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)

On the use of rapid-scan, low point density terrestrial laser scanning (TLS) for structural assessment of complex forest environments

Forests fulfill an important role in natural ecosystems, e.g., they provide food, fiber, habitat, and biodiversity, all of which contribute to stable ecosystems. Assessing and modeling the structure and characteristics in forests can lead to a better understanding and management of these resources. Traditional methods for collecting forest traits, known as “forest inventory”, is achieved using rough proxies, such as stem diameter, tree height, and foliar coverage; such parameters are limited in their ability to capture fine-scale structural variation in forest environments. It is in this context that terrestrial laser scanning (TLS) has come to the fore as a tool for addressing the limitations of traditional forest structure evaluation methods. However, there is a need for improving TLS data processing methods. In this work, we developed algorithms to assess the structure of complex forest environments – defined by their stem density, intricate root and stem structures, uneven-aged nature, and variable understory - using data collected by a low-cost, portable TLS system, the Compact Biomass Lidar (CBL). The objectives of this work are listed as follow: 1. Assess the utility of terrestrial lidar scanning (TLS) to accurately map elevation changes (sediment accretion rates) in mangrove forest; 2. Evaluate forest structural attributes, e.g., stems and roots, in complex forest environments toward biophysical characterization of such forests; and 3. Assess canopy-level structural traits (leaf area index; leaf area density) in complex forest environments to estimate biomass in rapidly changing environments. The low-cost system used in this research provides lower-resolution data, in terms of scan angular resolution and resulting point density, when compared to higher-cost commercial systems. As a result, the algorithms developed for evaluating the data collected by such systems should be robust to issues caused by low-resolution 3D point cloud data. The data used in various parts of this work were collected from three mangrove forests on the western Pacific island of Pohnpei in the Federated States of Micronesia, as well as tropical forests in Hawai’i, USA. Mangrove forests underscore the economy of this region, where more than half of the annual household income is derived from these forests. However, these mangrove forests are endangered by sea level rise, which necessitates an evaluation of the resilience of mangrove forests to climate change in order to better protect and manage these ecosystems. This includes the preservation of positive sediment accretion rates, and stimulating the process of root growth, sedimentation, and peat development, all of which are influenced by the forest floor elevation, relative to sea level. Currently, accretion rates are measured using surface elevation tables (SETs), which are posts permanently placed in mangrove sediments. The forest floor is measured annually with respect to the height of the SETs to evaluate changes in elevation (Cahoon et al. 2002). In this work, we evaluated the ability of the CBL system for measuring such elevation changes, to address objective #1. Digital Elevation Models (DEMs) were produced for plots, based on the point cloud resulted from co-registering eight scans, spaced 45 degree, per plot. DEMs are refined and produced using Cloth Simulation Filtering (CSF) and kriging interpolation. CSF was used because it minimizes the user input parameters, and kriging was chosen for this study due its consideration of the overall spatial arrangement of the points using semivariogram analysis, which results in a more robust model. The average consistency of the TLS-derived elevation change was 72%, with and RMSE value of 1.36 mm. However, what truly makes the TLS method more tenable, is the lower standard error (SE) values when compared to manual methods (10-70x lower). In order to achieve our second objective, we assessed structural characteristics of the above-mentioned mangrove forest and also for tropical forests in Hawaii, collected with the same CBL scanner. The same eight scans per plot (20 plots) were co-registered using pairwise registration and the Iterative Closest Point (ICP). We then removed the higher canopy using a normal change rate assessment algorithm. We used a combination of geometric classification techniques, based on the angular orientation of the planes fitted to points (facets), and machine learning 3D segmentation algorithms to detect tree stems and above-ground roots. Mangrove forests are complex forest environments, containing above-ground root mass, which can create confusion for both ground detection and structural assessment algorithms. As a result, we needed to train a supporting classifier on the roots to detect which root lidar returns were classified as stems. The accuracy and precision values for this classifier were assessed via manual investigation of the classification results in all 20 plots. The accuracy and precision for stem classification were found to be 82% and 77%, respectively. The same values for root detection were 76% and 68%, respectively. We simulated the stems using alpha shapes in order to assess their volume in the final step. The consistency of the volume evaluation was found to be 85%. This was obtained by comparing the mean stem volume (m3/ha) from field data and the TLS data in each plot. The reported accuracy is the average value for all 20 plots. Additionally, we compared the diameter-at-breast-height (DBH), recorded in the field, with the TLS-derived DBH to obtain a direct measure of the precision of our stem models. DBH evaluation resulted in an accuracy of 74% and RMSE equaled 7.52 cm. This approach can be used for automatic stem detection and structural assessment in a complex forest environment, and could contribute to biomass assessment in these rapidly changing environments. These stem and root structural assessment efforts were complemented by efforts to estimate canopy-level structural attributes of the tropical Hawai’i forest environment; we specifically estimated the leaf area index (LAI), by implementing a density-based approach. 242 scans were collected using the portable low-cost TLS (CBL), in a Hawaii Volcano National Park (HAVO) flux tower site. LAI was measured for all the plots in the site, using an AccuPAR LP-80 Instrument. The first step in this work involved detection of the higher canopy, using normal change rate assessment. After segmenting the higher canopy from the lidar point clouds, we needed to measure Leaf Area Density (LAD), using a voxel-based approach. We divided the canopy point cloud into five layers in the Z direction, after which each of these five layers were divided into voxels in the X direction. The sizes of these voxels were constrained based on interquartile analysis and the number of points in each voxel. We hypothesized that the power returned to the lidar system from woody materials, like branches, exceeds that from leaves, due to the liquid water absorption of the leaves and higher reflectivity for woody material at the 905 nm lidar wavelength. We evaluated leafy and woody materials using images from projected point clouds and determined the density of these regions to support our hypothesis. The density of points in a 3D grid size of 0.1 m, which was determined by investigating the size of the branches in the lower portion of the higher canopy, was calculated in each of the voxels. Note that “density” in this work is defined as the total number of points per grid cell, divided by the volume of that cell. Subsequently, we fitted a kernel density estimator to these values. The threshold was set based on half of the area under the curve in each of the distributions. The grid cells with a density below the threshold were labeled as leaves, while those cells with a density above the threshold were set as non-leaves. We then modeled the LAI using the point densities derived from TLS point clouds, achieving a R2 value of 0.88. We also estimated the LAI directly from lidar data by using the point densities and calculating leaf area density (LAD), which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was found to be 90%. Since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed a semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets, where each of the plots were 30 meter spaced for each subset. LAI model R2 values for these subsets ranged between 0.84 - 0.96. The results bode well for using this method for automatic estimation of LAI values in complex forest environments, using a low-cost, low point density, rapid-scan TLS

Remote sensing of leaf area index : enhanced retrieval from close-range and remotely sensed optical observations

A wide range of models used in agriculture, ecology, carbon cycling, climate and other related studies require information on the amount of leaf material present in a given environment to correctly represent radiation, heat, momentum, water, and various gas exchanges with the overlying atmosphere or the underlying soil. Leaf area index (LAI) thus often features as a critical land surface variable in parameterisations of global and regional climate models, e.g., radiation uptake, precipitation interception, energy conversion, gas exchange and momentum, as all areas are substantially determined by the vegetation surface. Optical wavelengths of remote sensing are the common electromagnetic regions used for LAI estimations and generally for vegetation studies. The main purpose of this dissertation was to enhance the determination of LAI using close-range remote sensing (hemispherical photography), airborne remote sensing (high resolution colour and colour infrared imagery), and satellite remote sensing (high resolution SPOT 5 HRG imagery) optical observations. The commonly used light extinction models are applied at all levels of optical observations. For the sake of comparative analysis, LAI was further determined using statistical relationships between spectral vegetation index (SVI) and ground based LAI. The study areas of this dissertation focus on two regions, one located in Taita Hills, South-East Kenya characterised by tropical cloud forest and exotic plantations, and the other in Gatineau Park, Southern Quebec, Canada dominated by temperate hardwood forest. The sampling procedure of sky map of gap fraction and size from hemispherical photographs was proven to be one of the most crucial steps in the accurate determination of LAI. LAI and clumping index estimates were significantly affected by the variation of the size of sky segments for given zenith angle ranges. On sloping ground, gap fraction and size distributions present strong upslope/downslope asymmetry of foliage elements, and thus the correction and the sensitivity analysis for both LAI and clumping index computations were demonstrated. Several SVIs can be used for LAI mapping using empirical regression analysis provided that the sensitivities of SVIs at varying ranges of LAI are large enough. Large scale LAI inversion algorithms were demonstrated and were proven to be a considerably efficient alternative approach for LAI mapping. LAI can be estimated nonparametrically from the information contained solely in the remotely sensed dataset given that the upper-end (saturated SVI) value is accurately determined. However, further study is still required to devise a methodology as well as instrumentation to retrieve on-ground green leaf area index . Subsequently, the large scale LAI inversion algorithms presented in this work can be precisely validated. Finally, based on literature review and this dissertation, potential future research prospects and directions were recommended.Ei saatavill

Mapping evapotranspiration to disaggregate eddy-covariance footprints

Land-atmosphere interactions are commonly quantified using eddy-covariance (EC) equipment. This technique provides fluxes which are attributed to an area-averaged two-dimensional flux footprint. Although source flux heterogeneity is present within these footprints, current EC footprint models are unable to distinguish the variable flux contributions (although they are incorporated into the bulk EC flux). A disaggregation method would increase the value of EC data by allowing users to isolate individual fluxes from features within the flux footprint; furthermore, this may extend the useability of the EC method to more complex terrain of mixed land classifications. It remains unexplored how high-resolution surface energy balance (SEB) models can be used to disaggregate these EC footprints. This thesis presents a SEB workflow using Unoccupied Aerial Vehicles (UAVs) to generate high-resolution patterns of evapotranspiration (ET) to disaggregate EC footprints in a novel disaggregated flux footprint prediction (disFFP) method. This workflow begins by using novel UAV Light Ranging And Detection (LiDAR) techniques to derive detailed maps of canopy height (h), effective leaf area index (LAI_e), and canopy viewing fraction (f_c), consistent with known vegetation patterns and field-average observations (LAI_e RMSE=0.08-0.81 m^2 m^(-2)). It then follows an atmospheric correction (path transmissivity and upward-welling radiance) and an ensemble emissivity adjustment (brightness to radiometric temperature), testing these with UAV thermal data to determine their absolute (magnitude) and relative (spatial pattern) effects on temperature. A modified t-test - considering autocorrelation - showed that a raw brightness temperature has similar spatial patterns (r>.99, p_val≫0.001) to brightness and radiometric temperatures. Combining these thermal and LiDAR UAV inputs with a high-resolution SEB model (HRMET), the model performance was then compared against EC fluxes. It followed that HRMET tended to overestimate latent heat over full canopies when surface-air temperature differences exceed 4-5°C. Overall, HRMET succeeded at replicating EC latent heat flux within RMSE of 79-136 W m^(-2) using raw brightness and ensemble emissivity corrected (observed radiometric) temperatures. The resultant relative ET maps (ET_R) provided a coherent chronology of the changing flux landscape. Furthermore, the ET_R trials using corrected temperatures (brightness, radiometric, and observed radiometric) had similar spatial patterns to those found using just raw brightness temperature (r>.93, p_val≫0.001). This implies that a raw brightness temperature is sufficient for determining relative patterns of evapotranspiration. The next part of the workflow uses ET_R to disaggregate a well-known parameterization of a backwards-Lagrangian flux footprint model. The proposed disaggregation method (disFFP) uses the concept of ET period to describe an interval of time (day scale) where the EC flux environment remains relatively constant (constant-rate cumulative ET rate). ET periods were determined using a piece-wise regression of the cumulative day-over-day EC latent flux rate. Each season was divided into five ET periods and compared with the two other seasons to discover potential metrics for further classifying ET periods. It was found that ET rates were consistent across comparable ET periods, 2-38 % (standard deviation as a percentage of sample mean), and that additional metrics (plant phenology, growing degree day, standard precipitation index) played an important role in characterizing ET periods for inter-seasonal work. Eddy-covariance and UAV data were coupled using climatology footprints of ET periods and the coinciding ET patterns (ET_R and coefficient of variation). The disFFP combination of these two products (footprint-weighted factoring) provided disaggregated footprints (EC bulk ET of 144 mm) that reflected increased contributions (180-200 mm) over high ET areas and diminished values (90-100 mm) over lower ET areas. These preliminary findings present an exciting new opportunity to connect discrete UAV data with continuous EC flux monitoring

Spatial Relationships between Trees and Snow in a Cold Regions Montane Forest

Vegetation structure is one of the primary factors that drives spatial variation of snow accumulation in forests due to interactions between falling snow, intercepted snow, and the forest canopy. These processes result in spatially heterogeneous snowpacks and snowpack energy fluxes, driving areal snow cover depletion rates during melt periods with repercussions for stand- and basin-scale ablation rates and snowmelt runoff quantities and timings. While spatial variation of forest snowpack has been documented at scales from individual tree branches to forest stands, the underlying processes are not fully understood. Understanding these relationships is critical to understanding the combined effects of climate and vegetation changes on streamflow and ecology in basins with seasonal snowpacks. To better understand these processes, this study examined the spatial relationships between branch-scale canopy structure and subcanopy snow accumulation over two accumulation events in February of 2019, at an instrumented montane forest site in Marmot Creek Research Basin on the eastern slope of the Canadian Rockies. Repeated UAV lidar surveys were paired with manual snow surveys to produce estimates of snowpack snow water equivalent (SWE) and change in snowpack (ΔSWE) over each event at high spatial resolutions. Lidar observations of the forest canopy were combined with contemporary hemispherical photography to produce a diverse set of canopy metrics, including light transmittance metrics from a novel voxel ray sampling method. Results showed that over 75% of the spatial variance in subcanopy ΔSWE for each event was found within 2.0 m of horizontal distance, indicating that the spatial scale of canopy effects on snow interception and redistribution were primarily found at the scale of tree branches in this forest. Significant vertical asymmetry was seen in the relationships between snow accumulation and surrounding vegetation which was explained by prevailing wind directions. A descriptive Gaussian snowfall model that was consistent with the tight coupling observed between near-overhead canopy characteristics and snow accumulation explained more of the spatial variation in observed ΔSWE than any canopy metric considered and performed better than two other forest snow accumulation models based on larger scale canopy characteristics found in the literature. These findings emphasize the importance of representing branch-scale forest heterogeneity in models of snow accumulation and suggest that representation of vertical asymmetry in parametrizations of snow-vegetation relationships may yield more physically realistic models

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

Urban trees are strategically important in a city's effort to mitigate their carbon footprint, heat island effects, air pollution, and stormwater runoff. Currently, the most common method for quantifying urban forest structure and ecosystem function is through field plot sampling. However, taking intensive structural measurements on private properties throughout a city is difficult, and the outputs from sample inventories are not spatially explicit. The overarching goal of this dissertation is to develop methods for mapping urban forest structure and function using fused hyperspectral imagery and waveform lidar data at the individual tree crown scale. Urban forest ecosystem services estimated using the USDA Forest Service’s i-Tree Eco (formerly UFORE) model are based largely on tree species and leaf area index (LAI). Accordingly, tree species were mapped in my Santa Barbara, California study area for 29 species comprising >80% of canopy. Crown-scale discriminant analysis methods were introduced for fusing Airborne Visible Infrared Imaging Spectrometry (AVIRIS) data with a suite of lidar structural metrics (e.g., tree height, crown porosity) to maximize classification accuracy in a complex environment. AVIRIS imagery was critical to achieving an overall species-level accuracy of 83.4% while lidar data was most useful for improving the discrimination of small and morphologically unique species. LAI was estimated at both the field-plot scale using laser penetration metrics and at the crown scale using allometry. Agreement of the former with photographic estimates of gap fraction and the latter with allometric estimates based on field measurements was examined. Results indicate that lidar may be used reasonably to measure LAI in an urban environment lacking in continuous canopy and characterized by high species diversity. Finally, urban ecosystem services such as carbon storage and building energy-use modification were analyzed through combination of aforementioned methods and the i-Tree Eco modeling framework. The remote sensing methods developed in this dissertation will allow researchers to more precisely constrain urban ecosystem spatial analyses and equip cities to better manage their urban forest resource

The global tree carrying capacity (keynote)

editorial reviewe