameter estimation from high resolution remote sensing data

The aim of the study was to develop a tool for the estimation of forest variables using high-resolution satellite data. The tool included modular operative software. The image analysis methodology focused on the reduction of the known problems of the previous satellite image based methods, i.e. the saturation of the estimates at higher biomass levels and uncertainty in tree species estimation. Modern contextual image analysis methods were combined with the spectral information of the imagery. In the test application the tool used images from the Ikonos satellite with a ground resolution of one and four meters. The developed Forestime software estimated the forest variables by segmenting the imagery to ‘micro-stands’, by computing standwise image feature vectors for the stands from the input satellite image, and by combining ground reference data with clusters from an unsupervised clustering stage. The estimates are produced as weighted sums of the input sample class probabilities. The target variables in the study were stem volume, average stem diameter, stem number and tree species proportions. The RMSE % for total stem volume was 37.4 % ( % of mean), for average stem diameter 23.4 %, for stem number 87 %, for pine percentage 111 %, for spruce percentage 47 %, and for broad-leaved tree percentage 137 %. 1

Multiscale analysis and validation of the MODIS LAI product

The development of appropriate ground-based validation techniques is critical to assessing uncertainties associated with satellite databased products. In this paper, the second of a two-part series, we present a method for validation of the Moderate Resolution Imaging Spectroradiometer Leaf Area Index (MODIS LAI) product with emphasis on the sampling strategy for field data collection. Using a hierarchical scene model, we divided 30-m resolution LAI and NDVI images from Maun (Botswana), Harvard Forest (USA) and Ruokulahti Forest (Finland) into individual scale images of classes, region and pixel. Isolating the effects associated with different landscape scales through decomposition of semivariograms not only shows the relative contribution of different characteristic scales to the overall variation, but also displays the spatial structure of the different scales within a scene. We find that (1) patterns of variance at the class, region and pixel scale at these sites are different with respect to the dominance in order of the three levels of landscape organization within a scene; (2) the spatial structure of LAI shows similarity across the three sites, that is, ranges of semivariograms from scale of pixel, region and class are less than 1000 m. Knowledge gained from these analyses aids in formulation of sampling strategies for validation of biophysical products derived from moderate resolution sensors such as MODIS. For a homogeneous (within class) site, where the scales of class and region account for most of the spatial variation, a sampling strategy should focus more on using accurate land cover maps and selection of regions. However, for a heterogeneous (within class) site, accurate point measurements and GPS readings are needed

Multiscale analysis and validation of the MODIS LAI product

The development of appropriate ground-based validation techniques is critical to assessing uncertainties associated with satellite databased products. In this paper, the second of a two-part series, we present a method for validation of the Moderate Resolution Imaging Spectroradiometer Leaf Area Index (MODIS LAI) product with emphasis on the sampling strategy for field data collection. Using a hierarchical scene model, we divided 30-m resolution LAI and NDVI images from Maun (Botswana), Harvard Forest (USA) and Ruokulahti Forest (Finland) into individual scale images of classes, region and pixel. Isolating the effects associated with different landscape scales through decomposition of semivariograms not only shows the relative contribution of different characteristic scales to the overall variation, but also displays the spatial structure of the different scales within a scene. We find that (1) patterns of variance at the class, region and pixel scale at these sites are different with respect to the dominance in order of the three levels of landscape organization within a scene; (2) the spatial structure of LAI shows similarity across the three sites, that is, ranges of semivariograms from scale of pixel, region and class are less than 1000 m. Knowledge gained from these analyses aids in formulation of sampling strategies for validation of biophysical products derived from moderate resolution sensors such as MODIS. For a homogeneous (within class) site, where the scales of class and region account for most of the spatial variation, a sampling strategy should focus more on using accurate land cover maps and selection of regions. However, for a heterogeneous (within class) site, accurate point measurements and GPS readings are needed