Abiotic controls on macroscale variations of humid tropical forest height

Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004–2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics.The research was funded by Gabon National Park (ANPN) under the contract of 011-ANPN/2012/SE-LJTW at UCLA. We thank IIASA, FAO, USGS, NASA, Worldclim science teams for making their data available. (011-ANPN/2012/SE-LJTW - Gabon National Park (ANPN) at UCLA

Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests

The objective of this study is to better understand the relationship between forest structure and point cloud features generated from certain airborne and space borne sensors. Point cloud features derived from airborne laser scanning (ALS), aerial imagery (AI), WorldView-2 imagery (WV2), TerraSAR-X, and Tandem-X (TDX) data were classified as features characterizing forest height and density as well as variation in tree height. Correlations between these features and field-measured attributes describing forest height, density and tree height variation were investigated at plot scale. From the field-measured attributes, basal area (G) and the number of trees per unit area (N) were used as forest density indicators whereas maximum tree height (H-max) and standard deviation in tree height (H-std) were used as indicators for forest height and tree height variation, respectively. In the analyses, field observations from 91 sample plots (32 m x 32 m) located in southern Finland were used. Even though ALS was found to be the most accurate data source in characterizing forest structure, AI, WV2, and TDX were also capable of characterizing forest height at plot scale with correlation coefficients stronger than 0.85. However, ALS was the only data source capable of providing separate features for characterizing also the variation in tree height and forest density. Features related to forest height, generated from the other data sources besides ALS, also provided strongest correlation with the forest density attributes and variation in tree height, in addition to H-max. Due to these more diverse characterization capabilities, forest structural attributes can be predicted more accurately by using ALS, also in the areas where the relation between the attributes of interest is not solely dependent on forest height, compared to the other investigated 3D remote sensing data sources.Peer reviewe

Forest height inventory from airborne Synthetic Aperture Radar

This study assesses the capabilities of commercially available airborne short wavelength Synthetic Aperture Radar (SAR) Interferometry (JnSAR) for retrieving individual tree and forest stand height. Individual tree and stand heights are of importance to the forest industry for a number of reasons. Tree height is a key variable for calculating the amount of wood volume in a tree stem, as well as for predictions of amount of timber for extraction. Forest stand height is an important indicator of standing biomass for management purposes as well as for the assessment of carbon storage. Height is also an important ecological parameter in its own right, and an important input parameter for line-of-site analysis. Remote sensing offers an alternative to destructive measurements for accurate, rapid and cost effective technique without user subjectivity. SAR provides the potential for direct height measurement over large areas, and can operate independently of lighting or weather conditions, which often restricts the use of other remote sensing techniques.In this study, tree height is estimated by subtracting a ground surface elevation model (a UK Ordnance Survey DEM, OSDE M , or a Digital Terrain Model, DTM, from commercial Intermap Technologies) from a Digital Surface Model, DSM, (from Intermap Technologies) and the results are then compared to field measurements of tree and stand heights. The accuracy of Intermap Technologies ST AR-3i InSAR DEM products are initially compared to national elevation data sets. Over various ground types, it was concluded that, within the test areas, over non-vegetated ground the mean difference between the DTM and OSDEM was l.38m RMSE with a l.05m Standard Deviation (SD), and this is within Intermap's stated accuracies. Over forested ground the mean difference was 13.5lm RMSE (2.2lm SD). This vegetation bias was primarily due to limitations of the interpolation procedure used to determine the DTM from the DSM.Subsequently, the use of two airborne InSAR data sets is assessed for top height retrieval as an operational product, as well as a precursor and supplement to satellite data. Firstly, X-band data from Intermap are used to retrieve homogenous plantation top height over four UK study sites using the difference between the DSM and OSDEM with mean underestimations of 33.48% (6.99m mean difference). When assessed for single species, the DSM-OSDEM procedure gave height underestimations of 18-24% for Sitka spruce and 40% for Scots pine, indicating a dependency on canopy structure. Correcting retrieved height based on linear regression with ground reference data is shown to improve height estimation; as such, applying a generic correction to retrieved heights from all four UK study sites improves overall accuracy to 16.77% (3.12m mean difference). For trees greater than 18m measured height, the accuracy is increased to 12.27% (0.92m mean difference).Secondly, X-band data are also used to retrieve tree total height over two heterogeneous woodland areas in Belize and the UK. In Glen Affric, UK, height retrieval using the X-band DSM-OSDEM procedure for individual trees produce mean underestimation of 94.87% (6.08m mean difference). In Belize, height retrieval using the X-band DSM-DEM procedure for individual trees produces a mean underestimation of 74.71% (6.85m mean difference). For the Belize test site, height retrieval using JPL Airsar C-band DSM-DEM procedure for individual trees produces retrieved heights with a mean underestimation of 55.97% (4.79m mean difference). The primary cause of error is that layover effects due to SAR geometry may result in the retrieved height from a specific image coordinate not representing the same geographical position as the measured height.Relationships between radar retrieved height and forest parameters such as stocking density and tree height and radar dependent properties such as slope and edge effects are presented as possible explanations for variations across the collected data. Supporting work using a simple coherent interferometric scattering model is also used to characterise and explain the effects on tree height retrieval due to variations in slope, number density, stand height and forest edges.The results indicate that top height retrieval over homogenous forest stands is feasible with similar accuracies to those found with other remote sensing techniques and ground survey. Individual tree location assessment does not appear to be a suitable technique for assessing height retrieval in heterogeneous environments, and further investigations are required to determine a more suitable approach. This new data set therefore potentially allows a rapid and timely management tool for use in cost-effective sustainable forest management and related applications

Forest height maps obtained with ICESAT-2 data

The purpose of this project is to get data from the ICESAT 2 satellite to obtain maps for the regions of Europe and North-America, of the height of the vegetation. To do it, we will use tools as MATLAB to store all this data obtained and compare it through 2d and 3d graphics.El propósito de este proyecto es obtener datos del satélite ICESAT 2 para obtener mapas de la regiones de Europa y Norte América, de la altura de la vegetación. Para ello utilizaremos herramientas como MATLAB para almacenar todos estos datos obtenidos y compararlos mediante gráficos 2d y 3d.El propòsit d'aquest projecte és obtenir dades del satèl·lit ICESAT 2 per obtenir mapes de les regions d'Europa i nord America, de l'alçada de la vegetació. Per això utilitzarem eines com MATLAB per emmagatzemar totes aquestes dades obtingudes i comparar-les mitjançant gràfics 2d i 3d

Estimation of Forest Height Using Spaceborne Repeat-Pass L-Band InSAR Correlation Magnitude over the US State of Maine

This paper describes a novel, simple and efficient approach to estimate forest height over a wide region utilizing spaceborne repeat-pass InSAR correlation magnitude data at L-band. We start from a semi-empirical modification of the RVoG model that characterizes repeat-pass InSAR correlation with large temporal baselines (e.g., 46 days for ALOS) by taking account of the temporal change effect of dielectric fluctuation and random motion of scatterers. By assuming (1) the temporal change parameters and forest backscatter profile/extinction coefficient follow some mean behavior across each inteferogram; (2) there is minimal ground scattering contribution for HV-polarization; and (3) the vertical wavenumber is small, a simplified inversion approach is developed to link the observed HV-polarized InSAR correlation magnitude to forest height and validated using ALOS/PALSAR repeat-pass observations against LVIS lidar heights over the Howland Research Forest in central Maine, US (with RMSE \u3c 4 m at a resolution of 32 hectares). The model parameters derived from this supervised regression are used as the basis for propagating the estimates of forest height to available interferometric pairs for the entire state of Maine, thus creating a state-mosaic map of forest height. The present approach described here serves as an alternative and complementary tool for other PolInSAR inversion techniques when full-polarization data may not be available. This work is also meant to be an observational prototype for NASA’s DESDynI-R (now called NISAR) and JAXA’s ALOS-2 satellite missions

Abrupt Change in Forest Height along a Tropical Elevation Gradient Detected Using Airborne Lidar

Most research on vegetation in mountain ranges focuses on elevation gradients as climate gradients, but elevation gradients are also the result of geological processes that build and deconstruct mountains. Recent findings from the Luquillo Mountains, Puerto Rico, have raised questions about whether erosion rates that vary due to past tectonic events and are spatially patterned in relation to elevation may drive vegetation patterns along elevation gradients. Here we use airborne light detection and ranging (LiDAR) technology to observe forest height over the Luquillo Mountain Range. We show that models with different functional forms for the two prominent bedrock types best describe the forest height-elevation patterns. On one bedrock type there are abrupt decreases in forest height with elevation approximated by a sigmoidal function, with the inflection point near the elevation of where other studies have shown there to be a sharp change in erosion rates triggered by a tectonic uplift event that began approximately 4.2 My ago. Our findings are consistent with broad geologically mediated vegetation patterns along the elevation gradient, consistent with a role for mountain building and deconstructing processes

An Automatic Mosaicking Algorithm for the Generation of a Large-Scale Forest Height Map Using Spaceborne Repeat-Pass InSAR Correlation Magnitude

This paper describes an automatic mosaicking algorithm for creating large-scale mosaic maps of forest height. In contrast to existing mosaicking approaches through using SAR backscatter power and/or InSAR phase, this paper utilizes the forest height estimates that are inverted from spaceborne repeat-pass cross-pol InSAR correlation magnitude. By using repeat-pass InSAR correlation measurements that are dominated by temporal decorrelation, it has been shown that a simplified inversion approach can be utilized to create a height-sensitive measure over the whole interferometric scene, where two scene-wide fitting parameters are able to characterize the mean behavior of the random motion and dielectric changes of the volume scatterers within the scene. In order to combine these single-scene results into a mosaic, a matrix formulation is used with nonlinear least squares and observations in adjacent-scene overlap areas to create a self-consistent estimate of forest height over the larger region. This automated mosaicking method has the benefit of suppressing the global fitting error and, thus, mitigating the “wallpapering” problem in the manual mosaicking process. The algorithm is validated over the U.S. state of Maine by using InSAR correlation magnitude data from ALOS/PALSAR and comparing the inverted forest height with Laser Vegetation Imaging Sensor (LVIS) height and National Biomass and Carbon Dataset (NBCD) basal area weighted (BAW) height. This paper serves as a companion work to previously demonstrated results, the combination of which is meant to be an observational prototype for NASA’s DESDynI-R (now called NISAR) and JAXA’s ALOS-2 satellite missions

Quantifying Temporal Decorrelation over Boreal Forest at L- and P-band

Temporal decorrelation is probably the most critical factor towards a successful implementation of Pol-InSAR parameter inversion techniques in terms of repeat-pass InSAR scenarios. In this paper the effect and impact of temporal decorrelation at L- and P-band is quantified. For this, data acquired by DLR’s E-SAR system in the frame of the BioSAR campaign (initiated and sponsored by the European Space Agency (ESA)) over boreal forest with variable temporal baseline in 2007 in Sweden are analyzed. For validation lidar data and ground measurements data are used

Biomass estimation as a function of vertical forest structure and forest height. Potential and limiations for remote sensing (radar and LiDAR)

Forest biomass stock, spatial distribution and dynamics are unknown parameters for many regions of the world. Today’s information is largely based on ground measurements on a plot basis without coverage in many remote regions that are fundamental for the global carbon cycle. Thus, a method capable of quantifying biomass by means of Remote Sensing (RS) could help to reduce these uncertainties and contribute to a better understanding of it. In this study the capacity to improve the estimation of above-ground biomass (AGB) with a new approach based on forest vertical structure and its potential to improve RS estimations is analyzed. Height to biomass allometry allows biomass estimations from remote sensing systems capable to resolve forest height (LiDAR and polarimetric SAR interferometry (Pol-InSAR)). However, this approach meets its limitations for forest ecosystems under changing conditions in density and structure. To improve biomass estimation accuracy, additional parameters need to be measured. Pol-InSAR and LiDAR allow getting besides forest height vertical backscattering profiles which are connected to forest vertical structure. Thus, due to the relation between structural parameters and AGB expressed by the Structure to Biomass allometry, AGB can be potentially inverted from these systems. The best characterization of forest vertical structure is obtained using the Legendre polynomials. Biomass profiles can be then characterized by the decomposition into a set of Legendre-Fourier basis functions. This method is able to accurately reconstruct vertical biomass profiles with low frequency features. Vertical backscattering profiles are strongly dependent on the sensor used as the resulting profiles are very sensitive to the wavelength and system geometry. E.g. LiDAR profiles are more sensitive to leaves and crowns while Pol-InSAR tends to reconstruct more the woody compartments (stems and branches). In this study, vertical backscattering profiles from short footprint airborne LiDAR and Pol-InSAR data are evaluated for their potential to reconstruct vertical forest structure. With the Legendre decomposition it is possible to parameterize the vertical backscattering profiles and relate them to forest biomass; even though for each remote sensing system different calibration methodologies must be derived. A first step is achieved using the calibration of backscattering signal with known biomass levels showing optimum results. In order to reduce the need of known parameters a new calibration methodology that exploits height to biomass allometric relations has been derived. Inversions using this methodology are tested for LiDAR and SAR profiles showing good correlations for an optimum subset of samples. As each system (frequency) is sensitive to certain biomass components an underestimation is generally expected. Research in this area is ongoing and will be presented with special focus on each system capacity to reconstruct forest vertical biomass distribution for broader sets of samples

Extrapolation of Airborne Polarimetric and Interferometric SAR Data for Validation of Bio-Geo-Retrieval Algorithms for Future Spaceborne SAR Missions

Spaceborne SAR system concepts and mission design is often based on algorithms developed and the experience gathered from airborne SAR experiments and associated dedicated campaigns. However, airborne SAR systems have better performance parameters than their future space-borne counterparts as their design is not impacted by mass, power, and storage constraints. This paper describes a methodology to extrapolate spaceborne quality SAR image products from long wavelength airborne polarimetric SAR data which were acquired especially for the development and validation of bio/geo-retrieval algorithms in forested regions. For this purpose not only system (sensor) related parameters are altered, but also those relating to the propagation path (ionosphere) and to temporal decorrelation