Todd A. Schroeder

Bringing an ecological view of change to Landsat-based remote sensing

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, longterm trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Validation of Solar Radiation Surfaces from MODIS and Reanalysis Data over Topographically Complex Terrain

Abstract The magnitude and distribution of incoming shortwave solar radiation (SW↓) has significant influence on the productive capacity of forest vegetation. Models that estimate forest productivity require accurate and spatially explicit radiation surfaces that resolve both long- and short-term temporal climatic patterns and that account for topographic variability of the land surface. This paper presents a validation of monthly average total (SW↓t) and diffuse ( SW↓df ) incoming solar radiation surfaces taken from North American Regional Reanalysis (NARR) data and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a mountainous region of the Pacific northwestern United States and Canada. A topographic solar radiation model based on a regionally defined clearness index was used to downscale the 32-km NARR SW↓t surfaces to 1 km, resulting in surfaces that better matched the spatial resolution of MODIS, as well as accounted for elevation and terrain effects including shadowing. Va...

Predicting forest successional stages using multitemporal Landsat imagery with forest inventory and analysis data

Forest succession is an important ecological process that has profound biophysical, biological and biogeochemical implications in terrestrial ecosystems. Therefore, information on forest successional stages over an extensive forested landscape is crucial for us to understand ecosystem processes, such as carbon assimilation and energy interception. This study explored the potential of using Forest Inventory and Analysis (FIA) plot data to extract forest successional stage information from remotely sensed imagery with three widely used predictive models, linear regression (LR), decision trees (DTs) and neural networks (NNs). The predictive results in this study agree with previous findings that multitemporal Landsat Thematic Mapper (TM) imagery can improve the accuracy of forest successional stage prediction compared to models using a single image. Because of the overlap of spectral signatures of forests in different successional stages, it is difficult to accurately separate forest successional stages into more than three broad age classes (young, mature and old) with reasonable accuracy based on the age information of FIA plots and the spectral data of the plots from Landsat TM imagery. Given the mixed spectral response of forest age classes, new approaches need to be explored to improve the prediction of forest successional stages using FIA data.

Estimating live forest carbon dynamics with a Landsat-based curve-fitting approach

Direct estimation of aboveground biomass with spectral reflectance data has proven challenging for high biomass forests of the Pacific Northwestern United States. We present an alternative modeling strategy which uses Landsats spatial, spectral and temporal characteristics to predict live forest carbon through integration of stand age and site index maps and locally calibrated Chapman-Richards curves. Predictions from the curve-fit model were evaluated at the local and landscape scales using two periods of field inventory data. At the pixel-level, the curve-fit model had large positive bias statistics and at the landscape scale over-predicted study area carbon for both inventory periods. Despite the over-estimation, the change in forest carbon estimated by the curve-fit model was well within the standard error of the inventory estimates. In addition to validating the curve-fit models carbon predictions we used Landsat data to evaluate the degree to which the field inventory plots captured the forest conditions of the study area. Landsat-based frequency histograms revealed the systematic sample of inventory plots effectively captured the broad range of forest conditions found inthe study area, whereas stand age trajectories revealed a temporally punctuated shift in landuse which was not spectrally detected by the inventory sample.