An improved strategy for regression of biophysical variables and Landsat ETM+ data.

By Warren B. Cohen, Thomas K. Maiersperger, S. T. Gower, D. Turner. 2003

484 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/708c1905ae9eeef6037fe48b6cf836a86f7241dc

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Citations in the corpus

Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position (2009, background): “A number of other studies have recognized this region of the reflectance spectrum as potentially important for tracking vegetation properties (Asner 1998, Eklundh et al. 2001, Cohen et al. 2003).”
Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest. (2005, methodology): “Cohen et al. (2003) compared traditional OLS (Y on X), inverse OLS (X on Y), and RMA regression models for predicting LAI in an agro-ecosystem and cover percentage at the boreal forest system used in this study; they determined that RMA regression maintained the variance of the observations in the…”
Remote sensing of forest biophysical variables using HyMap imaging spectrometer data (2005, methodology): “To improve LAI mapping, it has been suggested to derive land cover specific VI-LAI relationships using land cover maps (Chen et al., 2002; Cohen et al., 2003; Franklin et al., 1997).”
Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta, Vietnam (2013, background): “…that different phenological stages (leaf production, leaf constant, and leaf senescence) had different NDVI/EVI−LAI relationships due to variations in phenological plant development in response to temporal variations of climatic conditions (Davi et al. 2006; Cohen et al. 2003; Wang et al. 2005”
Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects (2009, background): “In all cases, large sample sizes improve estimation, but sometimes statistical approaches can be used to leverage small sample sizes (such as bootstrap or jackknifing procedures), allowing evaluation of accuracy when expensive field samples are sparse (Cohen et al., 2003).”
Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup (2006, background): “The authors thank D. Wickland for the continued support of the MODIS validation infrastructure.”
Pine plantation structure mapping using WorldView-2 multispectral image (2013, methodology): “Moreover, the mean height and mean DBH with 8% and 13.7% error of estimation, respectively, were estimated more accurately than basal area, stand volume, and stocking, where the error of estimation is up to 30%.”
Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon (2006, methodology): “Normalization was accomplished onaband-by-bandbasisusingthese “training”PIFswithreduced major axis (RMA) regression (Canty et al., 2004; Cohen et al., 2003 ).AnindependentsetoftestPIFs(13dark,13bright;n=26) was also manually selected (Fig. 2) for the purpose of validating thecommonscaleproducedbyth”
LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements (2008, unknown): “ables (Cohen et al., 2003; Curran et al., 2001; Hansen and Schjoerring, 2003; Hinzman et al., 1986; McMurtrey et al., 1994; Weiss and Baret, 1999).”
Parametric (modified least squares) and non-parametric (Theil–Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors (2005, methodology): “Cohen et al. (2003) argue that Geometric Mean (GM) regression (they label it RMA but we use the conventional term as in Sokal & Rohlf, 1981) is suitable for calibrating remote sensing models in the absence of information on measurement errors.”
Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction (2006, methodology): “Many approaches were focused on the retrieval of these parameters from passive optical systems, often by the use of regression models (Cohen et al., 2003; Colombo et al., 2003), and in some cases by using radiative transfer modeling (Koetz et al.”
Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota (2011, background): “In recent years, the estimationof forest biophysical parameters using satellite data has become increasingly routine (Franco-Lopez et al., 2001; Cohen et al., 2003; Pulliainen et al., 2003; Rauste, 2005; Hyde et al., 2006; Chopping et al., 2008; McRoberts et al., 2007, and many more).”
Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data (2009, background): “…variables, does not make unrealistic assumptions about spectral or groundmeasurement error, as in ordinary least-squares regression (Curran & Hay, 1986; Cohen et al., 2003), and addresses the problem of collinearity (dependence) among multiple independent and dependent variables (Helland, 1988).”
Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI–rainfall relationship (2003, methodology): “OLS regression, however, may not always be appropriate and other approaches have been suggested for use by the remote sensing research community (Cohen et al., 2003; Curran & Hay, 1986).”