Jacqueline Rosette

Amazon forests maintain consistent canopy structure and greenness during the dry season

The seasonality of sunlight and rainfall regulates net primary production in tropical forests. Previous studies have suggested that light is more limiting than water for tropical forest productivity, consistent with greening of Amazon forests during the dry season in satellite data. We evaluated four potential mechanisms for the seasonal green-up phenomenon, including increases in leaf area or leaf reflectance, using a sophisticated radiative transfer model and independent satellite observations from lidar and optical sensors. Here we show that the apparent green up of Amazon forests in optical remote sensing data resulted from seasonal changes in near-infrared reflectance, an artefact of variations in sun-sensor geometry. Correcting this bidirectional reflectance effect eliminated seasonal changes in surface reflectance, consistent with independent lidar observations and model simulations with unchanging canopy properties. The stability of Amazon forest structure and reflectance over seasonal timescales challenges the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions. Correcting optical remote sensing data for artefacts of sun-sensor geometry is essential to isolate the response of global vegetation to seasonal and interannual climate variability.

Vegetation height estimates for a mixed temperate forest using satellite laser altimetry

Data from the Geoscience Laser Altimeter System (GLAS) aboard the Ice Cloud and land Elevation Satellite (ICESat) offer an unprecedented opportunity for canopy height retrieval at a regional to global scale. The data also provide useful information for forest stand level assessment at coincident locations. In this study height indices from light detection and ranging (LiDAR) waveforms were explored as a means of extracting canopy height; these were examined with reference to a mixed temperate forest in Gloucestershire, UK, containing planted stands with a mean age of 51 years and mean maximum height of 26.6 m. A method based on using a terrain index (TI; maximum minus minimum elevations from a 7×7 subset 10‐m resolution digital terrain model (DTM)) to adjust the waveform extent (WE; signal begin minus signal end) produced an R 2 value of 0.89 when regressed against field measurements of maximum canopy height at footprint locations (field height = 0.91(WE−TI)+4.86; root mean squared error (RMSE) = 2.99 m, coefficient significance p<0.001, intercept significance p>0.01). Multiple regression performed on both WE and TI with field measurements produced an R 2 of 0.90 and an RMSE of 2.86 m (field height = 1.0208WE−0.7310TI; coefficient significance p<0.001, intercept not significant). Maximum canopy height estimates using an automated approach to ground return identification based on iterative fitting of Gaussian peaks (GP1_2MAXAMP) to the waveform explained 74% of variance when compared to field measurements (field height = 1.05(GP1_2MAXAMP); RMSE = 4.53 m, coefficient significance p<0.001, intercept not significant). The ability of satellite LiDAR to retrieve data for such a complex and diverse area further indicates the potential of this technique for both carbon accounting and forest management.

A Monte Carlo radiative transfer model of satellite waveform LiDAR

We present a method and results for a model of the interaction of waveform Light Detection And Ranging (LiDAR) with a three-dimensional vegetation canopy. The model is developed from the FLIGHT radiative transfer model based on Monte Carlo simulations of photon transport. Foliage is represented by structural properties of leaf area, leaf-angle distribution, crown dimensions and fractional cover, and the optical properties of leaves, branch, shoot and ground components. The model represents multiple scattering of light within the canopy and with the ground surface, simulates the return signal efficiently at multiple wavebands and includes the effects of topography. LiDAR-emitted pulse and spatial and temporal sampling characteristics of the instrument are explicitly modelled. Agreement is found between the integrated waveform energy and directly derived bidirectional reflectance factors from FLIGHT (root mean square error < 0.01), and between simulated and observed Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) waveforms for a complex forest site. A sensitivity analysis gives expected effects of canopy parameters on the waveform, and indicates potential for retrieval of the canopy properties of fractional cover and leaf area, in addition to height. Where canopy and ground pulses can be separated, an index derived from the waveform shows theoretical retrieval of vertically projected plant area index with correlation coefficient R 2 = 0.87.

Uncertainty within satellite LiDAR estimations of vegetation and topography

This paper demonstrates the ability to identify representative ground elevation and vegetation height estimates within the Ice, Cloud and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) waveforms for an area of mixed vegetation and varied topography. Estimating vegetation height within large-footprint Light Detection and Ranging (LiDAR) waveforms relies on the ability to estimate the uppermost canopy surface (signal beginning) and an elevation representing the ground surface, both of which are influenced by vegetation properties and topographic slope. We examined sources of uncertainty for vegetation height estimation from ICESat/GLAS data using airborne LiDAR data, field measurements and the FLIGHT radiative transfer model. In comparison with an independent 10-m resolution digital terrain model (DTM), a method using Gaussian decomposition of the satellite waveform produced a mean bias of −0.10 m when estimating ground elevation. A second method of estimating vegetation height using waveform extent and a terrain index effectively removed slope as an error source but produced a greater ground surface offset (−0.83 m). The two methods of estimating vegetation height compared well with airborne LiDAR estimates (correlation coefficient (R 2) = 0.68, root mean square error (RMSE) = 4.4 m and R 2 = 0.61, RMSE = 4.9 m, respectively). However, the complex interplay of the structural and optical properties of the intercepted vegetation and slope requires further understanding. A tool such as FLIGHT provides a useful means to explore the sensitivity of the waveform to both vegetation properties and topographic slope.

A comparison of biophysical parameter retrieval for forestry using airborne and satellite LiDAR

This paper compares vegetation height metrics and fractional cover derived from coincident small footprint, discrete return airborne Light Detection and Ranging (LiDAR) scanning data (Optech Airborne Laser Terrain Mapper (ALTM)) with those estimated from large footprint, full waveform LiDAR profiling using the Geoscience Laser Altimeter System (GLAS). Estimates of maximum canopy height showed correspondence between the two methods with R2 = 0.68 (rms. error (RMSE) = 4.4 m). The relationship between 99th percentiles (often associated with forestry top height) showed R2 = 0.75, RMSE = 3.5 m. Detection of surface elevation limits corresponded well, (R2 = 0.71, RMSE = 5.0 m). Correlations between satellite waveform and airborne LiDAR canopy cover estimates gave R2 = 0.41 and R2 = 0.63 for dominant cover of conifers or broadleaf species, respectively. The results suggest that the broad Ice, Cloud and land Elevation Satellite (ICESat)/GLAS footprints can provide estimates of mixed vegetation canopy height which are comparable to those obtained from relatively high density airborne LiDAR data.

Slope Estimation from ICESat/GLAS

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Evaluating Prospects for Improved Forest Parameter Retrieval From Satellite LiDAR Using a Physically-Based Radiative Transfer Model

A space-based full-waveform LiDAR system, optimised for vegetation analysis, offers the opportunity for global biophysical parameter retrieval of the worlds forests. However the conditions under which signals from the ground and vegetation can be detected will vary as a result of sensor specifications, vegetation characteristics and underlying surface properties. This paper demonstrates the utility of a ray tracing radiative transfer model for assessing sensitivity to site-specific conditions (e.g., topography, canopy and ground reflectance) that will improve our ability to estimate structural parameters in forest ecosystems.

Lidar Remote Sensing for Biomass Assessment

Optical remote sensing provides us with a two dimensional representation of land-surface vegetation and its reflectance properties which can be indirectly related to biophysical parameters (e.g. NDVI, LAI, fAPAR, and vegetation cover fraction). However, in our interpretation of the world around us, we use a three-dimensional perspective. The addition of a vertical dimension allows us to gain information to help understand and interpret our surroundings by considering features in the context of their size, volume and spatial relation to each other. In contrast to estimates of vegetation parameters which can be obtained from passive optical data, active lidar remote sensing offers a unique means of directly estimating biophysical parameters using physical interactions of the emitted laser pulse with the vegetation structure being illuminated. This enables the vertical profile of the vegetation canopy to be represented, not only permitting canopy height, metrics and cover to be calculated but also enabling these to be related to other biophysical parameters such as biomass. This chapter provides an overview of this technology, giving examples of how lidar data have been applied for forest biomass assessment at different scales from the perspective of satellite, airborne and terrestrial platforms. The chapter concludes with a discussion of further applications of lidar data and a look to the future towards emerging lidar developments.

Forestry Applications for Satellite Lidar Remote Sensing

This paper presents a method to estimate forest parameters and surface topography from NASAs Geosciences Laser Altimeter System (GLAS). Their potential use as observational inputs to models is demonstrated using a wind-risk model for the UK, ForestGALES. Relative heights above ground were used as biophysical parameter estimators. Top Height was estimated with R 2 = 0.73, RMSE = 4.5 m. Diameter at breast height estimates differed for conifer-dominated stands (R 2 = 0.72, RMSE = 0.07 m) and for stands containing mostly broadleaves (R 2 = 0.41, RMSE = 0.11 m). Ground elevation estimation produced R 2 = 0.997, RMSE = 2.2 m. These three parameters were applied to F orestGALES for stand-level assessment of wind-throw risk. Stability is sensitive to small differences in tree dimensions, and therefore vegetation parameters require greater accuracy than those currently retrievable from GLAS to more reliably determine risk of wind-throw. Future satellite lidar missions such as NASAs DESDynI sensor aim to produce improved vegetation parameter estimation plus greater spatial coverage which would offer more appropriate inputs for forestry models.

Sensor Compatibility for Biomass Change Estimation Using Remote Sensing Data Sets: Part of NASA's Carbon Monitoring System Initiative

Time series of remote sensing data offers the opportunity to predict changes in vegetation extent and to estimate forest parameter change such as biomass. However, as sensors and technology advance, it is important to ensure that estimates obtained from different time periods or using different, but related, instruments are consistent in order to have confidence in detected change. This study compares estimates of biomass from small-footprint discrete-return LiDAR data and medium-footprint full-waveform LiDAR for Howland Experimental Forest, Maine, USA. Data were collected from both sensors during Summer 2009. Similar results were found using the same height metric with R2 = 0.67, SE = 58.5 Mg ha-1 and R2 = 0.52, SE = 58.1 Mg ha-1, respectively. The predicted model of the relationship between LiDAR metrics and biomass was applied to data captured in 2003. Identified areas of change corresponded well with a map of forest management operations of varying intensities. Where sensitivity to change allows, vegetation age estimated using time series of Landsat observations, combined with biomass estimates, allows growth curves to be produced to monitor the effect of pests or disease, recovery rates following disturbance, or carbon sequestration.