Tasseled Cap Wetness Research Articles

Does fire regime affect both temporal patterns and drivers of vegetation recovery in a resilient Mediterranean landscape? A remote sensing approach at two observation levels

We investigate how fire regime may influence both temporal patterns and drivers of vegetation regeneration in a Natural Park (NW Spain) affected by a long history of fire events and human activities. To address this issue, we evaluate the suitability of five spectral indices derived from Landsat imagery (for the period 1992–2005) for estimating biophysical properties of vegetation and monitoring post-fire recovery. Complementarily, we assess the role of the observation level (pixel and patch) on the results, identifying which of them is more informative for land management. Tasselled Cap Wetness was the best-performing index and total cover was the vegetation property more closely related to spectral data. Most post-fire recovery occurred within the 2 years following the fire event. Fire recurrence did not influence patch extent or shape nor did it affect ecosystem resilience. However, patch extent and shape affected resilience. The relevance of the environmental drivers behind vegetation recovery was not related to fire recurrence and changed over time. Prior vegetation status and rainfall were the most important drivers, while topography and vegetation type had a more secondary role. Our results advocate the consideration of patches as the most appropriate organisational unit when monitoring vegetation recovery.

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, R 2 = 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.

Assessment of two methods to monitor wetness to control dust emissions, Owens Dry Lake, California

Legally mandated control of blowing dust from 250 km2 Owens Dry Lake, eastern California, USA is accomplished by wetting within shallow flooding basins. Monitoring of surface wetness is required to assess dust-control compliance. The cost, scale, heterogeneity and limited access prevent the use of moderate-resolution satellite data (e.g. Landsat Thematic Mapper (TM), 28.5 m, chosen a priori as the monitoring base) for calibrating directly to surface wetness. Instead, calibration and testing employed controlled spectrometry converted to equivalent broadbands of TM as surrogates.Spectra, photography and surface assessments were obtained from twenty-nine 0.07 m2 undisturbed surfaces varying from flooded to dry and emissive. Two competing broadband methods to assess surface wetting were tested with Landsat TM-equivalent data: TM band 5-equivalent reflectance (TMB5) as a surface wetness analogue; and tasseled cap wetness (TCW), calculated using all six TM-equivalent reflectance bands. Five performance criteria were evaluated: (1) progressive ranking, wet to dry; (2) wet and dry at ends of each index’s distribution; (3) no overlap, wet to dry; (4) large separation, moist/protected to dry/dust-emissive; and (5) clear yes/no dichotomy, whether moist/protected or not. For all criteria, TMB5 outperformed TCW. TMB5 reflectance of 0.19 provided an empirically consistent, verifiable threshold: below this, surfaces are sufficiently wet and protected from windborne dust emission.

Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data

In this study, we tested the Maximum Entropy model (Maxent) for its application and performance in remotely sensing invasive Tamarix sp. Six Landsat 7 ETM+ satellite scenes and a suite of vegetation indices at different times of the growing season were selected for our study area along the Arkansas River in Colorado. Satellite scenes were selected for April, May, June, August, September, and October and tested in single-scene and time-series analyses. The best model was a time-series analysis fit with all spectral variables, which had an AUC = 0.96, overall accuracy = 0.90, and Kappa = 0.79. The top predictor variables were June tasselled cap wetness, September tasselled cap wetness, and October band 3. A second time-series analysis, where the variables that were highly correlated and demonstrated low predictive strengths were removed, was the second best model. The third best model was the October single-scene analysis. Our results may prove to be an effective approach for mapping Tamarix sp., which has been a challenge for resource managers. Of equal importance is the positive performance of the Maxent model in handling remotely sensed datasets.

Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades

Information regarding the spatial extent and timing of flooding in the world's major wetlands is important to a wide range of research questions including global methane models, water management, and biodiversity assessments. The Florida Everglades is one of the largest wetlands in the US, and is subject to substantial development and pressures that require intensive hydrological modeling and monitoring. The Moderate Resolution Imaging Spectrometer (MODIS) is a global sensor with high frequency repeat coverage and significant potential for mapping wetland extent and dynamics at moderate spatial resolutions. In this study, empirical models to predict surface inundation in the Everglades were estimated using MODIS data calibrated to water stage data from the South Florida Water Management District for the calendar year 2004. The results show that hydropatterns in the Florida Everglades are strongly correlated to a Tasseled Cap wetness index derived from MODIS Nadir Bidirectional Reflectance Function Adjusted Reflectance data. Several indices were tested, including the Normalized Difference Wetness Index and the diurnal land surface temperature difference, but the Tasseled Cap wetness index showed the strongest correlation to water stage data across a range of surface vegetation types. Other variables included in the analysis were elevation and percent tree cover present within a pixel. Using logistic regression and ensemble regression trees, maps of water depth and flooding likelihood were produced for each 16-day MODIS data period in 2004. The results suggest that MODIS is useful for dynamic monitoring of flooding, particularly in wetlands with sparse tree cover.

Monitoring farmers' decisions on Mediterranean irrigated crops using satellite image time series

The main purpose of this study is to present a methodology for mapping and monitoring temporal signatures of Mediterranean crops over several years in irrigated areas, and to study their inter‐annual dynamics. These goals were achieved by remote sensing using 36 Landsat images from 2002 to 2005. Four crop maps, one for each year, with six agricultural categories and a thematic accuracy of 93%, 95%, 96% and 94% were obtained using a hybrid classifier. A mean of nine images produced these highly accurate results, but the absence of one image in the growth period of 2002 resulted in lower accuracies, particularly in fruit trees (85% of user accuracy). This highlights the importance of a multi‐temporal approach based on a relatively large number of images. After the classification results were validated, two parameters were used to characterize the dynamics of the four crops (rice, maize, alfalfa and fruit trees): greenness, extracted from the Normalized Difference Vegetation Index (NDVI), and wetness, calculated from the Tasselled Cap Wetness (TCW) Index. In order to differentiate the wetness origin of crops, an analysis of local daily precipitation (which could cause significant anomalies in the TCW coefficients) and water stored in the Susqueda reservoir (which may result in farmers making important management decisions when water is limited) was conducted during this four‐year period. After applying statistical analysis, the results showed that, of the four crops analysed, rice, alfalfa and fruit trees had more stable dynamics than maize, which was planted later in case of water deficit at the beginning of the irrigation campaign (in 2002) and earlier when the deficit occurred later (in 2005).

Ecological site descriptions and remotely sensed imagery as a tool for rangeland evaluation

We classified Landsat-7 enhanced thematic mapper plus (ETM+) satellite imagery within ecological site descriptions to identify spectrally anomalous locations and determine whether these correlated with anomalous ground locations. Sites located in the Montana plains were classified by their departure from mean values in tasseled cap brightness, greenness, and wetness components, stratified by ecological site description. The classification had 98% overall accuracy in identifying locations that were or were not outside the norm in productivity and exposed soil for their ecological site description. Success was explained by the high correlations between field measures of productivity and exposed soil compared with tasseled cap components. Using this modeling technique might help rangeland managers identify sites needing more detailed field inventory and (or) management attention.

A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data

Because of the complicated shorelines, inaccessibility and vast spread of some lakes, information on changing shorelines is difficult to acquire. A new water index (WI) has been applied to quantify changes in five saline and non‐saline Rift Valley lakes in Kenya using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. The WI is based on a logical combination of the Tasseled Cap Wetness (TCW) index and the Normalized Difference Water Index (NDWI). Using regression analysis with estimated shoreline coordinates, the WI detected the shorelines with an accuracy of 98.4%, which was 22.3% higher than the TCW, and 43.2% more accurate than the NDWI. Change detection was derived using image differencing followed by density slicing and unsupervised classification. The saline lakes (Bogoria, Nakuru and Elementaita) changed more with respect to the ratio of the change in the original surface areas than the non‐saline lakes (Baringo and Naivasha).

Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data

Although partial harvests are common in many forest types globally, there has been little assessment of the potential to map the intensity of these harvests using Landsat data. We modeled basal area removal and percent cover change in a study area in central Washington (northwestern USA) using biennial Landsat imagery and reference data from historical aerial photos and a system of inventory plots. First, we assessed the correlation of Landsat spectral bands and associated indices with measured levels of forest removal. The variables most closely associated with forest removal were the shortwave infrared (SWIR) bands (5 and 7) and those strongly influenced by SWIR reflectance (particularly Tasseled Cap Wetness, and the Disturbance Index). The band and indices associated with near-infrared reflectance (band 4, Tasseled Cap Greenness, and the Normalized Difference Vegetation Index) were only weakly correlated with degree of forest removal. Two regression-based methods of estimating forest loss were tested. The first, termed “state model differencing” (SMD), involves creating a model representing the relationship between inventory data from any date and corresponding, cross-normalized spectral data. This “state model” is then applied to imagery from two dates, with the difference between the two estimates taken as estimated change. The second approach, which we called “direct change modeling” (DCM), involves modeling forest structure changes as a single term using re-measured inventory data and spectral differences from corresponding image pairs. In a leave-one-out cross-validation process, DCM-derived estimates of harvest intensity had lower root mean square errors than SMD for both relative basal area change and relative cover change. The higher measured accuracy of DCM in this project must be weighed against several operational advantages of SMD relating to less restrictive reference data requirements and more specific resultant estimates of change.

Estimation of managed loblolly pine stand age and density with Landsat ETM+ data

We analyzed the relationship between Landsat ETM+ reflectance values and commercially managed loblolly pine ( Pinus taeda L.) stand characteristics in east Texas. Multivariate regression techniques were used to predict age and tree density for all stands. A linear combination of NDVI, ETM4/ETM3 and the tasseled cap wetness index was a better predictor of stand age ( R 2 = 78%) than other combinations of original bands and derived indices. However, models involving transformed bands did not improve the overall predictability of stand density ( R 2 = 60%). Results from the principal component analyses (PCA) conducted on mature stands (age > 18 years) yielded valuable information about the relationship between stand structure and reflectance values recorded by the ETM+ sensor. The first principal component was interpreted as a measure of stand complexity. A linear regression model with infrared bands 4 and 5 as independent variables was able to account for 76% of the variability in stand structure. A second model with transformed bands did not increase the amount of variability in stand structure. Results obtained from this study demonstrate the relationship between loblolly pine stand characteristics and ETM+ reflectance values and the utility of certain transformed bands. Forest managers could use ETM+ data for gaining insights about stand characteristics and this information would be also useful for generating maps required for developing forest management plans.

Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances

Vegetation indices and transformations have been used extensively in forest change detection studies. In this study, we processed multitemporal normalized difference moisture index (NDMI) and tasseled cap wetness (TCW) data sets and compared their statistical relationships and relative efficiencies in detecting forest disturbances associated with forest type and harvest intensity at five, two and one year Landsat acquisition intervals. The NDMI and TCW were highly correlated (>0.95 r 2) for all five image dates. There was no significant difference between TCW and NDMI for detecting forest disturbance. Using either a NDMI or TCW image differencing method, when Landsat image acquisitions were 5 years apart, clear cuts could be detected with nearly equal accuracy compared to images collected 2 years apart. Partial cuts had much higher commission and omission errors compared to clear cut. Both methods had 7–8% higher commission and 12–22% higher omission error to detect hardwood disturbance when it occurred in the first year of the 2-year interval (as compared to 1-year interval). Softwood and hardwood change detection errors were slightly higher at 2-year Landsat acquisition intervals compared to 1-year interval. For images acquired 1 and 2 years apart, NDMI forest disturbance commission and omission errors were slightly lower than TCW. The NDMI can be calculated using any sensor that has near-infrared and shortwave bands and is at least as accurate as TCW for detecting forest type and intensity disturbance in biomes similar to the Maine forest, particularly when Landsat images are acquired less than 2 years apart. Where partial cutting is the most dominant harvesting system as is currently the case in northern Maine, we recommend images collected every year to minimize (particularly omission) errors. However, where clear cuts or nearly complete canopy removal occurs, Landsat intervals of up to 5 years may be nearly as accurate in detecting forest change as 1 or 2 year intervals.

An Assessment of Snow Avalanche Paths and Forest Dynamics Using Ikonos Satellite Data

Ikonos panchromatic and multispectral satellite data were acquired in October 2000 and August 2002 for a test area along US Highway 2, the southern border of Glacier National Park (GNP), Montana, USA. The research goals were to map snow avalanche paths and to characterize vegetation patterns in selected paths for longitudinal (i.e., source, track, and runout) and transverse (i.e., inner, flanking, outer) zones as part of a study of forest dynamics and nutrient flux from paths into terrestrial and aquatic systems. In some valleys, as much as 50 percent of the area may be covered by snow avalanche paths, and as such, serve as an important carbon source servicing terrestrial and aquatic ecosystems. Snow avalanches move woody debris down‐slope by snapping, tipping, trimming, and excavating branches, limbs, and trees, and by injuring and scaring trees that remain in‐place. Further, snow avalanches alter the vegetation structure on paths through secondary plant succession of disturbed areas. Contrast and edge enhancements, Normalized Difference Vegetation Index (NDVI), and the Tasseled Cap greenness and wetness transformations were used to examine vegetation patterns in selected paths that were affected by high magnitude snow avalanches during the winter of 2001-2002. Using image transects organized in longitudinal patterns in paths and in forests, and transects arranged in transverse patterns across the sampled paths, the Tasseled Cap transforms (and NDVI values) were plotted and assessed. Preliminary results suggest that NDVI patterns are different for paths and forests, and Tasseled Cap greenness and wetness patterns are different for longitudinal and transverse zones that describe the morphology of snow avalanche paths. The differentiation of paths from the background forest and the characterization of paths by morphometric zones through remote sensing has implications for mapping forest disturbances and dynamics over time and for large geographic areas and for modeling nutrient flux in terrestrial and aquatic systems.

Assessing wildfire effects with Landsat thematic mapper data

We evaluated Landsat Thematic Mapper (TM) imagery to map forest survival after a wildfire using single-date and multi-date TM imagery. In addition, landscape patterns were measured to describe the wildfire effects on successional stage patterns, and their impacts on wildlife habitat. The study site was the 1991 Warner Creek Burn, covering 3669 ha, on the Willamette National Forest in western Oregon, USA. Regressions of TM band transformations were used to estimate forest survival. Single-date TM 4/5 accounted for 73% (P 0.0001) of the variation in post-burn canopy cover, whereas the TM difference (by ratio) imagery with stratification by pre-fire tasseled cap (TC) wetness explained 78% (P 0.0001). Verification of the best models using additional data in observed versus predicted post-burn canopy cover confirmed these results. The pre-fire landscape had a matrix of closed mature/old-growth stands comprising 77% of the area. After the burn, the early seral/rock stage expanded, the open mature/old-growth stage was created, and the closed mature/old-growth stage was reduced and fragmented. Overall habitat diversity and edge increased after the burn, but interior habitat decreased.