Michael A. Lefsky

Lidar Remote Sensing for Ecosystem Studies

Articles R emote sensing has facilitated extraordinary advances in the modeling, mapping, and understanding of ecosystems. Typical applications of remote sensing involve either images from passive optical systems, such as aerial photography and Landsat Thematic Mapper (Goward and Williams 1997), or to a lesser degree, active radar sensors such as RADARSAT (Waring et al. 1995). These types of sensors have proven to be satisfactory for many ecological applications , such as mapping land cover into broad classes and, in some biomes, estimating aboveground biomass and leaf area index (LAI). Moreover, they enable researchers to analyze the spatial pattern of these images. However, conventional sensors have significant limitations for ecological applications. The sensitivity and accuracy of these devices have repeatedly been shown to fall with increasing aboveground biomass and leaf area index (Waring et al. 1995, Carlson and Ripley 1997, Turner et al. 1999). They are also limited in their ability to represent spatial patterns: They produce only two-dimensional (x and y) images, which cannot fully represent the three-dimensional structure of, for instance, an old-growth forest canopy.Yet ecologists have long understood that the presence of specific organisms, and the overall richness of wildlife communities, can be highly dependent on the three-dimensional spatial pattern of vegetation (MacArthur and MacArthur 1961), especially in systems where biomass accumulation is significant (Hansen and Rotella 2000). Individual bird species, in particular, are often associated with specific three-dimensional features in forests (Carey et al. 1991). In addition, other functional aspects of forests, such as productivity, may be related to forest canopy structure. Laser altimetry, or lidar (light detection and ranging), is an alternative remote sensing technology that promises to both increase the accuracy of biophysical measurements and extend spatial analysis into the third (z) dimension. Lidar sensors directly measure the three-dimensional distribution of plant canopies as well as subcanopy topography, thus providing high-resolution topographic maps and highly accurate estimates of vegetation height, cover, and canopy structure. In addition , lidar has been shown to accurately estimate LAI and aboveground biomass even in those high-biomass ecosystems where passive optical and active radar sensors typically fail to do so. The basic measurement made by a lidar device is the distance between the sensor and a target surface, obtained by determining the elapsed time between the emission of a short-duration laser pulse and the arrival of the reflection of that pulse (the return signal) at the sensors receiver. Multiplying this …

Benchmark map of forest carbon stocks in tropical regions across three continents

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a “benchmark” map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ±5% and ca. ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

Estimates of forest canopy height and aboveground biomass using ICESat

Exchange of carbon between forests and the atmosphere is a vital component of the global carbon cycle. Satellite laser altimetry has a unique capability for estimating forest canopy height, which has a direct and increasingly well understood relationship to aboveground carbon storage. While the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud and land Elevation Satellite (ICESat) has collected an unparalleled dataset of lidar waveforms over terrestrial targets, processing of ICESat data to estimate forest height is complicated by the pulse broadening associated with large-footprint, waveform-sampling lidar. We combined ICESat waveforms and ancillary topography from the Shuttle Radar Topography Mission to estimate maximum forest height in three ecosystems; tropical broadleaf forests in Brazil, temperate broadleaf forests in Tennessee, and temperate needleleaf forests in Oregon. Final models for each site explained between 59% and 68% of variance in field-measured forest canopy height (RMSE between 4.85 and 12.66 m). In addition, ICESat-derived heights for the Brazilian plots were correlated with field-estimates of aboveground biomass (r(2) = 73%, RMSE = 58.3 Mgha(-1)).

Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height

Light detection and ranging (lidar) data provide accurate measurements of forest canopy structure in the vertical plane; however, current lidar sensors have limited coverage in the horizontal plane. Landsat data provide extensive coverage of generalized forest structural classes in the horizontal plane but are relatively insensitive to variation in forest canopy height. It would, therefore, be desirable to integrate lidar and Landsat data to improve the measurement, mapping, and monitoring of forest structural attributes. We tested five aspatial and spatial methods for predicting canopy height, using an airborne lidar system (Aeroscan) and Landsat Enhanced Thematic Mapper (ETM+) data: regression, kriging, cokriging, and kriging and cokriging of regression residuals. Our 200-km 2 study area in western Oregon encompassed Oregon State University’s McDonald–Dunn Research Forest, which is broadly representative of the age and structural classes common in the region. We sampled a spatially continuous lidar coverage in eight systematic patterns to determine which lidar sampling strategy would optimize lidar– Landsat integration in western Oregon forests: transects sampled at 2000, 1000, 500, and 250 m frequencies, and points sampled at these same spatial frequencies. The aspatial regression model results, regardless of sampling strategy, preserved actual vegetation pattern, but underestimated taller canopies and overestimated shorter canopies. The spatial models, kriging and cokriging, produced less biased results than regression but poorly reproduced vegetation pattern, especially at the sparser (2000 and 1000 m) sampling frequencies. The spatial model predictions were more accurate than the regression model predictions at locations <200 m from sample locations. Cokriging, using the ETM+ panchromatic band as the secondary variable, proved slightly more accurate than kriging. The integrated models that kriged or cokriged regression residuals were preferable to either the aspatial or spatial models alone because they preserved the vegetation pattern like regression yet improved estimation accuracies above those predicted from the regression models alone. The 250-m point sampling strategy proved most optimal because it oversampled the landscape relative to the geostatistical range of actual spatial variation, as indicated by the sample semivariograms, while making the sample data volume more manageable. We concluded that an integrated modeling strategy is most suitable for estimating and mapping canopy height at locations unsampled by lidar, and that a 250-m discrete point sampling strategy most efficiently samples an intensively managed forested landscape in western Oregon. D 2002 Published by Elsevier Science Inc.

Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms

The vertical extent of waveforms collected by the Geoscience Laser Altimeter System (onboard ICESat - the Ice, Cloud, and land Elevation Satellite) increases as a function of terrain slope and footprint size (the area on the ground that is illuminated by the laser). Over sloped terrain, returns from both canopy and ground surfaces can occur at the same elevation. As a result, the height of the waveform (waveform extent) is insufficient to make estimates of tree height on sloped terrain, and algorithms are needed that are capable of retrieving information about terrain slope from the waveform itself. Early work on this problem used a combination of waveform height indices and slope indices from a digital elevation model (DEM). A second generation algorithm was developed using datasets from diverse forests in which forest canopy height has been estimated in the field or by via airborne lidar. Forest types considered in this paper include evergreen needleleaf, deciduous broadleaf and mixed stands in temperate North America, and tropical evergreen broadleaf forests in Brazil. The algorithm described eliminates the need for a DEM, and estimates forest canopy height with an RMSE of 5 m (83% of variance in forest canopy height explained).

The ICESat-2 Laser Altimetry Mission

Satellite and aircraft observations have revealed that remarkable changes in the Earths polar ice cover have occurred in the last decade. The impacts of these changes, which include dramatic ice loss from ice sheets and rapid declines in Arctic sea ice, could be quite large in terms of sea level rise and global climate. NASAs Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), currently planned for launch in 2015, is specifically intended to quantify the amount of change in ice sheets and sea ice and provide key insights into their behavior. It will achieve these objectives through the use of precise laser measurements of surface elevation, building on the groundbreaking capabilities of its predecessor, the Ice Cloud and Land Elevation Satellite (ICESat). In particular, ICESat-2 will measure the temporal and spatial character of ice sheet elevation change to enable assessment of ice sheet mass balance and examination of the underlying mechanisms that control it. The precision of ICESat-2s elevation measurement will also allow for accurate measurements of sea ice freeboard height, from which sea ice thickness and its temporal changes can be estimated. ICESat-2 will provide important information on other components of the Earth System as well, most notably large-scale vegetation biomass estimates through the measurement of vegetation canopy height. When combined with the original ICESat observations, ICESat-2 will provide ice change measurements across more than a 15-year time span. Its significantly improved laser system will also provide observations with much greater spatial resolution, temporal resolution, and accuracy than has ever been possible before.

Three-dimensional Structure of an Old-growth Pseudotsuga-Tsuga Canopy and Its Implications for Radiation Balance, Microclimate, and Gas Exchange

We describe the three-dimensional structure of an old-growth Douglas-fir/western hemlock forest in the central Cascades of southern Washington, USA. We concentrate on the vertical distribution of foliage, crowns, external surface area, wood biomass, and several components of canopy volume. In addition, we estimate the spatial variation of some aspects of structure, including the topography of the outer surface, and of microclimate, including the within-canopy transmittance of photosynthetically active radiation (PAR). The crowns of large stems, especially of Douglas-fir, dominate the structure and many aspects of spatial variation. The mean vertical profile of canopy surfaces, estimated by five methods, generally showed a single maximum in the lower to middle third of the canopy, although the height of that maximum varied by method. The stand leaf area index was around 9 m2 m−2, but also varied according to method (from 6.3 to 12.3). Because of the deep narrow crowns and numerous gaps, the outer canopy surface is extremely complex, with a surface area more than 12 times that of the ground below. The large volume included below the outer canopy surface is very porous, with spaces of several qualitatively distinct environments. Our measurements are consistent with emerging concepts about the structure of old-growth forests, where a high degree of complexity is generated by diverse structural features. These structural characteristics have implications for various ecosystem functions. The height and large volume of the stand indicate a large storage component for microclimatic variables. The high biomass influences the dynamics of those variables, retarding rates of change. The complexity of the canopy outer surface influences radiation balance, particularly in reducing short-wave reflectance. The bottom-heaviness of the foliage profile indicates much radiation absorption and gas exchange activity in the lower canopy. The high porosity contributes to flat gradients of most microclimate variables. Most stand respiration occurs within the canopy and is distributed over a broad vertical range.

Light transmittance in forest canopies determined using airborne laser altimetry and in-canopy quantum measurements

The vertical distribution of light transmittance was derived from field and laser altimeter observations taken in the same canopies of five forests of several ages (young to mature) and canopy types (eastern broadleaved and western tall conifer). Vertical transmittances were derived remotely from the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) laser altimeter and in the field from measurements of Photosynthetically Active Radiation (PAR) made within the canopy using quantum sensors suspended from the gondola of a tower crane or atop small balloons. Derived numerical characteristics of mean transmittance profiles (the rate of attenuation, whole canopy transmittance, and the radiation-effective height) were similar for both methods across the sites. Measures of the variance and skewness of transmittance also showed similar patterns for corresponding heights between methods. The two methods exhibited greater correspondence in the eastern stands than in the western ones; differences in the interaction between canopy organization and the sensor characteristics between the stand types might explain this. The narrower, more isolated crowns of the western stands permit a deeper penetration into the canopy of nadir-directed laser light than of direct solar radiation from typical elevation angles. Transects of light transmittance in two stands demonstrate that the SLICER sensor can capture meaningful functional variation. Additionally, for one stand with numerous overlapping transects we constructed a three-dimensional view of the transmittance field. Using geostatistics, we demonstrated that the spatial covariance measured in the horizontal plane varied as a function of height. These results suggest a means to remotely assess an important functional characteristic of vegetation, providing a capacity for process-based ecological studies at large scales.

Selection of Remotely Sensed Data

An increasing number of sensors are available for forest ecologists and managers seeking to map attributes of forest canopy cover, forest structure and composition, and their dynamics. This Chapter seeks to put these advances within the context of the needs of forest managers and scientists. To do so, we review the basic physics behind a variety of imagery types, discuss fundamental limitations and trade-offs that apply to all remotely sensed data, review sensor options for several established and emerging technologies, and present our approach for matching imagery and attributes of interest.

Validation of the ICEsat vegetation product using crown-area-weighted mean height derived using crown delineation with discrete return lidar data

The Geoscience Laser Altimeter System (GLAS), a spaceborne light detection and ranging (lidar) sensor, has acquired over 250 million lidar observations over forests globally, an unprecedented dataset of vegetation height information. To be useful, GLAS must be calibrated to measurements of height used in forestry inventory and ecology. Airborne discrete return lidar (DRL) can characterize vegetation and terrain surfaces in detail, but its utility as calibration data for GLAS is limited by the lack of a direct relationship between the canopy height measurements collected by airborne and spaceborne lidar systems and coincident field data. We demonstrate that it is possible to use DRL to directly estimate the crown-area-weighted mean height (Hcw), which is conceptually and quantitatively similar to the Lorey’s height, which is calculated from forest inventory data, and can be used to calibrate GLAS without the use of field data. For a dataset from five sites in western North America, the two indices of height (Hcw from DRL and Lorey’s from forest inventory) are directly related (r2 = 0.76; RMSE of 3.8 m; intercept and slope of 0.8 m and 0.98, respectively). We derived a relationship between the DRL-derived Hcw and height information from coincident GLAS waveforms; the resulting equation explained 69% of variance, with an RMSE of 6.2 m.