Coupling potential of ICESat/GLAS and SRTM for the discrimination of forest landscape types in French Guiana

The Shuttle Radar Topography Mission (SRTM) has produced the most accurate nearly global elevation dataset to date. Over vegetated areas, the measured SRTM elevations are the result of a complex interaction between radar waves and tree crowns. In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations, and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classifier was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. The parameters used in the RF classification were the GLAS canopy height, the SRTM roughness index, the difference between the GLAS highest elevations and the SRTM elevations and the difference between the GLAS centroid elevations and the SRTM elevations. Discrimination of the five forest landscape types in French Guiana was possible, with an overall classification accuracy of 81.3% and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%. Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were analyzed. The results showed that the open forest LT (LT12), in some locations, contains trees that lose leaves during the dry season. These trees allow LT12 to be easily discriminated from the other LTs that retain their leaves using the following three criteria: (1) difference between the GLAS centroid elevations and the SRTM elevations, (2) ratio of top energy in the wet season to top energy in the dry season, or (3) ratio of ground energy in the wet season to ground energy in the dry season

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Lidar Remote Sensing of Vertical Foliage Profile and Leaf Area Index

Leaf Area Index (LAI) and Vertical Foliage Profile (VFP) are among the most important forest structural parameters, and characterization of those parameters in high biomass forests remains a major challenge in passive remote sensing due to signal saturation problem. Recently an active remote sensing technology, light detection and ranging (lidar), has shown a great promise in this task recognizing its accuracy in measuring aboveground biomass and canopy height. This dissertation further expands current application of lidar on ecosystem monitoring, and explores the capacity of deriving LAI and VFP from lidar data in particular. The overall goal of this study is to derive large scale forest LAI and VFP using data from the Geoscience Laser Altimeter System (GLAS) on board of ICESat, and provide a framework of validating such LAI products from plot level to global scale. To achieve this goal, a physically based Geometry Optical and Radiative Transfer (GORT) model was first developed using high quality airborne waveform lidar data over a tropical rainforest in La Selva, Costa Rica. The excellent agreement between lidar data and field destructively sampled data demonstrated the effectiveness of the Lidar-LAI model and suggested large footprint waveform lidar can provide accurate vertical LAI profile estimates that do not saturate even at the highest possible LAI levels. Next, an intercomparative study of ground-based, airborne and spaceborne retrievals of total LAI was conducted over the conifer-dominated forests of Sierra Nevada in California. Good relationships were discovered in their comparisons, following a scaling-up validation strategy where ground-based LAI observations were related to aircraft observations of LAI, which in turn were used to validate GLAS LAI derived from coincident data. Successful implementation of this strategy can pave the way for the future recovery of vertical LAI profiles globally. LAI and VFP products were then derived over both the entire state of California and Contiguous United States as an efficacy demonstration of the method. These products were the first ever attempts to obtain large scale estimates of LAI and VFP from lidar observations. Such forest structural measurement can be used not only to quantify carbon stock and flux of terrestrial ecosystem, but also to provide spatial information of specie abundance in biodiversity. Results from this study can also greatly help broaden scientific applications of future spaceborne lidar missions (e.g. ICESat-2 and GEDI)

A Comparison of Foliage Profiles in the Sierra National Forest Obtained with a Full-Waveform Under-Canopy EVI Lidar System with the Foliage Profiles Obtained with an Airborne Full-Waveform LVIS Lidar System

Foliage profiles retrieved froma scanning, terrestrial, near-infrared (1064 nm), full-waveformlidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r = 0.987 at 100 mhorizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Alsowe noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions

Biophysical parameter retrieval from satellite laser altimetry.

Quantifying and monitoring vegetation distribution and change are fundamental to carbon accounting and requirements of national forest inventories. This research explores the potential of the Geoscience Laser Altimeter System (GLAS), launched in 2003 by NASA as the first global Earth surface satellite LiDAR mission. The project study site is the Forest of Dean, Gloucestershire, UK, a highly mixed, temperate forest with varied topography. Methods are developed to distinguish the regions within waveforms returned from vegetation and ground. When compared with field measurements, estimation of canopy height gives a correlation of R2=0.92; RMSE=2.81m. Waveform indices are determined and evaluated with respect to their potential to estimate biophysical parameters. Heights of cumulative energy percentiles within the waveform prove to be significant estimators. When compared to calculations from independent yield models, results show correlations with stand- level top height (R2=0.76; RMSE 3.9m) and stemwood volume (mixed composition stands dominated by broadleaves: R2=0.47, RMSE=75.6m3/ha; conifers: R2=0.66, RMSE=82.5m3/ha). Uncertainty analysis is undertaken of both waveform and yield model estimates. Canopy cover is estimated for the area beneath GLAS waveforms, corrected for differences in reflectance for ground and canopy surfaces. These are assessed against airborne LiDAR estimates, validated using hemispherical photography. The method produces results with R2=0.63; RMSE=11% for stands with greatest coverage by broadleaves and R2=0.41; RMSE 16% for conifer-dominated stands. Small footprint airborne LiDAR (AL) is widely accepted to offer valuable data regarding forest parameters. An evaluation of AL and GLAS results demonstrate that the broad GLAS footprint dimensions allow similar estimation of stand-level parameters (e.g. AL/yield model Top Height: R2=0.73, RMSE=4.5m). Direct comparison of GLAS with AL shows ground surface identification with mean difference of 0.32m and that elevation profiles correspond well (98th percentiles R2=0.76, RMSE=3.4m). Finally, prospects for use of LiDAR in carbon accounting, assimilation within models and for forestry applications are discussed

Spatial and temporal variations of carbon in global tropical forests using satellite and ground observations

Tropical forests play an important role in the global carbon cycle. Covering 7-10% of the Earth land surface, they contribute to more than half of carbon stock in the world’s forests. Spatial and temporal variations of canopy structure and carbon stock are thus key indicators of ecological processes associated with the changing climate. At macroscales, we evaluated the contributions of climate, soil and topography to the structural variations of pan-tropical forests. Using LiDAR observations from satellite, we built spatial regression models between the LiDAR-derived canopy height and abiotic variables. Results show these factors and spatial contextual information can explain more than 60% of the variations in the heights of these forests. Within the tropics, Amazonian forests contain nearly half of the tropical carbon stocks and thus a vital part to the global carbon budget. The impacts of droughts in Amazonia have been recorded as short-term tree mortality and biomass loss from inventory plots. Using interannual satellite LiDAR measurements from 2003 to 2008, we quantitatively assessed carbon lost after the 2005 Amazon drought. Through careful signal filtering and sampling strategies, we found a significant loss of carbon over the Amazon basin, turning the ecosystem to a net source of carbon at 0.63 PgC/yr (0.16-1.10 PgC). And there was no sign of complete recovery 3 years after the drought. Besides natural disturbances such as droughts, human activities vastly alter the carbon footprint in the tropics. Tropical secondary forests (SF), mainly restored from deforestation, are often identified as a major terrestrial carbon sink. We analyzed changes in SF from 2004 to 2014 in the Brazilian Amazon and found SF contribution to regional carbon sink was negligible, due to significant turnover and frequent clearing activities. But it has the capacity of more than 0.2 PgC/yr net sink to compensate for total emissions from deforestation, if policies to restore secondary forests are implemented and enforced. My dissertation studies provide a clearer picture of abiotic controls over the pan-tropical forests and a better understanding of the carbon dynamics in regions of post-drought Amazonia and secondary forests in the Brazilian Amazon

Recovery of forest canopy parameters by inversion of multispectral LiDAR data

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation