Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage

Red-attack damage caused by mountain pine beetle (Dentroctonus ponderosa Hopkins) infestation in stands of lodgepole pine (Pinus contorta) in the Prince George Forest Region of British Columbia was examined using multitemporal Landsat-7 ETM+ imagery acquired in 1999, 2000, and 2001. The image data were geometrically and atmospherically corrected, and processed using the Tasseled Cap Transformation (TCT) to obtain wetness indices. The final steps included pixel subtraction, enhancement, and thresholding of the wetness index differences. The resulting enhanced wetness difference index (EWDI) was used to interpret spectral patterns in stands with confirmed (through aerial survey) red-attack damage in 2001, and these EWDI patterns were compared to the patterns of reflectance in normal-colour composites. We stratified the aerial survey dataset into two levels and used the EWDI to discriminate classes of 10-29 red-attack trees and 30-50 red-attack trees, and a sample of healthy forest collected from inventory data. Classification accuracy of red-attack damage based on the EWDI ranged from 67% to 78% correct.     

Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection

Landsat satellite data has become ubiquitous in regional-scale forest disturbance detection. The Tasseled Cap (TC) transformation for Landsat data has been used in several disturbance-mapping projects because of its ability to highlight relevant vegetation changes. We used an automated composite analysis procedure to test four multi-date variants of the TC transformation (called “data structures” here) in their ability to facilitate identification of stand-replacing disturbance. Data structures tested included one with all three TC indices (brightness, greenness, wetness), one with just brightness and greenness, one with just wetness, and one called the Disturbance Index (DI) which is a novel combination of the three TC indices. Data structures were tested in the St. Petersburg region of Russia and in two ecologically distinct regions of Washington State in the US. In almost all cases, the TC variants produced more accurate change classifications than multi-date stacks of the original Landsat reflectance data. In general, there was little overall difference between the TC-derived data structures. However, DI performed better than the others at the Russian study area, where slower succession rates likely produce the most durable disturbance signal. Also, at the highly productive western Washington site, where the disturbance signal is likely the most ephemeral, DI and wetness performed worse than the larger data structures when a longer monitoring interval was used (eight years between image acquisitions instead of four). This suggests that both local forest recovery rates and the re-sampling interval should be considered in choosing a Landsat transformation for use in stand-replacing disturbance detection.     

Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat

Remote sensing of forest vertical structure is possible with lidar data, but lidar is not widely available. Here we map tropical dry forest height (RMSE = 0.9 m, R² = 0.84, range 0.6-7 m), and we map foliage height profiles, with a time series of Landsat and Advanced Land Imager (ALI) imagery on the island of Eleuthera, The Bahamas, substituting time for vertical canopy space. We also simultaneously map forest disturbance type and age. We map these variables in the context of avian habitat studies, particularly for wintering habitat of an endangered Nearctic-Neotropical migrant bird, the Kirtland's Warbler (Dendroica kirtlandii). We also illustrate relationships between forest vertical structure, disturbance type and counts of forage species important to the Kirtland's Warbler. The ALI imagery and the Landsat time series are both critical to the result for forest height, which the strong relationship of forest height with disturbance type and age facilitates. Also unique to this study is that seven of the eight image time steps are cloud-cleared images: mosaics of the clear parts of several cloudy scenes. We created each cloud-cleared image, including a virtually seamless ALI image mosaic, with regression tree normalization. We also illustrate how viewing time series imagery as red-green-blue composites of tasseled cap wetness (RGB wetness composites) aids reference data collection for classifying tropical forest disturbance type and age. Our results strongly support current Landsat Program production of co-registered imagery, and they emphasize the value of seamless time series of cloud-cleared imagery.     

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.     

Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data

Estimating the water status of vegetation is one of the most important elements in assessing forest fire danger. In this paper, laboratory measurement confirmed a relationship between leaf water status and the normalized difference water index (NDWI), derived from near-infrared and shortwave-infrared spectral data. Two results were confirmed: (a) NDWI is related to equivalent water thickness, and, (b) in addition to NDWI, the quantity of leaf material must be known in order to estimate vegetation dryness. Based on these findings, the authors developed a vegetation dryness index (VDI) to estimate global vegetation water content. VDI values, calculated by using SPOT/VEGETATION data, were applied to data from a 1998 forest fire in the Russian Far East. This led to two results: (a) VDI was useful for detecting areas with a high potential for ignition, and (b) VDI may have been able to detect the fire-spread direction.     

Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery

This paper compares the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to estimate the LST from four different seasons for the Twin Cities, Minnesota, metropolitan area. A map of percent impervious surface with a standard error of 7.95% was generated using a normalized spectral mixture analysis of July 2002 Landsat TM imagery. Our analysis indicates there is a strong linear relationship between LST and percent impervious surface for all seasons, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests percent impervious surface provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.     

Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating

This paper presents an empirical method for deriving fuel moisture content (FMC) for Mediterranean grasslands and shrub species based on multitemporal analysis of NOAA-AVHRR data. The results are based on 6 years of field measurements of FMC. The empirical function was derived from a 4-year series and includes multitemporal composites of AVHRR's normalized difference vegetation index (NDVI) and surface temperature (ST) values, as well as a function of the day of the year. It was tested using data from 2 other years on the same site as well as other sites with similar species but very distant from each other and with different elevation ranges. The results show that the model provides a consistent estimation of FMC, with high accuracies for all study sites and species considered, with r² values over 0.8 for both grasslands and shrub species. This performance enables the model to be used to derive spatial estimator of FMC, which is a key factor in operational fire danger management in Mediterranean conditions.     

Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA

Lidar provides enhanced abilities to remotely map leaf area index (LAI) with improved accuracies. We aim to further explore the capability of discrete-return lidar for estimating LAI over a pine-dominated forest in East Texas, with a secondary goal to compare the lidar-derived LAI map and the GLOBCARBON moderate-resolution satellite LAI product. Specific problems we addressed include (1) evaluating the effects of analysts and algorithms on in-situ LAI estimates from hemispherical photographs (hemiphoto), (2) examining the effectiveness of various lidar metrics, including laser penetration, canopy height and foliage density metrics, to predict LAI, (3) assessing the utility of integrating Quickbird multispectral imagery with lidar for improving the LAI estimate accuracy, and (4) developing a scheme to co-register the lidar and satellite LAI maps and evaluating the consistency between them. Results show that the use of different analysts or algorithms in analyzing hemiphotos caused an average uncertainty of 0.35 in in-situ LAI, and that several laser penetration metrics in logarithm models were more effective than other lidar metrics, with the best one explaining 84% of the variation in the in-situ LAI (RMSE = 0.29 LAI). The selection of plot size and height threshold in calculating laser penetration metrics greatly affected the effectiveness of these metrics. The combined use of NDVI and lidar metrics did not significantly improve estimation over the use of lidar alone. We also found that mis-registration could induce a large artificial discrepancy into the pixelwise comparison between the coarse-resolution satellite and fine-resolution lidar-derived LAI maps. By compensating for a systematic sub-pixel shift error, the correlation between two maps increased from 0.08 to 0.85 for pines (n = 24 pixels). However, the absolute differences between the two LAI maps still remained large due to the inaccuracy in accounting for clumping effects. Overall, our findings imply that lidar offers a superior tool for mapping LAI at local to regional scales as compared to optical remote sensing, accuracies of lidar-estimate LAI are affected not only by the choice of models but also by the absolute accuracy of in-situ reference LAI used for model calibration, and lidar-derived LAI maps can serve as reliable references for validating moderate-resolution satellite LAI products over large areas.     

Remote sensing of sunlight-induced chlorophyll fluorescence and reflectance of Scots pine in the boreal forest during spring recovery

A measurement campaign to assess the feasibility of remote sensing of sunlight-induced chlorophyll fluorescence (ChlF) from a coniferous canopy was conducted in a boreal forest study site (Finland). A Passive Multi-wavelength Fluorescence Detector (PMFD) sensor, developed in the LURE laboratory, was used to obtain simultaneous measurements of ChlF in the oxygen absorption bands, at 687 and 760 nm, and a reflectance index, the PRI (Physiological Reflectance Index), for a month during spring recovery. When these data were compared with active fluorescence measurements performed on needles they revealed the same trend. During sunny days fluorescence and reflectance signals were found to be strongly influenced by shadows associated with the canopy structure. Moreover, chlorophyll fluorescence variations induced by rapid light changes (due to transient cloud shadows) were found to respond more quickly and with larger amplitude under summer conditions compared to those obtained under cold acclimation conditions. In addition, ChlF at 760 nm was observed to increase with the chlorophyll content. During this campaign, the CO₂ assimilation was measured at the forest canopy level and was found remarkably well correlated with the PRI index.