Estimating forest parameters using Landsat ETM+ spectral responses and monocultured plantation fieldwork measurements data

Forest parameters, such as mean diameter at breast height (DBH), mean stand height (H) or volume per hectare (V), are imperative for forest resources assessment. Traditional forest inventory that is usually based on fieldwork is often difficult, time-consuming, and expensive to conduct over large areas. Therefore, estimating forest parameters in large areas using a traditional inventory approach combined with satellite data analysis can improve the spatial estimates of forest inventory data, and hence be useful for sustainable forest management and natural resources assessment. However, extracting practical information from satellite imagery for such purpose is a challenging task mainly because of insufficient knowledge linking forest inventory data to satellite spectral response. Here, we present the use of a cost-free Landsat-7 Enhanced Thematic Mapper Plus (ETM+) in order to explore whether it is possible to combine all available optical bands from a specific sensor for improving forest parameter spatial estimates, based on fieldwork at Lahav and Kramim Forests, in the Israeli Northern Negev. A generic strategy, based on morphological structuring element, convex hall and spectral band linear combination algorithms, was developed in order to extract the mathematical dependencies between the forest inventory measurements and linear combination sets of Landsat-7 ETM+ spectral bands, which yields the highest possible correlation with the forest inventory measured data. Using the mathematical dependency functions, we then convert the entire Landsat-7 ETM+ scenes into forest inventory parameter values with sufficient accuracy and tolerance errors needed for sustainable forest management. The root mean square error obtained between the measured and the estimated values for Lahav Forest are 0.70 cm, 0.29 m, and 1.48 m³ ha^?1 for the mean DBH, H, and V, respectively, and for Kramim forest are 0.61 cm, 0.70 m, and 6.31 m³ ha^?1, respectively. Furthermore, the suggested strategy could also be applied with other satellites data sources.     

Modelling forest stand volume and tree density using Landsat ETM+ data

Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources. In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETM+) reflectance values. We used multivariate regression technique to predict stand volume and tree density. The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R² = 43%; root mean square error (RMSE) = 97.4 m³ ha^?1) than other ETM+ bands and vegetation indices. In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R² = 73.4%; RMSE = 170.13 ha^?1) than other combinations of ETM⁺ bands and vegetation indices. Results obtained from this study demonstrate the significant relationship between forest stand characteristics and ETM+ reflectance values and the utility of transformed bands in modelling stand volume and tree density. Based on the results of this study, we conclude that ETM+ data are useful to estimate forest volume and density and to gain insights into its structural characteristics in our study area. Forest managers could use ETM+ data for gaining insights into stand characteristics and generating maps required for developing forest management plans and identifying locations within stands that require treatments and other interventions.     

Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery

A lack of spatially and thematically accurate vegetation maps complicates conservation and management planning, as well as ecological research, in tropical rain forests. Remote sensing has considerable potential to provide such maps, but classification accuracy within primary rain forests has generally been inadequate for practical applications. Here we test how accurately floristically defined forest types in lowland tropical rain forests in Peruvian Amazonia can be recognized using remote sensing data (Landsat ETM+ satellite image and STRM elevation model). Floristic data and a vegetation classification with four forest classes were available for eight line transects, each 8 km long, located in an area of ca 800 km². We compared two sampling unit sizes (line transect subunits of 200 and 500 m) and several image feature combinations to analyze their suitability for image classification. Mantel tests were used to quantify how well the patterns in elevation and in the digital numbers of the satellite image correlated with the floristic patterns observed in the field. Most Mantel correlations were positive and highly significant. Linear discriminant analysis was used first to build a function that discriminates between forest classes in the eight field-verified transects on the basis of remotely sensed data, and then to classify those parts of the line transects and the satellite image that had not been visited in the field. Classification accuracy was quantified by 8-fold crossvalidation. Two of the tierra firme (non-inundated) forest types were combined because they were too often misclassified. The remaining three forest types (inundated forest, terrace forest and Pebas formation/intermediate tierra firme forest) could be separated using the 500-m sampling units with an overall classification accuracy of 85% and a Kappa coefficient of 0.62. For the 200-m sampling units, the classification accuracy was clearly lower (71%, Kappa 0.35). The forest classification will be used as habitat data to study wildlife habitat use in the same area. Our results show that remotely sensed data and relatively simple classification methods can be used to produce reasonably accurate forest type classifications, even in structurally homogeneous primary rain forests.     

Estimating forest canopy fuel parameters using LIDAR data

Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to accurately map the spatial distribution of canopy fuels and model fire behavior over the landscape. The use of airborne laser scanning (LIDAR), a high-resolution active remote sensing technology, provides for accurate and efficient measurement of three-dimensional forest structure over extensive areas. In this study, regression analysis was used to develop predictive models relating a variety of LIDAR-based metrics to the canopy fuel parameters estimated from inventory data collected at plots established within stands of varying condition within Capitol State Forest, in western Washington State. Strong relationships between LIDAR-derived metrics and field-based fuel estimates were found for all parameters [sqrt(crown fuel weight): R²=0.86; ln(crown bulk density): R²=0.84; canopy base height: R²=0.77; canopy height: R²=0.98]. A cross-validation procedure was used to assess the reliability of these models. LIDAR-based fuel prediction models can be used to develop maps of critical canopy fuel parameters over forest areas in the Pacific Northwest.     

Mapping northern land cover fractions using Landsat ETM+

The goal of fractional mapping is to obtain land cover fraction estimates within each pixel over a region. Using field, Ikonos and Landsat data at three sites in northern Canada, we evaluate a physical unmixing method against two modeling approaches to map five land cover fractions that include bare, grass, deciduous shrub, conifer, and water along an 1100 km north-south transect crossing the tree-line of northern Canada. Error analyses are presented to assess factors that affect fractional mapping results, including modeling method (linear least squares inversion (LLSI) vs. linear regression vs. regression trees), number of Landsat spectral bands (3 vs. 5), local and distant fraction estimation using locally and globally calibrated models, and spatial resolution (30 m vs. 90 m). The ultimate purpose of this study is to determine if reliable land cover fractions can be obtained for biophysical modeling over northern Canada from a three band, resampled 90 m Landsat ETM+ mosaic north of the tree-line. Of the three modeling methods tested, linear regression and regression trees with five spectral bands produced the best local fraction estimates, while LLSI produced comparable results when unmixing was sufficiently determined. However, distant fraction estimation using both locally and globally calibrated models was most accurate using the three spectral bands available in the Landsat mosaic of northern Canada at 30 m resolution, and only slightly worse at 90 m resolution. While local calibrations produced more accurate fractions than global calibrations, application of local calibration models requires stratification of areas where local endmembers and models are representative. In the absence of such information, globally calibrated linear regression and regression trees to estimate separate fractions is an acceptable alternative, producing similar root mean square error, and an average absolute bias of less than 2%.     

Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA

Aboveground biomass (AGB; Mg/ha) is defined in this study as a biomass of growing stock trees greater than 2.5 cm in diameter at breast height (dbh) for stands >5 years and all trees taller than 1.3 m for stands <5 years. Although AGB is an important variable for evaluating ecosystem function and structure across the landscape, such estimates are difficult to generate without high-resolution satellite data. This study bridges the application of remote sensing techniques with various forest management practices in Chequamegon National Forest (CNF), Wisconsin, USA by producing a high-resolution stand age map and a spatially explicit AGB map. We coupled AGB values, calculated from field measurements of tree dbh, with various vegetation indices derived from Landsat 7 ETM+ data through multiple regression analyses to produce an initial biomass map. The initial biomass map was overlaid with a land-cover map to generate a stand age map. Biomass threshold values for each age category (e.g., young, intermediate, and mature) were determined through field observations and frequency analysis of initial biomass estimates by major cover types. We found that AGB estimates for hardwood forests were strongly related to stand age and near-infrared reflectance (r²=0.95) while the AGB for pine forests was strongly related to the corrected normalized difference vegetation index (NDVIc; r²=0.86). Separating hardwoods from pine forests improved the AGB estimates in the area substantially, compared to overall regression (r²=0.82). Our AGB results are comparable to previously reported values in the area. The total amount of AGB in the study area for 2001 was estimated as 3.3 million metric tons (dry weight), 76.5% of which was in hardwood and mixed hardwood/pine forests. AGB ranged from 1 to 358 Mg/ha with an average of 70 and a standard deviation of 54 Mg/ha. The AGB class with the highest percentage (16.1%) was between 81 and 100 Mg/ha. Forests with biomass values >200 Mg/ha accounted for less than 3% of the study area and were usually associated with mature hardwood forests. Estimated AGB was validated using independent field measurements (R²=0.67, p<0.001). The AGB and age maps can be used as baseline information for future landscape level studies such as quantifying the regional carbon budget, accumulating fuel, or monitoring management practices.     

Seasonally-managed wetland footprint delineation using Landsat ETM+ satellite imagery

One major challenge in water resource management is the estimation of evapotranspiration losses from seasonally managed wetlands. Quantifying these losses is complicated by the dynamic nature of the wetlands' areal footprint during the periods of flood-up and drawdown. We present a data-lean solution to this problem using an example application in the San Joaquin Basin, California. Through analysis of high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery, we develop a metric to better capture the extent of total flooded wetland area. The procedure is validated using year-long, continuously-logged field datasets for two wetlands within the study area. The proposed classification which uses a Landsat ETM + Band 5 (mid-IR wavelength) to Band 2 (visible green wavelength) ratio improves estimates by 30–50% relative to previous wetland delineation studies. Requiring modest ancillary data, the study results provide a practical and efficient option for wetland management in data-sparse regions or un-gauged watersheds.     

Comparison of two types of forest disturbance using multitemporal Landsat TM/ETM+ imagery and field vegetation data

Forest disturbances influence many landscape processes, including changes in microclimate, hydrology, and soil erosion. We analyzed the spectral response and temporal progress of two types of disturbances of spruce forest (bark beetle outbreak and clear-cuts) in the central part of Šumava Mountains at the border between the Czech Republic and Germany, Central Europe. The bark beetle (Ips typographus [L.]) outbreak in this region in the last 20 years resulted in regional-scale spruce forest decay. Clear-cutting was done here to prevent further bark-beetle propagation in the buffer zones.The aim of the study is to identify the differences in spectral response between the two types of forest disturbances and their temporal dynamics. General trends were analyzed throughout the study area, with sampled disturbance areas selected to assess the relationship between field vegetation data and their spectral response. Thirteen Landsat TM/ETM+ scenes from 1985 to 2007 were used for the assessment. The following spectral indices were estimated: NDMI, Tasseled Cap (Brightness, Greenness, Wetness), DI, and DI′. The DI′, Wetness, and Brightness indices show the highest sensitivity to forest disturbance for both disturbance types (clear-cuts and bark beetle outbreak). The multitemporal analysis distinguished three different stages of development. The highest spectral differences between the clear-cuts and the bark beetle disturbances were found in the period between 1996 and 2004 with increased levels of forest disturbance (repeated measures ANOVA, Scheffé post hoc test; p ≤ 0.05). Clear-cut disturbance resulted in significantly higher spectral differences from the original forest and occurred as a more discrete event in comparison to bark beetle outbreak.     

Simulating Landsat ETM+ imagery using DAIS 7915 hyperspectral scanner data

Any application in remote sensing requires specific datasets that are defined in terms of the spectral and spatial characteristics of the sensor. The question as to which spatial resolution is preferable for a particular problem is a difficult one, and represents a critical decision in the planning of a project. Remote sensing data with different spatial resolutions are often required. In such cases, an alternative to the purchase of new images is simulation. Simulation is often used to generate images that will be acquired by a future sensor such as using aircraft imagery to simulate a new, as yet unavailable, orbital sensor. Simulation has usually been carried out as a spatial degradation process. However, to make a realistic simulation it is necessary to take a number of factors into consideration. Two such factors are the point spread function (PSF) and the spectral response of the sensor. In this study, Digital Airborne Imaging Spectrometer (DAIS 7915) data for a test site from the La Mancha Alta region of Spain are used to simulate a Landsat Enhanced Thematic Mapper Plus (ETM+) image. The results are promising in that comparable images are produced by considering the PSF and spectral response of the Landsat ETM+.     

Relationship between Landsat MSS data and forest tree parameters

Abstract

Band ratios, indices and radiance in the four channels of the Multi-spectral Scanner (MSS) on the Landsat-4 satellite for October 1984 and March 1985 were correlated with mean tree parameters of teak plantations (age, mean tree height, mean tree diameter at breast height, mean canopy diameter, mean canopy volume). The Landsat MSS data for March (when teak trees are leafless) were more suitable than the Landsat MSS data for October for categorizing tree parameters.     

Automatic detection of coastal plumes using Landsat TM/ETM+ images

Riverine fresh water outflows create coastal plumes that are distinguished from surrounding sea water by their specific spectral signature. Coastal waters are unique ecosystems, and they are very important in terms of living resources and oceanographic processes. River plumes and coastal turbid waters have important effects on coastal marine ecosystems, and they also influence marine life cycles, sediment distribution, and pollution. Remote sensing and digital image-processing techniques provide an effective tool to detect and monitor these plume zones over large areas. The primary goal of this study was automatic detection and monitoring of coastal plume zones using multispectral Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) imagery. For that purpose, the proposed algorithm exploits spectral features of the multispectral images by using feature extraction and decision-making steps. The procedure has two main stages: (1) some pre-processing operations were applied to the images in order to extract the plume core reflectance values with maximum turbidity and offshore water mass reflectance values; (2) a k-means algorithm was applied with initial seed values of reflectance computed from the pre-processing stage to classify coastal plume zones. Spatial pattern and variability of optical characteristics of coastal plume zones were then defined following the results of the classification process. The algorithm was automatically applied in three different regions with three multispectral Landsat images acquired on different dates, and yielded a very high classification accuracy in detecting coastal plume zones.     

Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM+ data

Bromus tectorum (cheatgrass) is an annual Eurasian grass that has invaded rangelands of the western USA. Being both a fire follower and a fire promoter, it can rapidly exclude native vegetation and is among the greatest threats to conservation in the region. Key to land management is a strong understanding of B. tectorum distribution and density. Percentage ground cover of B. tectorum was estimated and mapped as a continuous variable over 13.3?million?ha in Nevada, USA. Estimation involved a statistical model derived from 262 training plots, two dates of six scenes from Landsat 7 ETM+ imagery collected in 2001, and elevation. Absence of B. tectorum in many plots led to a dataset that was left‐censored at zero for the response variable, B. tectorum ground cover. Tobit regression, a method for modelling censored data, was found to produce a better model from these data than ordinary least squares regression. The two dates of the imagery were used to derive a variable representing phenology of the landscape. The derived phenology (in quadratic form), elevation, and the late‐season green band were statistically significant in the model development. Additionally, a brightness index was used to limit estimates in bright and dark portions of the imagery such as playas and lakes. Final map accuracy determined from an additional 75 independent assessment plots showed good correspondence between sampled and estimated B. tectorum ground cover (r?=?0.71) and the root‐mean‐square error for estimated ground cover is 9.1%.     

Identifying ring geomorphic features in Lebanon using ASTER and Landsat 7 ETM+ images

Satellite images enable us to identify several geomorphologic features on the Earth's surface that could not be easily recognized on ground surface or by using conventional methods. This is mainly attributed to the optical advantages of remote sensing techniques. Thus, suspicious geomorphologic signatures can be observed on the terrain surface. These are mostly geologic‐controlled. Linear aspects are given most attention in many geological studies to reflect subsurface, hidden structures. In this study, using ENVI‐4.3 and ERDAS Imagine‐9.1 software to analyse ASTER and Landsat 7 ETM+ images of Lebanon, we exposed a miscellany of geomorphic ring structures on different geologic formations of the Lebanese terrain. The diameter of the recognized ring structures ranges from a few hundreds of metres to several kilometres. Preliminary field surveys were carried out on some of these structures in order to identify their origin. All the recognized structures are circular and semi‐rounded in their geomorphology; however, they are attributed either to subsurface crust processes or to the impact of falling meteoritic from outer space. The former were indicated through rock deformations and existence of intrusive bodies. Meteorite impact was induced from the glassy and metallic materials prevailing within the ring structure area, as well as from the interference of some rings.     

Estimating forest canopy density of tropical mixed deciduous vegetation using Landsat data: a comparison of three classification approaches

Although a number of image classification approaches are available to estimate forest canopy density (FCD) using satellite data, assessment of their relative performances with tropical mixed deciduous vegetation is lacking. This study compared three image classification approaches – maximum likelihood classification (MLC), multiple linear regression (MLR) and FCD Mapper – in estimating the FCD of mixed deciduous forest in Myanmar. The application of MLC and MLR was based on spectral reflectance of vegetation, whereas FCD Mapper was operated on integrating the biophysical indices derived from the reflectance of the vegetation. The FCD was classified into four categories: closed canopy forest (CCF; FCD ≥ 70%), medium canopy forest (MCF; 40% ≥ FCD < 70%), open canopy forest (OCF; 10% ≥ FCD < 40%) and non-forest (NF; FCD < 10%). In the three classification approaches, producer's and user's accuracies were higher for more homogeneous vegetation such as NF and CCF than for heterogeneous vegetation density (VD) such as OCF and MCF. FCD Mapper produced the best overall accuracy and kappa coefficient. This study revealed that only spectral reflectance is not enough to get good results in estimating FCD in tropical mixed deciduous vegetation. This study indicates that FCD Mapper, an inexpensive approach because it requires only validation data and thus saves time, can be applied to monitor tropical mixed deciduous vegetation over time at lower cost than alternative methods.     

Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data

Biomass fractions (total aboveground, branches and foliage) were estimated from a small footprint discrete-return LiDAR system in an unmanaged Mediterranean forest in central Spain. Several biomass estimation models based on LiDAR height, intensity or height combined with intensity data were explored. Raw intensity data were normalized to a standard range in order to remove the range dependence of the intensity signal. In general terms, intensity-based models provided more accurate predictions of the biomass fractions. Height models selected were mainly based on a percentile of the height distribution. Intensity models selected included variables that consider the percentage of the intensity accumulated at different height percentiles, which implicitly take into account the height distribution. The general models derived considering all species together were based on height combined with intensity data. These models yielded R² values greater than 0.58 for the different biomass fractions considered and RMSE values of 28.89, 18.28 and 1.51 Mg ha⁻¹ for aboveground, branch and foliage biomass, respectively. Results greatly improved for species-specific models using the main species present in each plot, with R² values greater than 0.85, 0.70 and 0.90 for black pine, Spanish juniper and Holm oak, respectively, and with lower RMSE for the biomass fractions. Reductions in LiDAR point density had only a small effect on the results obtained, except for those models based on a variation of the Canopy Reflection Sum, which was weighted by the mean point density. Based on the species-specific equations derived, Holm oak dominated plots showed the highest average carbon contained by aboveground biomass and branch biomass 44.66 and 31.42 Mg ha^− 1 respectively, while for foliage biomass carbon, Spanish juniper showed the highest average value (3.04 Mg ha^− 1).     

Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico

A number of methods to overcome the 2003 failure of the Landsat 7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) are compared in this article in a forest-monitoring application in the Yucatan Peninsula, Mexico. The objective of this comparison is to determine the best approach to accomplish SLC-off image gap-filling for the particular landscape in this region, and thereby provide continuity in the Landsat data sensor archive for forest-monitoring purposes. Four methods were tested: (1) local linear histogram matching (LLHM); (2) neighbourhood similar pixel interpolator (NSPI); (3) geostatistical neighbourhood similar pixel interpolator (GNSPI); and (4) weighted linear regression (WLR). All methods generated reasonable SLC-off gap-filling data that were visually consistent and could be employed in subsequent digital image analysis. Overall accuracy, kappa coefficients (κ), and quantity and allocation disagreement indices were used to evaluate unsupervised Iterative Self-Organizing Data Analysis (ISODATA) land-cover classification maps. In addition, Pearson correlation coefficients (r) and root mean squares of the error (RMSEs) were employed for estimates agreement with fractional land cover. The best results visually (overall accuracy > 85%, κ < 9%, quantity disagreement index < 5.5%, and allocation disagreement index < 12.5%) and statistically (r > 0.84 and RMSE < 7%) were obtained from the GNSPI method. These results suggest that the GNSPI method is suitable for routine use in reconstructing the imagery stack of Landsat ETM+ SLC-off gap-filled data for use in forest-monitoring applications in this type of heterogeneous landscape.     

Structural attributes of three forest types in central Spain and Landsat ETM+ information evaluated with redundancy analysis

Structural attributes of forest, such as canopy crown closure, stand height, stem density and basal area, derived from the third Spanish National Forest Inventory (IFN‐3) were used in combination with spectral information derived from Landsat Enhanced Thematic Mapper Plus (ETM+) imagery and topographic information to evaluate their relationships. To deal with the variability found in the literature, three different types of vegetation, dominated by conifers, evergreen sclerophyll and broad‐leaved deciduous trees, were analysed. In addition, the analyses were performed using three sets of plots filtered to be successively more homogeneous. A multivariate canonical ordination method, redundancy analysis (RDA), was used to enable the simultaneous evaluation of the two data sets and provide a useful graphical output highlighting the relationships between response (structural attributes) and explanatory (spectral and topographic) variables. Rank correlation analyses were also performed. The low percentage of explained variance at the multivariate analyses and low rank correlation coefficients made it difficult to derive practical empirical models. The strong influence of vegetation type on the results was confirmed, given that each type was sensitive to a different kind of spectral information. Finally, the results did not allow validation of the hypothesis that the relationship should be better when using a more homogeneous set of plots.     

Estimating stand structural details using nearest neighbor analyses to link ground data, forest cover maps, and Landsat imagery

Forest information over a landscape is often represented as a spatial mosaic of polygons, separated by differences in species composition, height, age, crown closure, productivity, and other variables. These polygons are commonly delineated on medium-scale photography (e.g., 1:15,000) by a photo-interpreter familiar with the inventory area, and displayed and stored in a Geographic Information System (GIS) layer as a forest cover map. Forest cover maps are used for multiple purposes including timber and habitat supply analyses, and carbon inventories, at a regional or management unit level, and for parks planning, operational planning, and selection of stands for many purposes at a local level. Attribute data for each polygon commonly include the variables used to delineate the polygon, and other variables that can be measured or estimated using these medium-scale photographs. Additional measures that can only be obtained via expensive ground measures or possibly on high resolution photographs (e.g., volume per unit area, biomass components per unit area, tree-list of species and diameters) are available only for a sample of polygons, or may have been gathered independently using a sample survey over the land area. Improved linkages over a variety of data sources may help to support landscape level analyses. This study presents an approach to combine information from a systematic (grid) ground survey, forest cover (polygon) data, and Landsat Thematic Mapper (TM) imagery using variable-space nearest neighbor methods to estimate (i) mean ground-measured attributes for each polygon, in particular, volume per ha (m³/ha), stems per ha, and quadratic mean diameter for each polygon; and (ii) variation of these ground attributes within polygons. The approach was initially evaluated using Monte Carlo simulations with known measures of these attributes. Nearest neighbor methods were then applied to an approximate 5000 ha area (about 1000 polygons) of high productivity, mountainous forests located near the Pacific Coast of British Columbia, Canada. Based on the simulation results, the use of Landsat pixel reflectances to estimate volume per ha, average tree size (i.e., quadratic mean diameter), and stems per ha did not show great promise in improving estimates for each polygon over using forest cover data alone. However, in application, the use of remotely sensed data provided estimates of within-polygon variability. At the same time, the estimated means of these three imputed variables over the entire study area were very similar to the representative sample estimates using the ground data only. Extensions to other variables such as ranges of diameters and numbers of snags may also be possible providing useful data for habitat and forest growth analysis.     

Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data

Integration of multisensor data provides the opportunity to explore benefits emanating from different data sources. A fusion between fraction images derived from spectral mixture analysis of Landsat-7 ETM+ and phased array L-band synthetic aperture radar (PALSAR) is introduced. The aim of this fusion is to improve the estimation accuracy of above-ground biomass (AGB) in lowland mixed dipterocarp forest. Spectral mixture analysis was applied to decompose a mixture of spectral components of Landsat-7 ETM+ into vegetation, soil, and shade fractions. These fraction images were integrated with PALSAR data using the discrete wavelet transform (DWT) and Brovey transform. As a comparison, spectral reflectance of Landsat-7 ETM+ was fused directly with PALSAR data. Backscatter of horizontal–horizontal and horizontal–vertical polarizations was also used to estimate AGB. Forest inventory was carried out in 77 randomly distributed plots, the data being used for either model development or validation. A local allometric equation was applied to calculate AGB per plot. Regression models were developed by integrating field measurements of 50 sample plots with remotely sensed data, e.g. fraction images, reflectance of Landsat-7 ETM+, and PALSAR data. The models developed were validated using 27 independent sample plots. The results showed that not all fused images significantly improved the accuracy of AGB estimation. The model based on Brovey transform using the reflectance of Landsat-7ETM+ and PALSAR produced an R² of only 0.03–0.10. By contrast, fusion between PALSAR data and fraction images using Brovey transform improved the accuracy of R² to 0.33–0.46. Further improvement in the accuracy of estimating AGB was observed when DWT was applied to integrate PALSAR with the reflectance of Landsat-7ETM+ (R² = 0.69–0.72) and PALSAR with fraction images (R² = 0.70–0.75).