Estimation of spruce needle-leaf chlorophyll content based on DART and PARAS canopy reflectance models

Needle-leaf chlorophyll content (Cab) of a Norway spruce stand was estimated from CHRIS-PROBA images using the canopy reflectance simulated by the PROSPECT model coupled with two canopy reflectance models: 1) discrete anisotropic radia- tive transfer model (DART); and 2) PARAS. The DART model uses a detailed description of the forest scene, whereas PARAS is based on the photon recollision probability theory and uses a simplified forest structural description. Subsequently, statisti- cally significant empirical functions between the optical indices ANCB 670 − 720 and ANMB 670 − 720 and the needle-leaf Cab content were established and then applied to CHRIS-PROBA data. The Cab estimating regressions using ANMB 670 − 720 were more robust than using ANCB 670 − 720 since the latter was more sensitive to LAI, especially in case of PARAS. Comparison between Cab esti- mates showed strong linear correlations between PARAS and DART retrievals, with a nearly perfect one-to-one fit when using ANMB 670 − 720 (slope = 1.1, offset = 11 μ g · cm − 2 ). Further com- parison with Cab estimated from an AISA Eagle image of the same stand showed better results for PARAS (RMSE = 2.7 μ g · cm − 2 for ANCB 670 − 720 ;RMSE = 9.5 μ g · cm − 2 for ANMB 670 − 720 )than for DART (RMSE = 7.5 μ g · cm − 2 for ANCB 670 − 720 ;RMSE = 23 μ g · cm − 2 for ANMB 670 − 720 ). Although these results show the potential for simpler models like PARAS in estimating needle-lea

Coniferous needle-leaves, shots and canopies : a remote sensing approach

Coniferous forests are important in the regulation of the Earth’s climate and thus continuous monitoring of these ecosystems is crucial to better understand potential responses to climate change. Optical remote sensing (RS) provides powerful methods for the estimation of essential climate variables and for global forest monitoring. However, coniferous forests represent challenging targets for RS methods, mainly due to structural features specific for coniferous trees (e.g. narrow needle leaves, shoot clumping) whose effects on the RS signal are not yet known or not yet fully understood. Recognizing the need for a better adaptation of RS methods to such spatially heterogeneous and structurally complex canopies, this thesis contributes to improving the interpretation of the remotely sensed optical signal reflected from coniferous stands by focusing on specific knowledge gaps identified in the RS methods at different scales of the coniferous canopies. In addition, it explores the application of approaches that simplify the way the structural complexity of such an environment is tackled when using canopy-level radiative transfer approaches. Three main levels based on the identified gaps were defined for the analysis: (needle) leaf level (chapter 2 and 3); shoot level (chapter 4) and canopy level (chapter 5). At leaf levelthis thesis contributes to minimizing the uncertainties and errors related to leaf optical measuring methods adapted for needle leaves. Although optical properties of coniferous leaves are extensively used in RS approaches (i.e. as input or as validation data), there is only a limited number of techniques available for measuring coniferous leaves. The first focus of this thesis was to review the shortcomings and uncertainties of such methods in order to identify application limits and potential improvements (chapter 2). A review showed that a more standardized measuring protocol was needed, for which measurement uncertainties and errors had to be identified, quantified and preferably removed or minimized. Thus, an experimental set-up improving the original method of Mesarch et al. (1999) was presented (chapter 3), which focused on analyzing uncertainties caused by the presence of the sample holder and by the multiple scattering triggered by both the shape of the specific needle cross-section, and the distance between the needles composing a sample. Results showed that both the sample holder and the multiple scattering, triggered specially by the shape of the non-flat cross section of the coniferous needle-leaves, had a non-negligible effect on the optical signal when measured using a standard spectroradiometer coupled to a single-beam integrating sphere and following the method suggested by Mesarch. Thus, approaches designed to measure optical properties of non-flat coniferous needle samples more comprehensively should take into account these effects in their current signal correction algorithms. Needle clumping into shoots quickly transforms the optical signal making the description of the canopy radiative transfer a complex task and encouraging the search for simplified yet robust approaches. Thus, subsequent steps in this thesis focus on one such simplified approach, known as the recollision probability theory (“p-theory”), applied at two hierarchical levels, i.e., shoots (Chapter 4) and the whole canopy (Chapter 5).At shoot level, an empirical verification of the relationship between the photon recollision probability and a structural parameter called STAR was investigated. The approach allows upscaling needle albedo to shoot albedo and was previously theoretically tested only (chapter 4). For this analysis empirical optical measurements of Scots pine needles and shoots were used. Results showed that the approach works well for the VIS and SWIR spectral regions. However, it was less accurate for the NIR and also for sparse shoots (STAR Finally, accurate modelling of the reflectance signal at canopy levelfor coniferous canopies requires realistic representations of the forest stands, which in general implies a large number of input parameters and computationally demanding algorithms. Radiative transfer modelling based on the photon recollision probability offers an alternative for a simplified definition of the forest canopy structure. The performance of such approach for estimation of the leaf chlorophyll content from satellite imaging spectroscopy data acquired by the CHRIS-PROBA sensor was investigated. The approach was compared to a computationally more demanding one based on a detailed 3D structural description of a forest (chapter 5). For this purposes two canopy models, PARAS and DART, representing the first and second approach respectively, were used. Top-of-canopy bidirectional reflectance factors (BRF) were simulated for both models and used to calculate two optical indices, ANCB670–720 and ANMB670–720.Subsequently, the empirical relationships established between the optical indices and the needle-leaf chlorophyll content (Cab) were applied to the CHRIS-PROBA image of a Norway spruce forest stand to retrieve a map of Cab estimates. Results showed that for the spatial resolution of CHRIS-PROBA (17 m), the simpler model PARAS can be applied to retrieve plausible needle-leaf Cab estimates from satellite imaging spectroscopy data with less intensive model parameterization and reduced computational powerthan when using a model like DART. The ANMB670–720 optical indexwas more robust andresulted in a linear relationship between the Cab estimated by both models. This relationship showed, however, a systematic offset that is potentially caused by differences in the implementation of woody elements in each model or by a different parameterization of leaf optical properties. Thus, further investigation on the impact of parameterization differences related to the needle optical properties and the implementation of woody elements in such a model is recommended.</p

Estimation of spruce needle-leaf chlorophyll content based on DART and PARAS canopy reflectance models

Needle-leaf chlorophyll content (Cab) of a Norway spruce stand was estimated from CHRIS-PROBA images using the canopy reflectance simulated by the PROSPECT model coupled with two canopy reflectance models: 1) discrete anisotropic radiative transfer model (DART); and 2) PARAS. The DART model uses a detailed description of the forest scene, whereas PARAS is based on the photon recollision probability theory and uses a simplified forest structural description. Subsequently, statistically significant empirical functions between the optical indices ANCB670-720 and ANMB670-720 and the needle-leaf Cab content were established and then applied to CHRIS-PROBA data. The Cab estimating regressions using ANMB670_720 were more robust than using ANCB670-720 since the latter was more sensitive to LAI, especially in case of PARAS. Comparison between Cab estimates showed strong linear correlations between PARAS and DART retrievals, with a nearly perfect one-to-one fit when using ANMB670-720 (slope = 1.1, offset = 11 µg · cm-2). Further comparison with Cab estimated from an AISA Eagle image of the same stand showed better results for PARAS (RMSE = 2.7 µg · cm-2 for ANCB670-720; RMSE = 9.5 µg · cm-2 for ANMB670_720) than for DART (RMSE = 7.5 µg · cm-2 for ANCB670-720; RMSE = 23 µg · cm-2 for ANMB670-720). Although these results show the potential for simpler models like PARAS in estimating needle-leaf Cab from satellite imaging spectroscopy data, further analyses regarding parameterization of radiative transfer models are recommended

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Drylands include all terrestrial regions where the production of crops, forage, wood and other ecosystem services are limited by water. These ecosystems cover approximately 40% of the earth terrestrial surface and accommodate more than 2 billion people (Millennium Ecosystem Assessment, 2005). Moreover, the interannual variability of the global carbon budget is strongly regulated by vegetation dynamics in drylands. Understanding the dynamics of such ecosystems is significant for assessing the potential for and impacts of natural or anthropogenic disturbances and mitigation planning, and a necessary step toward enhancing the economic and social well-being of dryland communities in a sustainable manner (Global Drylands: A UN system-wide response, 2011). In this research, a combination of remote sensing, field data collection, and ecosystem modeling were used to establish an integrated framework for semi-arid ecosystems dynamics monitoring. Foliar nitrogen (N) plays an important role in vegetation processes such as photosynthesis and there is wide interest in retrieving this variable from hyperspectral remote sensing data. In this study, I used the theory of canopy spectral invariants (AKA p-theory) to understand the role of canopy structure and soil in the retrieval of foliar N from hyperspectral data and machine learning techniques. The results of this study showed the inconsistencies among different machine learning techniques used for estimating N. Using p-theory, I demonstrated that soil can contribute up to 95% to the total radiation budget of the canopy. I suggested an alternative approach to study photosynthesis is the use of dynamic global vegetation models (DGVMs). Gross primary production (GPP) is the apparent ecosystem scale photosynthesis that can be estimated using DGVMs. In this study, I performed a thorough sensitivity analysis and calibrated the Ecosystem Demography (EDv2.2) model along an elevation gradient in a dryland study area. I investigated the GPP capacity and activity by comparing the EDv2.2 GPP with flux towers and remote sensing products. The overall results showed that EDv2.2 performed well in capturing GPP capacity and its long term trend at lower elevation sites within the study area; whereas the model performed worse at higher elevations likely due to the change in vegetation community. I discussed that adding more heterogeneity and modifying ecosystem processes such as phenology and plant hydraulics in ED.v2.2 will improve its application to higher elevation ecosystems where there is more vegetation production. And finally, I developed an integrated hyperspectral-lidar framework for regional mapping of xeric and mesic vegetation in the study area. I showed that by considering spectral shape and magnitude, canopy structure and landscape features (riparian zone), we can develop a straightforward algorithm for vegetation mapping in drylands. This framework is simple, easy to interpret and consistent with our ecological understanding of vegetation distribution in drylands over large areas. Collectively, the results I present in this dissertation demonstrate the potential for advanced remote sensing and modeling to help us better understand ecosystem processes in drylands

Three dimensional estimation of vegetation moisture content using dual-wavelength terrestrial laser scanning

PhD ThesisLeaf Equivalent Water Thickness (EWT) is a water status metric widely used in vegetation health monitoring. Optical Remote Sensing (RS) data, spaceborne and airborne, can be used to estimate canopy EWT at landscape level, but cannot provide information about EWT vertical heterogeneity, or estimate EWT predawn. Dual-wavelength Terrestrial Laser Scanning (TLS) can overcome these limitations, as TLS intensity data, following radiometric corrections, can be used to estimate EWT in three dimensions (3D). In this study, a Normalized Difference Index (NDI) of 808 nm wavelength, utilized in the Leica P20 TLS instrument, and 1550 nm wavelength, employed in the Leica P40 and P50 TLS systems, was used to produce 3D EWT estimates at canopy level. Intensity correction models were developed, and NDI was found to be able to minimize the incidence angle and leaf internal structure effects. Multiple data collection campaigns were carried out. An indoors dry-down experiment revealed a strong correlation between NDI and EWT at leaf level. At canopy level, 3D EWT estimates were generated with a relative error of 3 %. The method was transferred to a mixed-species broadleaf forest plot and 3D EWT estimates were generated with relative errors < 7 % across four different species. Next, EWT was estimated in six short-rotation willow plots during leaf senescence with relative errors < 8 %. Furthermore, a broadleaf mixed-species urban tree plot was scanned during and two months after a heatwave, and EWT temporal changes were successfully detected. Relative error in EWT estimates was 6 % across four tree species. The last step in this research was to study the effects of EWT vertical heterogeneity on forest plot reflectance. Two virtual forest plots were reconstructed in the Discrete Anisotropic Radiative Transfer (DART) model. 3D EWT estimates from TLS were utilized in the model and Sentinel-2A bands were simulated. The simulations revealed that the top four to five metres of canopy dominated the plot reflectance. The satellite sensor was not able to detect severe water stress that started in the lower canopy layers. This study showed the potential of using dual-wavelength TLS to provide important insights into the EWT distribution within the canopy, by mapping the EWT at canopy level in 3D. EWT was found to vary vertically within the canopy, with EWT and Leaf Mass per Area (LMA) being highly correlated, suggesting that sun leaves were able to hold more moisture than shade leaves. The EWT vertical profiles varied between species, and trees reacted in different ways during drought conditions, losing moisture from different canopy layers. The proposed method can provide time series of the change in EWT at very high spatial and temporal resolutions, as TLS instruments are active sensors, independent of the solar illumination. It also has the potential to provide EWT estimates at the landscape level, if coupled with automatic tree ii segmentation and leaf-wood separation techniques, and thus filling the gaps in the time series produced from satellite data. In addition, the technique can potentially allow the characterisation of whole-tree leaf water status and total water content, by combining the EWT estimates with Leaf Area Index (LAI) measurements, providing new insights into forest health and tree physiology.Egyptian Ministry of Higher Educatio