Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data

Tropical forests are huge reservoirs of terrestrial carbon and are experiencing rapid degradation and deforestation. Understanding forest structure proves vital in accurately estimating both forest biomass and also the natural disturbances and remote sensing is an essential method for quantification of forest properties and structure in the tropics. Our objective is to examine canopy vegetation profiles formulated from discrete return LIght Detection And Ranging (lidar) data and examine their usefulness in estimating forest structural parameters measured during a field campaign. We developed a modeling procedure that utilized hypothetical stand characteristics to examine lidar profiles. In essence, this is a simple method to further enhance shape characteristics from the lidar profile. In this paper we report the results comparing field data collected at La Selva, Costa Rica (10° 26′ N, 83° 59′ W) and forest structure and parameters calculated from vegetation height profiles and forest structural modeling. We developed multiple regression models for each measured forest biometric property using forward stepwise variable selection that used Bayesian information criteria (BIC) as selection criteria. Among measures of forest structure, ranging from tree lateral density, diameter at breast height, and crown geometry, we found strong relationships with lidar canopy vegetation profile parameters. Metrics developed from lidar that were indicators of height of canopy were not significant in estimating plot biomass (p-value = 0.31, r2 = 0.17), but parameters from our synthetic forest model were found to be significant for estimating many of the forest structural properties, such as mean trunk diameter (p-value = 0.004, r2 = 0.51) and tree density (p-value = 0.002, r2 = 0.43). We were also able to develop a significant model relating lidar profiles to basal area (p-value = 0.003, r2 = 0.43). Use of the full lidar profile provided additional avenues for the prediction of field based forest measure parameters. Our synthetic canopy model provides a novel method for examining lidar metrics by developing a look-up table of profiles that determine profile shape, depth, and height. We suggest that the use of metrics indicating canopy height derived from lidar are limited in understanding biomass in a forest with little variation across the landscape and that there are many parameters that may be gleaned by lidar data that inform on forest biometric properties

Relating LANDSAT ETM+ and forest inventory data for mapping successional stages in a tropical wet forestRelacionando LANDSAT ETM+ e dados de inventário florestal para mapeamento estádios sucessionais em uma floresta tropical úmida

AbstractIn this study, we test whether an existing classification technique based on the integration of LANDSAT ETM+ and forest inventory data enables detailed characterization of successional stages in a tropical wet forest site. The specific objectives were: (1) to map forest age classes across the La Selva Biological Station in Costa Rica; and (2) to quantify uncertainties in the proposed approach in relation to field data and existing vegetation maps. Although significant relationships between vegetation hight entropy (a surrogate for forest age) and ETM+ data were detected, the classification scheme tested in this study was not suitable for characterizing spatial variation in age at La Selva, as evidenced by the error matrix and the low Kappa coefficient (0.129). Factors affecting the performance of the classification at this particular study site include the smooth transition in vegetation structure between intermediate and late successional stages, and the low sensitivity of NDVI to variations in vertical structure at high biomass levels. ResumoNesse estudo, testamos se uma técnica de classificação existente, baseada na integração de imagens LANDSAT ETM+ e os dados de inventário florestal, permite a caracterização detalhada dos estádios sucessionais em uma área de floresta tropical úmida. Os objetivos específicos foram: (1) mapear classes de idade florestal na Estação Biológica La Selva, na Costa Rica, e (2) quantificar as incertezas da abordagem proposta em relação aos dados de campo e mapas de vegetação existente. Apesar de terem sido detectadas relações significativas entre dados ETM+ e medidas de entropia da altura da vegetação (um substituto para a idade florestal) o sistema de classificação testados nesse estudo não se demonstrou adequado para caracterizar a variação espacial em idade em La Selva, como evidenciado pela matriz de erro e o baixo coeficiente Kappa (0,129). Fatores que afetam o desempenho da classificação área de estudo em particular, incluem a alta similaridade estrutural entre os estádios sucessionais intermediário e avançado, e a baixa sensibilidade do NDVI a variações na estrutura vertical da biomassa em áreas com níveis elevados de biomassa

THE AMBIGUITY IN FOREST PROFILES AND XTINCTION ESTIMATED FROM MULTIBASELINE INTERFEROMETRIC SAR

This paper demonstrates by simulation that in the estimation of vegetation profiles from multibaseline interferometric synthetic aperture radar (InSAR), the peak extinction coefficient is poorly determined for typical interferometric coherence and phase accuracies. This coefficient determines overall density and affects the relative density profiles estimated from interferometry. This paper shows that a given radar power profile gives rise to a family of vegetation density profiles, depending on the peak extinction assumed. It is further  demonstrated that estimating the peak extinction requires coherence accuracies of better than 0.1% and phase accuracies of better than a few tenths of a degree, both of which exceed the performance of typical or envisioned SAR systems. Two recommended approaches to profile production with InSAR are 1) use the radar power profile instead of the vegetation density profile for biomass estimation and other ecosystem characterization (in analogy to LIDAR power which is most frequently used for lidar studies of biomass) or 2) apply external information to establish the extinction characteristics needed for vegetation density profiles.Esse artigo procura demonstrar, por simulação, que na estimativa de perfis de volume da vegetação por interferometria  com múltiplas linhas de base, o pico de extinção não é adequadamente determinado pela coerência interferométrica e fase, com acurácias típicas de InSAR. Esse pico determina a densidade global, afetando os perfis de densidade relativa da vegetação estimados por interferometria. Esse trabalho mostra que para um dado perfil de potência-radar há uma série de perfis de densidade da vegetação, dependendo do pico de extinção assumido. É ainda demonstrado que a estimativa do pico de  extinção requer exatidões de coerência melhores que 0,1%, bem como, de acurácias de fases que alguns décimos de graus, valores esses que atualmente excedem o desempenho de sistemas SAR em operação ou aqueles previstos. As duas abordagens recomendadas para a produção de perfis com InSAR são: (1) utilizar o perfil-radar, ao invés do perfil de densidade de vegetação, para estimação de biomassa e outras caracterizações de ecossistema (em nalogia à potência-lidar, a qual é mais  frequentemente utilizada nos estudos de biomassa baseados em LIDAR); ou (2) aplicar informação externa para estabelecer as características de extinção necessárias aos perfis de densidade de vegetação

Estimating aboveground biomass in tropical forests: Field methods and error analysis for the calibration of remote sensing observations

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (<65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches. © 2017 by the authors; licensee MDPI, Basel, Switzerland

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Estimating Forest Vertical Structure from Multialtitude, Fixed-Baseline Radar Interferometric and Polarimetric Data

Parameters describing the vertical structure of forests, for example tree height, height-to-base-of-live-crown, underlying topography, and leaf area density, bear on land-surface, biogeochemical, and climate modeling efforts. Single, fixed-baseline interferometric synthetic aperture radar (INSAR) normalized cross-correlations constitute two observations from which to estimate forest vertical structure parameters: Cross-correlation amplitude and phase. Multialtitude INSAR observations increase the effective number of baselines potentially enabling the estimation of a larger set of vertical-structure parameters. Polarimetry and polarimetric interferometry can further extend the observation set. This paper describes the first acquisition of multialtitude INSAR for the purpose of estimating the parameters describing a vegetated land surface. These data were collected over ponderosa pine in central Oregon near longitude and latitude -121 37 25 and 44 29 56. The JPL interferometric TOPSAR system was flown at the standard 8-km altitude, and also at 4-km and 2-km altitudes, in a race track. A reference line including the above coordinates was maintained at 35 deg for both the north-east heading and the return southwest heading, at all altitudes. In addition to the three altitudes for interferometry, one line was flown with full zero-baseline polarimetry at the 8-km altitude. A preliminary analysis of part of the data collected suggests that they are consistent with one of two physical models describing the vegetation: 1) a single-layer, randomly oriented forest volume with a very strong ground return or 2) a multilayered randomly oriented volume; a homogeneous, single-layer model with no ground return cannot account for the multialtitude correlation amplitudes. Below the inconsistency of the data with a single-layer model is followed by analysis scenarios which include either the ground or a layered structure. The ground returns suggested by this preliminary analysis seem too strong to be plausible, but parameters describing a two-layer compare reasonably well to a field-measured probability distribution of tree heights in the area

Forest Attributes from Radar Interferometric Structure and its Fusion with Optical Remote Sensing

The possibility of global, three-dimensional remote sensing of forest structure with interferometric synthetic aperture radar (InSAR) bears on important forest ecological processes, particularly the carbon cycle. InSAR supplements two-dimensional remote sensing with information in the vertical dimension. Its strengths in potential for global coverage complement those of lidar (light detecting and ranging), which has the potential for high-accuracy vertical profiles over small areas. InSAR derives its sensitivity to forest vertical structure from the differences in signals received by two, spatially separate radar receivers. Estimation of parameters describing vertical structure requires multiple-polarization, multiple-frequency, or multiple-baseline InSAR. Combining InSAR with complementary remote sensing techniques, such as hyperspectral optical imaging and lidar, can enhance vertical-structure estimates and consequent biophysical quantities of importance to ecologists, such as biomass. Future InSAR experiments will supplement recent airborne and spaceborne demonstrations, and together with inputs from ecologists regarding structure, they will suggest designs for future spaceborne strategies for measuring global vegetation structure

Mapping Successional Stages in a Wet Tropical Forest Using Landsat ETM+ and Forest Inventory Data

In this study, we test whether an existing classification technique based on the integration of Landsat ETM+ and forest inventory data enables detailed characterization of successional stages in a wet tropical forest site. The specific objectives were: (1) to map forest age classes across the La Selva Biological Station in Costa Rica; and (2) to quantify uncertainties in the proposed approach in relation to field data and existing vegetation maps. Although significant relationships between vegetation height entropy (a surrogate for forest age) and ETM+ data were detected, the classification scheme tested in this study was not suitable for characterizing spatial variation in age at La Selva, as evidenced by the error matrix and the low Kappa coefficient (12.9%). Factors affecting the performance of the classification at this particular study site include the smooth transition in vegetation structure between intermediate and advanced successional stages, and the low sensitivity of NDVI to variations in vertical structure at high biomass levels