Mud bank colonization by opportunistic mangroves: A case study from French Guiana using lidar data

Mud bank colonization by mangroves on the Amazon-influenced coast of French Guiana was studied using light detection and ranging (lidar) data which provide unique information on canopy geometry an sub-canopy topography. The role of topography was assessed through analysis of vegetation characteristics derived from these data. Measurements and analyses of mangrove expansion rates over space and time led to the identification of two distinct colonization processes. The first involves regular step-by-step mangrove expansion to the northwest of the experimental site. The second is qualified as ‘opportunistic’ since it involves a clear relationship between specific ecological characteristics of pioneer Avicennia and mud cracks affecting the mud bank surface and for which probabilities of occurrence were computed from terrain elevations. It is argued from an original analysis of the latter relationship that mud cracks cannot be solely viewed as water stress features that reflect desiccation potentially harmful to plant growth. Indeed, our results tend to demonstrate that they significantly enhance the propensity for mangroves to anchor and take root, thus leading to the colonization of tens of hectares in a few days. The limits and potential of lidar data are discussed with reference to the study of muddy coasts. Finally, the findings of the study are reconsidered within the context of a better understanding of both topography and vegetation characteristics on mangrove-fringed muddy coasts

Deriving forest canopy parameters for backscatter models using the AMAP architectural plant model

International audienceA new approach using an architectural plant model to feed various theoretical scattering models is presented as a better interpretation of future remote sensing data acquired over natural media. The method is based on the architectural plant model (AMAP), which integrates knowledge of botanical growth processes and real plant measurements. AMAP is encapsulated in a flexible interface software called AMAP2SAR that allows one to (1) simulate a three-dimensional (3-D) plant such as a tree, (2) transform the tree into a collection of cylinders, and (3) feed theoretical models such as radiative transfer (RT) models. The method is illustrated by an example of Austrian black pine plantations in southern France. Simulated characteristics of black pines are validated for stands up to 50 years old and for a given environment. The results show the ability to derive classical forest parameters as well as those needed for electromagnetic models (such as geometry) as a function of age. Vertical profiles of canopy elements are derived and point out the vertical heterogeneity of the stands after they are 20 years old for parameters having an impact on the backscatter such as diameter and number of branches. Consequently, the crown layer variability with age and canopy depth should be considered in RT models. An RT model is modified in order to take account of accurate canopy descriptions and deal with encouraging modeling results at Cand L-band over the same test site

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

L-Band Multi-Polarization Radar Scatterometry over Global Forests: Modelling, Analysis, and Applications

Spaceborne L-band radars have the ability to penetrate vegetation canopies over forested areas, suggesting a potential for regular and frequent global monitoring of both the vegetation state and the subcanopy soil moisture. However, L-band radar’s sensitivity to both vegetation and ground also complicates the relationship between the radar observations and the ecological and geophysical parameters. Accurate yet parsimonious forward models of the radar backscatter are valuable to building an understanding of these relationships. In the first part of this thesis, a model of L-band multi-polarization radar backscatter from forests, intended for use at regional to global spatial scales, is presented. Novel developments in the model include the consideration of multiple scattering within the dense vegetation canopy, and the application of a general model of plant allometry to mitigate the need for much intensive field data for training or over-tuning towards specific sites and tree species. Aided by our model, in the remainder and majority of the thesis, a detailed analysis and interpretation of L-band backscatter over global forests is performed, using data from the Aquarius and SMAP missions. Quantitative differences in backscatter predicted by our model due to freeze/thaw states, branch orientation, and flooding are partially verified against the data, and fitted values of aboveground-biomass and microwave vegetation optical depths are comparable to independent estimates in the literature. Polarization information is used to help distinguish vegetation and ground effects on spatial and temporal variations. We show that neither vegetation nor ground effects alone can explain spatial variations within the same land cover class. For temporal variations during unfrozen periods, soil moisture is found to often be an important factor at timescales of a week to several months, although vegetation changes remain a non-negligible factor. We report the observation of significant differences in backscatter depending on beam azimuthal angle, possibly due to plant phototropism. We also investigated diurnal variations, which have the potential to reveal signals related to plant transpiration. SMAP data from May-July 2015 showed that globally, co-polarized backscatter was generally higher at 6PM compared to 6AM over boreal forests, which is not what one might expect based on previous studies. Based on our modelling, increased canopy extinction at 6AM is a possible cause, but this is unproven and its true underlying physical cause undetermined. Finally, by making simplifying approximations on our forward model, we propose and explore algorithms for soil moisture retrieval under forest canopies using L-band scatterometry, with preliminary evaluations suggesting improved performance over existing algorithms.</p

Large area forest stem volume mapping using synergy of spaceborne interferometric radar and optical remote sensing: a case study of northeast chin

More than a decade of investigations on the use of the interferometric ERS-1/2 tandem coherence for forest applications have increased the understanding of the behaviour of C-band repeat-pass coherence over forested terrain. It has been shown that under optimal imaging conditions, ERS-1/2 tandem coherence can be used for stem volume retrieval with accuracies in the range of ground surveys. Large-area applications of ERS-1/2 tandem coherence are rare though. One of the main limitations concerning large-area exploitation of the existing ERS-1/2 tandem archives for forest stem volume retrieval is related to the considerable dependence of repeat-pass coherence upon the meteorological (rain, temperature, wind speed) and environmental (soil moisture variations, snow metamorphism) acquisition conditions. Conventional retrieval algorithms require accurate forest inventory data for a dense grid of forest sites to tune models that relate coherence to stem volume to the local conditions. Accurate forest inventory data is, however, a rare commodity that is often not freely available. In this thesis, a fully automated algorithm was developed, based on a synergetic use of the MODIS Vegetation Continuous Field product (Hansen et al., 2002), that allowed the training of the Interferometric Water Cloud Model IWCM (Askne et al., 1997) without further need for forest inventory data. With the new algorithm it was possible to train the IWCM on a frame-by-frame basis and thus to account for the spatial and temporal variability of the meteorological and environmental acquisition conditions. The new algorithm was applied to a multi-seasonal ERS-1/2 tandem dataset covering Northeast China that was acquired between 1995 and 1998 with baselines up to 400 m

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps