Asim Banskota

Forest Monitoring Using Landsat Time Series Data: A Review

Abstract Unique among Earth observation programs, the Landsat program has provided continuous earth observation data for the past 41 years. Landsat data are systematically collected and archived following a global acquisition strategy. The provision of free, robust data products since 2008 has spurred a renaissance of interest in Landsat and resulted in an increasingly widespread use of Landsat time series (LTS) for multitemporal characterizations. The science and applications capacity has developed steadily since 1972, with the increase in sophistication offered over time incorporated into Landsat processing and analysis practices. With the successful launch of Landsat-8, the continuity of measures at scales of particular relevance to management and scientific activities is ensured in the short term. In particular, forest monitoring benefits from LTS, whereby a baseline of conditions can be interrogated for both abrupt and gradual changes and attributed to different drivers. Such benefits are enabled by data availability, analysis-ready image products, increased computing power and storage, as well as sophisticated image processing approaches. In this review, we present the status of remote sensing of forests and forest dynamics using LTS, including issues related to the sensors, data availability, data preprocessing, variables used in LTS, analysis approaches, and validation issues.

Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

The need for an efficient and standard technique for optimal spectral sampling of hyperspectral data during the inversion of canopy reflectance models has been the subject of many studies. The objective of this study was to investigate the utility of the discrete wavelet transform (DWT) for extracting useful features from hyperspectral data with which forest LAI can be estimated through inversion of a three dimensional radiative transfer model, the Discrete Anisotropy Radiative Transfer (DART) model. DART, coupled with the leaf optical properties model PROSPECT, was inverted with AVIRIS data using a look-up-table (LUT)-based inversion approach. We used AVIRIS data and in situ LAI measurements from two different hardwood forested sites in Wisconsin, USA. Prior to inversion, model-simulated and AVIRIS hyperspectral data were transformed into discrete wavelet coefficients using Haar wavelets. The LUT inversion was performed with three different datasets, the original reflectance bands, the full set of wavelet extracted features, and two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R2 = 0.77) than the original spectral bands (RMSE = 0.60, R2 = 0.47). The results indicate that the discrete wavelet transform can increase the accuracy of LAI estimates by improving the LUT-based inversion of DART (and, potentially, by implication, other terrestrial radiative transfer models) using hyperspectral data. The improvement in accuracy of LAI estimates is potentially due to different properties of wavelet analysis such as multi-scale representation, dimensionality reduction, and noise removal.

Improving within-genus tree species discrimination using the discrete wavelet transform applied to airborne hyperspectral data

Discrete wavelet analysis was assessed for its utility in aiding discrimination of three pine species (Pinus spp.) using airborne hyperspectral data (AVIRIS). Two different sets of Haar wavelet features were compared to each other and to calibrated radiance, as follows: (1) all combinations of detail and final level approximation coefficients and (2) wavelet energy features rather than individual coefficients. We applied stepwise discriminant techniques to reduce data dimensionality, followed by discriminant techniques to determine separability. Leave-one-out cross validation was used to measure the classification accuracy. The most accurate (74.2%) classification used all combinations of detail and approximation coefficients, followed by the original radiance (66.7%) and wavelet energy features (55.1%). These results indicate that application of the discrete wavelet transform can improve species discrimination within the Pinus genus.

An LUT-Based Inversion of DART Model to Estimate Forest LAI from Hyperspectral Data

The efficient inversion of complex, three-dimensional (3-D) radiative transfer models (RTMs), such as the discrete anisotropy radiative transfer (DART) model, can be achieved using a look-up table (LUT) approach. A pressing research priority in LUT-based inversion for a 3-D model is to determine the optimal LUT grid size and density. We present a simple and computationally efficient approach for populating an LUT database with DART simulations over a large number of spectral bands. In the first step, we built a preliminary LUT using model parameters with coarse increments to simulate reflectance for six broad bands of Landsat Thematic Mapper (TM). In the second step, the preliminary LUT was compared with the TM reflectance, and the optimal input ranges and realistic parameter combinations that led to simulations close to Landsat spectra were then identified. In the third step, this information was combined with a sensitivity study, and final LUTs were built for the full spectrum of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) narrow bands and six Landsat broad bands. The final LUT was inverted to estimate leaf area index (LAI) in northern temperate forests from AVIRIS and TM data. The results indicate that the approach used in this study can be a useful strategy to estimate LAI accurately by DART model inversion.

Utility of the Wavelet Transform for LAI Estimation Using Hyperspectral Data

We employed the discrete wavelet transform to refl ectance spectra obtained from hyperspectral data to improve estimation of LAI in temperate forests. We estimated LAI for 32 plots across a range of forest types in Wisconsin using hemispherical photography. Plot spectra were extracted from AVIRIS data and transformed into wavelet features using the Haar wavelet. Separately, subsets of spectral bands and the Haar features selected by a genetic algorithm were used as independent variables in linear regressions. Models using wavelet coeffi cients explained the most variance for both broadleaf plots (R 2 = 0.90 for wavelet features versus R 2 = 0.80 for spectral bands) and all plots independent of forest type (R 2 = 0.79 for wavelet features vs. R 2 = 0.58 for spectral bands). The forest-type specifi c models were better than the models using all plots combined. Overall, wavelet features appear superior to band refl ectances alone for estimating temperate forest LAI using hyperspectral data.