Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey

We used a single-beam, first return profiling LIDAR (Light Detection and Ranging) measurements of canopy height, intensive biometric measurements in plots, and Forest Inventory and Analysis (FIA) data to quantify forest structure and ladder fuels (defined as vertical fuel continuity between the understory and canopy) in the New Jersey Pinelands. The LIDAR data were recorded at 400 Hz over three intensive areas of 1 km² where transects were spaced at 200 m, and along 64 transects spaced 1 km apart (total of ca. 2500 km²). LIDAR and field measurements of canopy height were similar in the three intensive study areas, with the 80th percentile of LIDAR returns explaining the greatest amount of variability (79%). Correlations between LIDAR data and aboveground tree biomass measured in the field were highly significant when all three 1 km² areas were analyzed collectively, with the 80th percentile again explaining the greatest amount of variability (74%). However, when intensive areas were analyzed separately, correlations were poor for Oak/Pine and Pine/Scrub Oak stands. Similar results were obtained using FIA data; at the landscape scale, mean canopy height was positively correlated with aboveground tree biomass, but when forest types were analyzed separately, correlations were significant only for some wetland forests (Pitch Pine lowlands and mixed hardwoods; r² = 0.74 and 0.59, respectively), and correlations were poor for upland forests (Oak/Pine, Pine/Oak and Pine/Scrub Oak, r² = 0.33, 0.11 and 0.21, respectively). When LIDAR data were binned into 1-m height classes, more LIDAR pulses were recorded from the lowest height classes in stands with greater shrub biomass, and significant differences were detected between stands where recent prescribed fire treatments had been conducted and unburned areas. Our research indicates that single-beam LIDAR can be used for regional-scale (forest biomass) estimates, but that relationships between height and biomass can be poorer at finer scales within individual forest types. Binned data are useful for estimating the presence of ladder fuels (vertical continuity of leaves and branches) and horizontal fuel continuity below the canopy.     

NDVI responses to the forest canopy and floor from spring to summer observed by airborne spectrometer in eastern Siberia

A popular method of satellite-based monitoring of the photosynthetic potential of vegetation is to calculate the normalised difference vegetation index (NDVI) from measurements of the red (RED) and near-infrared (NIR) bands. Enormous amounts of vegetation information have been obtained over continental to global areas based on NDVI derived from NOAA-AVHRR, Terra/Aqua-MODIS, and SPOT-VEGETATION satellite observations. In eastern Siberia, where sparse boreal forests are dominant, the lack of landscape-scale canopy-reflectance observations impedes interpretation of how NDVI seasonality is controlled by the forest canopy and floor status. We discuss the NDVI of the canopy and floor separately based on airborne spectral reflectance measurements and simultaneous airborne land surface images acquired around Yakutsk, Siberia, using a hedgehopping aircraft from spring to summer 2000. The aerial land surface images (4402 scenes) were visually classified into four types according to the forest condition: no-green canopy and snow floor (Type 1), green canopy and snow floor (Type 2), no-green canopy and no-snow floor (Type 3), and green canopy and no-snow floor (Type 4). The spectral reflectance from 350 to 1200 nm was then calculated for these four types. Type 1 had almost no difference in reflectance between the RED and NIR bands, and the resultant NDVI was slightly negative (− 0.03). Although Type 2 showed a significant difference between the two bands because of canopy greenness, the resultant NDVI was rather low (0.17) because of high reflection from the snow cover on the floor. In Type 3, the significant difference between the two bands was mainly caused by the greenness of the floor, and the NDVI was relatively high (0.45). The NDVI for Type 4 was the highest (0.75) among the four types. The contributions of reflectance from the forest canopy and floor to the total reflectance were tested with a forest radiative transfer model. The reflectance difference between NIR and RED bands (NIR − RED) of Type 4 (15.6%) was approximately double the differences of Type 2 (7.0%) and of Type 3 (7.9%), suggesting half-and-half contributions of forest canopy greenness and floor greenness to the total greenness. The result also suggested that the satellite-derived NDVI in the larch forest around Yakutsk reaches 85% of the maximum NDVI owing to the forest floor greenness, and only the other 15% of the increase in NDVI should be attributed to the canopy foliation. These results quantitatively reveal that the NDVI depends considerably on forest floor greenness and snow cover in addition to canopy greenness in the case of relatively sparse forest in Siberia.     

Prospects for quantifying structure,floristic composition and species richness of tropical forests

Airborne spectral and light detection and ranging (lidar) sensors have been used to quantify biophysical characteristics of tropical forests. Lidar sensors have provided high-resolution data on forest height, canopy topography, volume, and gap size; and provided estimates on number of strata in a forest, successional status of forests, and above-ground biomass. Spectral sensors have provided data on vegetation types, foliar biochemistry content of forest canopies, tree and canopy phenology, and spectral signatures for selected tree species. A number of advances are theoretically possible with individual and combined spectral and lidar sensors for the study of forest structure, floristic composition and species richness. Delineating individual canopies of over-storey trees with small footprint lidar and discrimination of tree architectural types with waveform distributions is possible and would provide scientists with a new method to study tropical forest structure. Combined spectral and lidar data can be used to identify selected tree species and identify the successional status of tropical forest fragments in order to rank forest patches by levels of species richness. It should be possible in the near future to quantify selected patterns of tropical forests at a higher resolution than can currently be undertaken in the field or from space.     

An airborne lidar sampling strategy to model forest canopy height from Quickbird imagery and GEOBIA

High-resolution digital canopy models derived from airborne lidar data have the ability to provide detailed information on the vertical structure of forests. However, compared to satellite data of similar spatial resolution and extent, the small footprint airborne lidar data required to produce such models remain expensive. In an effort to reduce these costs, the primary objective of this paper is to develop an airborne lidar sampling strategy to model full-scene forest canopy height from optical imagery, lidar transects and Geographic Object-Based Image Analysis (GEOBIA). To achieve this goal, this research focuses on (i) determining appropriate lidar transect features (i.e., location, direction and extent) from an optical scene, (ii) developing a mechanism to model forest canopy height for the full-scene based on a minimum number of lidar transects, and (iii) defining an optimal mean object size (MOS) to accurately model the canopy composition and height distribution. Results show that (i) the transect locations derived from our optimal lidar transect selection algorithm accurately capture the canopy height variability of the entire study area; (ii) our canopy height estimation models have similar performance in two lidar transect directions (i.e., north-south and west-east); (iii) a small lidar extent (17.6% of total size) can achieve similar canopy height estimation accuracies as those modeled from the full lidar scene; and (iv) different MOS can lead to distinctly different canopy height results. By comparing the best canopy height estimate with the full lidar canopy height data, we obtained average estimation errors of 6.0 m and 6.8 m for conifer and deciduous forests at the individual tree crown/small tree cluster level, and an area weighted combined error of 6.2 m, which is lower than the provincial forest inventory height class interval (i.e., ≈ 9.0 m).     

Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data

Wildfire is an important disturbance agent in Canada's boreal forest. Optical remotely sensed imagery (e.g., Landsat TM/ETM+), is well suited for capturing horizontally distributed forest conditions, structure, and change, while Light Detection and Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to characterize post-fire conditions. The objective of this study is to compare changes in forest structure, as measured with a discrete return profiling LIDAR, to post-fire conditions, as measured with remotely sensed data. Our research is focused on a boreal forest fire that occurred in May 2002 in Alberta, Canada. The Normalized Burn Ratio (NBR), the differenced NBR (dNBR), and the relative dNBR (RdNBR) were calculated from two dates of Landsat data (August 2001 and September 2002). Forest structural attributes were derived from two spatially coincident discrete return LIDAR profiles acquired in September 1997 and 2002 respectively. Image segmentation was used to produce homogeneous spatial patches analogous to forest stands, with analysis conducted at this patch level.In this study area, which was relatively homogenous and dominated by open forest, no statistically significant relationships were found between pre-fire forest structure and post-fire conditions (r < 0.5; p > 0.05). Post-fire forest structure and absolute and relative changes in forest structure were strongly correlated to post-fire conditions (r ranging from − 0.507 to 0.712; p < 0.0001). Measures of vegetation fill (VF) (LIDAR capture of cross-sectional vegetation amount), post-fire and absolute change in crown closure (CC), and relative change in average canopy height, were most useful for characterizing post-fire conditions. Forest structural attributes generated from the post-fire LIDAR data were most strongly correlated to post-fire NBR, while dNBR and RdNBR had stronger correlations with absolute and relative changes in the forest structural attributes. Absolute and relative changes in VF and changes in CC had the strongest positive correlations with respect to dNBR and RdNBR, ranging from 0.514 to 0.715 (p < 0.05). Measures of average inter-tree distance and volume were not strongly correlated to post-fire NBR, dNBR, or RdNBR. No marked differences were found in the strength or significance of correlations between post-fire structure and the post-fire NBR, dNBR, RdNBR, indicating that for the conditions present in this study area all three burn severity indices captured post-fire conditions in a similar manner. Finally, the relationship between post-fire forest structure and post-fire condition was strongest for dense forests (> 60% crown closure) compared to open (26-60%) and sparse forests (10-25%). Forest structure information provided by LIDAR is useful for characterizing post-fire conditions and burn induced structural change, and will complement other attributes such as vegetation type and moisture, topography, and long-term weather patterns, all of which will also influence variations in post-fire conditions.     

Estimation of Forest Aboveground Biomass and Canopy Heightin Jiangxi Province Using GLAS and Landsat TM Images

Aiming at the problem that optical remote sensing cannot estimate forest biomass exactly because it’s easily affected by the weather and hard to penetrate the canopy of the forest.Using Jiangxi forest as the study area,established forest canopy height and forest biomass model by GLAS waveform data,integrating multispectral data(TM) and filed survey data.The study results show:(1) using waveform feature parameter,terrain feature parameters and field survey data to build forest canopy height model can eliminate the terrain influence and obtain the discrete canopy height.(2) Combined with the NDVI and discrete canopy height can be carried out large scale continuous forest canopy height mapping.(3) Power function relationship between canopy height and forest biomass can be used to estimate forest biomass.In general,large\|footprint LiDAR combined with optical Landsat TM data can give full paly to the advantages of multi\|source remote sensing and improve the precision of forest biomass inversion.     

Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

基于ICESat-GLAS数据估算复杂地形区域森林蓄积量潜力初探-以云南香格里拉县为例

近年来ICESat\|GLAS波形数据被广泛地应用于森林生态参数的估算。为了研究大光斑激光雷达数据在复杂地形区域估算森林蓄积量方面的能力,以云南省香格里拉县为研究区域,将GLA01数据处理后得到的平均树高与实测树高及坡度进行对比,探究了坡度对GLAS数据估算平均树高的影响,同时将其与平均树高、光斑范围内森林蓄积量建立关系,初步研究三者之间的关系。结果表明,坡度会降低大光斑激光雷达数据估算森林植被高度的精度,但GLAS数据估算出的树高与实测的平均树高、蓄积量数据仍有较好的相关性,这说明利用GLAS数据估算森林蓄积量有较大的潜力。     

Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry

Forest canopy height is a critical parameter in better quantifying the terrestrial carbon cycle. It can be used to estimate aboveground biomass and carbon pools stored in the vegetation, and predict timber yield for forest management. Polarimetric SAR interferometry (PolInSAR) uses polarimetric separation of scattering phase centers derived from interferometry to estimate canopy height. A limitation of PolInSAR is that it relies on sufficient scattering phase center separation at each pixel to be able to derive accurate forest canopy height estimates. The effect of wavelength-dependent penetration depth into the canopy is known to be strong, and could potentially lead to a better height separation than relying on polarization combinations at one wavelength alone. Here we present a new method for canopy height mapping using dual-wavelength SAR interferometry (InSAR) at X- and L-band. The method is based on the scattering phase center separation at different wavelengths. It involves the generation of a smoothed interpolated terrain elevation model underneath the forest canopy from repeat-pass L-band InSAR data. The terrain model is then used to remove the terrain component from the single-pass X-band interferometric surface height to estimate forest canopy height. The ability of L-band to map terrain height under vegetation relies on sufficient spatial heterogeneity of the density of scattering elements that scatter L-band electromagnetic waves within each resolution cell. The method is demonstrated with airborne X-band VV polarized single-pass and L-band HH polarized repeat-pass SAR interferometry using data acquired by the E-SAR sensor over Monks Wood National Nature Reserve, UK. This is one of the first radar studies of a semi-natural deciduous woodland that exhibits considerable spatial heterogeneity of vegetation type and density. The canopy height model is validated using airborne imaging LIDAR data acquired by the Environment Agency. The rmse of the LIDAR canopy height estimates compared to theodolite data is 2.15 m (relative error 17.6%). The rmse of the dual-wavelength InSAR-derived canopy height model compared to LIDAR is 3.49 m (relative error 28.5%). From the canopy height maps carbon pools are estimated using allometric equations. The results are compared to a field survey of carbon pools and rmse values are presented. The dual-wavelength InSAR method could potentially be delivered from a spaceborne constellation similar to the TerraSAR system.     

Identification of gaps in mangrove forests with airborne LIDAR

Mangrove forests change frequently due to disturbances from tropical storms, frost, lightning, and insects. It has been suggested that the death and regeneration of trees in small gaps due to lightning may play a critical role in mangrove forest turnover; however, the large-scale quantification of spatial pattern and areas of gaps is lacking for investigating this issue. Airborne light detection and ranging (LIDAR) technology provides an effective way for identifying gaps by remotely obtaining direct measurements of ground and canopy elevations. A method based on an alternative sequential filter and black top-hat mathematical morphological transformation was developed to extract gap features. Comparison of identified gap polygons with raw LIDAR measurements and field surveys shows that the proposed method successfully extracted gap features in mangrove forests in Everglades National Park. There are 400–500 lightning gaps per square kilometer in mangrove forests at the study sites. The distribution of gap sizes follows an exponential form and the area of gaps with sizes larger than 100 m² account for 55–61% of the total area of gaps. The area of gaps in the mangrove forest in Everglades National Park is about 4–5% of the total forest area and the average gap formation rate is about 0.3% of the total forest area per year, indicating that lightning gaps play an important role in mangrove forest dynamics.     

Assessment of tropical forest degradation by selective logging and fire using Landsat imagery

Many studies have assessed the process of forest degradation in the Brazilian Amazon using remote sensing approaches to estimate the extent and impact by selective logging and forest fires on tropical rain forest. However, only a few have estimated the combined impacts of those anthropogenic activities. We conducted a detailed analysis of selective logging and forest fire impacts on natural forests in the southern Brazilian Amazon state of Mato Grosso, one of the key logging centers in the country. To achieve this goal a 13-year series of annual Landsat images (1992-2004) was used to test different remote sensing techniques for measuring the extent of selective logging and forest fires, and to estimate their impact and interaction with other land use types occurring in the study region. Forest canopy regeneration following these disturbances was also assessed. Field measurements and visual observations were conducted to validate remote sensing techniques. Our results indicated that the Modified Soil Adjusted Vegetation Index aerosol free (MSAVI_af) is a reliable estimator of fractional coverage under both clear sky and under smoky conditions in this study region. During the period of analysis, selective logging was responsible for disturbing the largest proportion (31%) of natural forest in the study area, immediately followed by deforestation (29%). Altogether, forest disturbances by selective logging and forest fires affected approximately 40% of the study site area. Once disturbed by selective logging activities, forests became more susceptible to fire in the study site. However, our results showed that fires may also occur in undisturbed forests. This indicates that there are further factors that may increase forest fire susceptibility in the study area. Those factors need to be better understood. Although selective logging affected the largest amount of natural forest in the study period, 35% and 28% of the observed losses of forest canopy cover were due to forest fire and selective logging combined and to forest fire only, respectively. Moreover, forest areas degraded by selective logging and forest fire is an addition to outright deforestation estimates and has yet to be accounted for by land use and land cover change assessments in tropical regions. Assuming that this observed trend of land use and land cover conversion continues, we predict that there will be no undisturbed forests remaining by 2011 in this study site. Finally, we estimated that 70% of the total forest area disturbed by logging and fire had sufficiently recovered to become undetectable using satellite data in 2004.     

Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization

Forest dynamics are characterized by both continuous (i.e., growth) and discontinuous (i.e., disturbance) changes. Change detection techniques that use optical remotely sensed data to capture disturbance related changes are established and commonly applied; however, approaches for the capture of continuous forest changes are less mature. Optical remotely sensed imagery is well suited for capturing horizontally distributed conditions, structures, and changes, while Light Detection And Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to fully characterize forest canopy attributes and dynamics.The study described in this paper captures forest conditions along a corridor approximately 600 km long through the boreal forest of Canada. Two coincident LIDAR transects, representing 1997 and 2002 forest conditions respectively, are compared using image segments generated from Landsat ETM+ imagery. The image segments are used to provide a spatial framework within which the attributes and temporal dynamics of the forest canopy are estimated and compared. Segmented and classified Landsat imagery provides a context for the comparison of sufficiently spatially related LIDAR profiles and for the provision of categories to aid in the application of empirical models requiring knowledge of land cover.Global and local approaches were employed for characterizing changes in forest attributes over time. The global approach, emphasized the overall trend in forest change along the length of the entire transect, and indicated that key canopy attributes were stable, and transect characteristics, including forest canopy height, did not change significantly over the five-year period of this study (two sample t-test, p = 0.08). The local approach analyzed segment-based changes in canopy attributes, providing spatially explicit indications of forest growth and depletion. The local approach identified that 84% of the Landsat segments intercepted by both LIDAR transects either have no change, or have a small average increase in canopy height (0.7 m), while the other 16% of segments have an average decrease in canopy height of 1.6 m. As expected, the difference in the magnitude of the changes was markedly greater for depletions than it was for growth, but was less spatially extensive. Growth tends to occur incrementally over broad areas; whereas, depletions are dramatic and spatially constrained. The approach presented holds potential for investigating the impacts of climate change across a latitudinal gradient of boreal forest.     

Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA

An inversion of linked radiative transfer models (RTM) through artificial neural networks (ANN) was applied to MODIS data to retrieve vegetation canopy water content (CWC). The estimates were calibrated and validated using water retrievals from AVIRIS data from study sites located around the United States that included a wide range of environmental conditions. The ANN algorithm showed good performance across different vegetation types, with high correlations and consistent determination coefficients. The approach outperformed a multiple linear regression approach used to independently retrieve the same variable. The calibrated algorithm was then applied at the MODIS 500 m scale to follow changes in CWC for the year 2005 across the continental United States, subdivided into three vegetation types (grassland, shrubland, and forest). The ANN estimates of CWC correlated well with rainfall, indicating a strong ecological response. The high correlations suggest that the inversion of RTM through an ANN provide a realistic basis for multi-temporal assessments of CWC over wide areas for continental and global studies.     

Assessment of forest structure with airborne LiDAR and the effects of platform altitude

Airborne scanning LiDAR is a spatial technology increasingly used for forestry and environmental applications. However, the accuracy and coverage of LiDAR observations is highly dependent on both the extrinsic specifications of the LiDAR survey as well as the intrinsic effects such as the underlying forest structure. Extrinsic parameters which are set as part of the LiDAR survey include platform altitude, scan angle (half max. angle off nadir), and beam cross sectional diameter at the reflecting surface (referred to as footprint size). In this paper we investigate the effect of a number of these extrinsic parameters, including three different platform altitudes (1000, 2000, and 3000 m), two scan angles at 1000 m (10° and 15° half max. angle off nadir), and three footprint sizes (0.2, 0.4, and 0.6 m). The comparison was undertaken in eucalypt forests at three sites, varying in vegetation structure and topography within the Wedding Bells State Forest, Coffs Harbour, Australia. Results at the plot scale (40 × 90 m areas) indicate that tree heights computed from the 1000 m LiDAR data set (10° half max. angle off nadir) are well correlated with maximum plot heights (difference < 3 m) and field measured canopy volume (r² > 0.75, p < 0.001). Using normalised canopy height profiles (CHP) derived for sites, from data recorded at each altitude, we observed no significant difference between the relative distribution of LiDAR returns, indicating that platform altitude and footprint size have not had a major influence on CHP estimation. Interestingly, comparisons of first and last returns for individual pulses at increasing altitudes identified progressively fewer discrete first/last pulse combinations with more than 70% of pulses recorded as a single return at the highest altitude (3000 m). A possible hypothesis is that greater platform altitude and footprint size reduces the intensity of laser beam incident on a given surface area thus decreasing the probability of recording a last return above the noise threshold. Furthermore, tree scale analysis found a positive relationship between platform altitude and the underestimation of crown area and crown volume. The implications of this work for forest management are: (i) platform altitudes as high as 3000 m can be used to quantify the vertical distribution of phyto-elements, (ii) higher platform altitudes record a lower proportion of first/last return combinations that will further reduce the number of points available for forest structural assessment and development of digital elevation models, and (iii) for discrete LiDAR data, increasing platform altitude will record a lower frequency of returns per crown, resulting in larger underestimates of individual tree crown area and volume if standard algorithms are applied.     

An evaluation of imaging spectrometry for estimating forest canopy chemistry

Abstract

High spectral resolution Airborne Imaging Spectrometer (AIS) data were acquired over 20 well-studied Wisconsin forest sites to evaluate the potential of remote sensing for estimating forest canopy chemistry. Intensive nutrient cycling research in these forests demonstrates that canopy lignin content is strongly related to measured annual nitrogen mineralization at the undisturbed sites and may serve as an accurate index for nitrogen cycling rates. Ground measurements were made of foliar biomass and canopy nitrogen and lignin content, the latter within two weeks of the AIS overflight. The spectral data were transformed using derivative techniques modified from laboratory spectroscopy. Stepwise regression assisted in determining combinations of wavelengths most highly correlated with canopy chemistry and biomass. Strong correlations between AIS data and total canopy lignin content in deciduous forests and canopy lignin concentration (total lignin/biomass) in both deciduous and coniferous stands indicate that imaging spectrometry can be used to estimate canopy lignin content and, from that, the spatial distribution of annual nitrogen mineralization rates.     

Inversion of a coupled canopy-atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study

Since the launch of sensors with angular observation capabilities, such as CHRIS and MISR, the additional potential of multi-angular observations for vegetation structural and biochemical variables has been widely recognised. Various methods have been successfully implemented to estimate forest biochemical and biophysical variables from atmospherically-corrected multi-angular data, but the use of physically based radiative transfer (RT) models is still limited. Because both canopy and atmosphere have an anisotropic behaviour, it is important to understand the multi-angular signal measured by the sensor at the top of the atmosphere (TOA). Coupled canopy-atmosphere RT models allow linking surface variables directly to the TOA radiance measured by the sensor and are therefore very interesting tools to use for estimating forest variables from multi-angular data.We investigated the potential of TOA multi-angular radiance data for estimating forest variables by inverting a coupled canopy-atmosphere physical RT model. The case study focussed on three Norway spruce stands located at the Bily Kriz experimental site (Czech Republic), for which multi-angular CHRIS and field data were acquired in September 2006. The soil-leaf-canopy RT model SLC and the atmospheric model MODTRAN4 were coupled using a method allowing to make full use of the four canopy angular reflectance components provided by SLC. The TOA radiance simulations were in good agreement with the spectral and angular signatures measured by CHRIS. Singular value decompositions of the Jacobian matrices showed that the dimensionality of the variable estimation problem increased from 3 to 6 when increasing the number of observation angles from 1 to 4. The model inversion was conducted for two cases: 4 and 7 variables. The most influential parameters were chosen as free variables in the look-up tables, namely: vertical crown cover (Cv), fraction of bark material (fB), needle chlorophyll content (needleCab), needle dry matter content (needleCdm) for the 4-variable case, and additionally, tree shape factor (Zeta), dissociation factor (D), and needle brown pigments content (needleCs) in the 7-variable case. All angular combinations were tested, and the best estimates were obtained with combinations using two or three angles, depending on the number of variables and on the stand used. Overall, this case study showed that, although making use of its full potential is still a challenge, TOA multi-angular radiance data do have a higher potential for variable estimation than mono-angular data.     

Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data

In this study we use the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product to develop multivariate linear regression models that estimate canopy heights over study sites at Howland Forest, Maine, Harvard Forest, Massachusetts and La Selva Forest, Costa Rica using (1) directional escape probabilities that are spectrally independent and (2) the directional spectral reflectances used to derive the directional escape probabilities. These measures of canopy architecture are compared with canopy height information retrieved from the airborne Laser Vegetation Imaging Sensor (LVIS). Both the escape probability and the directional reflectance approaches achieve good results, with correlation coefficients in the range 0.54-0.82, although escape probability results are usually slightly better. This suggests that MODIS 500 m BRDF data can be used to extrapolate canopy heights observed by widely-spaced satellite LIDAR swaths to larger areas, thus providing wide-area coverage of canopy height.     

Quantifying forest above ground carbon content using LiDAR remote sensing

The UNFCCC and interest in the source of the missing terrestrial carbon sink are prompting research and development into methods for carbon accounting in forest ecosystems. Here we present a canopy height quantile-based approach for quantifying above ground carbon content (AGCC) in a temperate deciduous woodland, by means of a discrete-return, small-footprint airborne LiDAR. Fieldwork was conducted in Monks Wood National Nature Reserve UK to estimate the AGCC of five stands from forest mensuration and allometric relations. In parallel, a digital canopy height model (DCHM) and a digital terrain model (DTM) were derived from elevation measurements obtained by means of an Optech Airborne Laser Terrain Mapper 1210. A quantile-based approach was adopted to select a representative statistic of height distributions per plot. A forestry yield model was selected as a basis to estimate stemwood volume per plot from these heights metrics. Agreement of r=0.74 at the plot level was achieved between ground-based AGCC estimates and those derived from the DCHM. Using a 20×20 m grids superposed to the DCHM, the AGCC was estimated at the stand level and at the woodland level. At the stand level, the agreement between the plot data upscaled in proportion to area and the LiDAR estimates was r=0.85. At the woodland level, LiDAR estimates were nearly 24% lower than those from the upscaled plot data. This suggests that field-based approaches alone may not be adequate for carbon accounting in heterogeneous forests. Conversely, the LiDAR 20×20 m grid approach has an enhanced capability of monitoring the natural variability of AGCC across the woodland.     

Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazônia

Secondary forests may become increasingly important as temporary reservoirs of genetic diversity, stocks of carbon and nutrients, and moderators of hydrologic cycles in the Amazon Basin as agricultural lands are abandoned and often later cleared again for agriculture. We studied a municipality in northeastern Pará, Brazil, that has been settled for over a century and where numerous cycles of slash and burn agriculture have occurred. The forests were grouped into young (3-6 years), intermediate (10-20 years), advanced (40-70 years), and mature successional stages using 1999 Landsat 7 ETM imagery. Supervised classification of the imagery showed that these forest classes occupied 22%, 13%, 9%, and 6% of the area, respectively. Although this area underwent widespread deforestation many decades ago, forest of some type covers about 50% of the area. Row crops, tree crops, and pastures cover 8%, 20%, and 22%, respectively. The best separation among land covers appeared in a plot of NDVI versus band 5 reflectance. The same groupings of successional forests were derived independently from indices of similarity among tree species composition. Measured distributions of tree height and diameter also covaried with these successional classes, with the young forests having nearly uniform distributions, whereas multiple height and diameter classes were present in the advanced successional forests. Biomass accumulated more slowly in this secondary forest chronosequence than has been reported for other areas, which explains why the 70-year-old forests here were still distinguishable from mature forests using spectral properties. Rates of forest regrowth may vary across regions due to differences in edaphic, climatic, and historical land-use factors, thus rendering most relationships among spectral properties and forest age site-specific. Successional status, as characterized by species composition, biomass, and distributions of heights and diameters, may be superior to stand age as a means of stratifying these forests for characterization of spectral properties.     

Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification

This study evaluated the synergistic use of high spatial resolution multispectral imagery (i.e., QuickBird, 2.4 m) and low-posting-density LIDAR data (3 m) for forest species classification using an object-based approach. The integration of QuickBird multispectral imagery and LIDAR data was considered during image segmentation and the subsequent object-based classification. Three segmentation schemes were examined: (1) segmentation based solely on the spectral image layers; (2) segmentation based solely on LIDAR-derived layers; and (3) segmentation based on both the spectral and LIDAR-derived layers. For each segmentation scheme, objects were generated at twelve different scales in order to determine optimal scale parameters. Six categories of classification metrics were generated for each object based on spectral data alone, LIDAR data alone and the combination of both data sources. Machine learning decision trees were used to build classification rule sets. Quantitative segmentation quality assessment and classification accuracy results showed the integration of spectral and LIDAR data, in both image segmentation and object-based classification, improved the forest classification compared to using either data source independently. Better segmentation quality led to higher classification accuracy. The highest classification accuracy (Kappa = 91.6%) was acquired when using both spectral- and LIDAR-derived metrics based on objects segmented from both spectral and LIDAR layers at scale parameter 250, where best segmentation quality was achieved. Optimal scales were analyzed for each segmentation-classification scheme. Statistical analysis of classification accuracies at different scales revealed that there was a range of optimal scales that provided statistically similar accuracy.