Marc Simard

Mapping Height and Biomass of Mangrove Forests in Everglades National Park with SRTM Elevation Data

We produced a landscape scale map of mean tree height in mangrove forests in Everglades National Park (ENP) using the elevation data from the Shuttle Radar Topography Mission (SRTM). The SRTM data was calibrated using airborne lidar data and a high resolution USGS digital elevation model (DEM). The resulting mangrove height map has a mean tree height error of 2.0 m (RMSE) over a pixel of 30 m. In addition, we used field data to derive a relationship between mean forest stand height and biomass in order to map the spatial distribution of standing biomass of mangroves for the entire National Park. The estimation showed that most of the mangrove standing biomass in the ENP resides in intermediate-height mangrove stands around 8 m. We estimated the total mangrove standing biomass in ENP to be 5.6 � 10 9 kg.

Mapping tropical coastal vegetation using JERS-1 and ERS-1 radar data with a decision tree classifier

The objective of this paper is to investigate the complementarity of JERS-1 and ERS-1 data for mapping coastal tropical regions. We use a decision tree classifier to classify a coastal region of Gabon and describe the feature contribution using the decision tree diagram. The JERS-1 Global Rain Forest Mapping (GRFM) and ERS-1 Central Africa Mosaic Project (CAMP) datasets are used. The result is a land cover map of the west coast of Gabon. The analysis explicitly shows the complementary characteristics of the L- and C-band Synthetic Aperture Radar (SAR) instruments. We demonstrate the usefulness of combined use of L- and C-band data for large area mapping of coastal regions, especially in flooded areas for discrimination of high and low mangroves as well as grasses and tree swamps. The overall classification accuracy increases by 18% over single band classification.

The Global Rain Forest Mapping Project JERS-1 radar mosaic of tropical Africa: development and product characterization aspects

The Global Rain Forest Mapping Project (GRFM) is an international collaborative effort initiated and managed by the National Space Development Agency of Japan (NASDA). The main goal of the project is to produce a high resolution wall-to-wall map of the entire tropical rain forest domain in four continents using the L-band SAR onboard the JERS-1 spacecraft. The processing phase, which entails the generation of wide area radar mosaics from the raw SAR data, was split according to the geographic area. In this paper, the focus is on the part related to Africa. The GRFM projects goal calls for the coverage of a continental scale area of several million km 2 using a sensor with the resolution of tens of meters. In the case of the African continent, this entails the assemblage of some 3900 high resolution SAR scenes into a bitemporal mosaic at 100 m pixel spacing and with known geometric accuracy. While this fact opens up an entire new perspective for vegetation mapping in the tropics, it presents a number of technical challenges. In this paper, we report on the solutions adopted in the GRFM Africa mosaic development and discuss some quantitative and qualitative aspects related to the characterization and validation of the GRFM products. In particular, the mosaic geolocation and its validation are discussed in detail. Indeed, the internal geometric consistency (subpixel accuracy in the coregistration of the two dates), and the absolute geolocation (residual mean squared error of 240 m with respect to ground control points) are key features of the GRFM Africa mosaic. Other important aspects that are discussed are the multiresolution decomposition approach, which allows for tracking the evolution of natural phenomena with scale; the internal semi-automatic radiometric calibration, which minimizes artifacts in the mosaic; and the thematic information content for vegetation mapping, which is illustrated by a few examples elaborated by visual interpretation. Experience gained so far indicates that the GRFM products constitute an important source of information for global environmental studies.

The Role of the Everglades Mangrove Ecotone Region (EMER) in Regulating Nutrient Cycling and Wetland Productivity in South Florida

The authors summarize the main findings of the Florida Coastal Everglades Long-Term Ecological Research (FCE-LTER) program in the EMER, within the context of the Comprehensive Everglades Restoration Plan (CERP), to understand how regional processes, mediated by water flow, control population and ecosystem dynamics across the EMER landscape. Tree canopies with maximum height <3 m cover 49% of the EMER, particularly in the SE region. These scrub/dwarf mangroves are the result of a combination of low soil phosphorus (P < 59 μg P g dw−1) in the calcareous marl substrate and long hydroperiod. Phosphorus limits the EMER and its freshwater watersheds due to the lack of terrigenous sediment input and the phosphorus-limited nature of the freshwater Everglades. Reduced freshwater delivery over the past 50 years, combined with Everglades compartmentalization and a 10 cm rise in coastal sea level, has led to the landward transgression (∼1.5 km in 54 years) of the mangrove ecotone. Seasonal variation in freshwater input strongly controls the temporal variation of nitrogen and P exports (99%) from the Everglades to Florida Bay. Rapid changes in nutrient availability and vegetation distribution during the last 50 years show that future ecosystem restoration actions and land use decisions can exert a major influence, similar to sea level rise over the short term, on nutrient cycling and wetland productivity in the EMER.

Large-scale vegetation maps derived from the combined L-band GRFM and C-band CAMP wide area radar mosaics of Central Africa

Abstract A new dataset has been compiled by combining the wide area Synthetic Aperture Radar (SAR) mosaics over Central Africa generated in the context of the NASDA Global Rain Forest Mapping (GRFM) and the ESA/EC Central Africa Mosaic Projects (CAMP). The CAMP mosaic consists of more than 700 SAR scenes acquired over the Central Africa region (6° S-8° N and 5° E-26° E) by the ESA ERS satellites; the acquisitions were performed in 1994 (July, August) and in 1996 (January, February) in two different seasonal conditions. The GRFM Africa mosaic consists of some 3900 JERS-1 images acquired over the region (10° S-10° N, 14° W and 42° E) at two dates (January-March 1996 and October-November 1996). In this paper the methods used for combining the two wide area radar mosaics are at first presented. The GRFM Africa mosaic was processed using a block adjustment algorithm with the inclusion of external observations derived from high precision maps along the coastline, which assures an absolute geolocation residual mean squared error of 240 m with respect to ground control points. On the other hand, the CAMP mosaic was compiled taking into account only the internal relative geometric accuracy. Therefore the GRFM dataset was taken as the reference system and the C-band ERS layer composed by rectifying each ERS frame, after down-sampling at 100 m pixel spacing, to the reference mosaic. The rectification procedure uses a set of tie-points measured automatically between each ERS frame and the homologous subset in the JERS mosaic. Due to the different characteristics of the two sensors (microwave centre frequency, viewing geometry, polarization) and the different acquisition dates, each mosaic presents a different window over the same ecosystem. This fact suggests that a new dimension in terms of thematic information content can be added by the fusion of the two datasets. In support of this statement, the complementary characteristics of the two sensors are first discussed with respect to observations related to the vegetation cover in the Congo River floodplain. The potential of the combined dataset for vegetation mapping at the regional scale is further demonstrated by a classification pursuit of the main vegetation types in the central part of the Congo Basin. The main land-cover classes are: lowland rain forest, permanently flooded forest, periodically flooded forest, swamp grassland, and savannah. The classification map is validated using a compilation of national vegetation maps derived from other high resolution remote sensing data or by ground surveys. This first thematic result already confirms that the combined contributions from the L-band and the C-band sensors improve the information extraction capability. Indeed, the radar-derived vegetation map contains better spatial detail than any existing map, especially with respect to the extent of flooded formations.

New perspectives on global ecosystems from wide-area radar mosaics: flooded forest mapping in the tropics.

Large floodplains in the tropics, like the Congo river basin in Central Africa, are interesting ecosystems that function as water storage and faunistic and florensis habitat. Moreover, they host a series of bio-chemical processes, such as methane emission, which have a significance in global change issues. Characterization of these complex ecosystems can be tackled from different view points, such as bio-chemistry, geology, climatology, hydrology, geomorphology, floristics and forest structure. In this paper we focus on forest structure aspects and report about an approach for mapping two thematic classes - the swamp forest and lowland rain forest - by radar remote sensing at regional scale and high spatial resolution. The proposed solution hinges on the recent availability of a large radar mosaic acquired over Central Africa wall-to-wall by the Synthetic Aperture Radar instrument on board the ESA ERS-1 satellite. The focal points and main issues of this study are: the global mapping approach, using continuous spatial sampling over the region of interest; the signal processing techniques; the up-scaling to wide area of local area classification and (more critical) validation techniques. Results achieved so far already show that blanket radar coverage of the tropics can provide thematic information on the forest composition of a whole ecosystem at an unprecedented level of detail and accuracy.

The UAVSAR instrument: Description and first results

The UAVSAR instrument, employing an L-band actively electronically scanned antenna, had its genesis in the ESTO Instrument Incubator Program and after 3 years of development has begun collecting engineering and science data. System design was motivated by solid Earth applications where repeat pass radar interferometry can be used to measure subtle deformation of the surface, however flexibility and extensibility to support other applications were also major design drivers. In fact a Ka-band single-pass radar interferometer for making high precision topographic maps of ice sheets is being developed based to a large extent on components of the UAVSAR L-band radar. By designing the radar to be housed in an external unpressurized pod, it has the potential to be readily ported to many platforms. Initial testing is being carried out with the NASA Gulfstream III aircraft, which has been modified to accommodate the radar pod and has been equipped with precision autopilot capability developed by NASA Dryden Flight Research Center. With this the aircraft can fly within a 10 m diameter tube on any specified trajectory necessary for repeat-pass radar interferometric applications. To maintain the required pointing for repeat-pass interferometric applications we have employed an actively scanned antenna steered using INU measurement data. This paper presents a brief overview of the radar instrument and some of the first imagery obtained from the system.

An Empirical Assessment of Temporal Decorrelation Using the Uninhabited Aerial Vehicle Synthetic Aperture Radar over Forested Landscapes

We present an empirical assessment of the impact of temporal decorrelation on interferometric coherence measured over a forested landscape. A series of repeat-pass interferometric radar images with a zero spatial baseline were collected with UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), a fully polarimetric airborne L-band radar system. The dataset provided temporal separations of 45 minutes, 2, 7 and 9 days. Coincident airborne lidar and weather data were collected. We theoretically demonstrate that UAVSAR measurement accuracy enables accurate quantification of temporal decorrelation. Data analysis revealed precipitation events to be the main driver of temporal decorrelation over the acquisition period. The experiment also shows temporal decorrelation increases with canopy height, and this pattern was found consistent across forest types and polarization.

Contribution of L-Band SAR to Systematic Global Mangrove Monitoring

Information on the status of and changes in mangroves is required for national and international policy development, implementation and evaluation. To support these requirements, a component of the Japan Aerospace Exploration Agencys (JAXA) Kyoto and Carbon (KC (2) to quantify changes in the structure and associated losses and gains of carbon on the basis of canopy height and above- ground biomass (AGB) estimated from the shuttle radar topographic mission (SRTM; acquired 2000), the ice, cloud and land-elevation satellite (ICESAT) geoscience laser altimeter system (GLAS; 2003-2010) and L-band backscatter data; (3) to determine likely losses and gains of tree species diversity through reference to International Union for the ConservationofNature(IUCN)globalthematiclayersonthedistributionofmangrovespecies;and(4)tovalidatemapsof changesintheextentofmangroves,primarilythroughcomparisonwithdensetime-seriesofLandsatsensordataandtouse these same data to describe the causes and consequences of change. The paper outlines and justifies the techniques being implementedandtherolethattheGMWmightplayinsupportingnationalandinternationalpoliciesthatrelatespecifically to the long-term conservation of mangrove ecosystems and the services they provide to society.

A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest can provide crucial information for monitoring and verification protocols. We compared four CHMs derived from independent remotely sensed imagery and identified potential errors and bias between measurement types. CHMs were derived from three spaceborne datasets; Very-High Resolution (VHR) stereophotogrammetry, TerraSAR-X add-on for Digital Elevation Measurement, and Shuttle Radar Topography Mission (TanDEM-X), and lidar data which was acquired from an airborne platform. Each dataset exhibited different error characteristics that were related to spatial resolution, sensitivities of the sensors, and reference frames. Canopies over 10 m were accurately predicted by all CHMs while the distributions of canopy height were best predicted by the VHR CHM. Depending on the guidelines and strategies needed for monitoring and verification activities, coarse resolution CHMs could be used to track canopy height at regional and global scales with finer resolution imagery used to validate and monitor critical areas undergoing rapid changes.