Classification of the Gabon SAR Mosaic Using a Wavelet Based Rule Classifier
A method is developed for semi-automated classification of SAR images of the tropical forest.
Africa↗
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The Global Ecosystem Dynamics Investigation: High-Resolution Laser Ranging of the Earth’s Forests and Topography
Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a longsought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6 N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.
Dubayah, Ralph↗
Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar
Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.
Brazilian Amazon↗
Fitting a Two-Component Scattering Model to Polarimetric SAR Data from Forests
Two simple scattering mechanisms are fitted to polarimetric synthetic aperture radar (SAR) observations of forests. The mechanisms are canopy scatter from a reciprocal medium with azimuthal symmetry and a ground scatter term that can represent double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants or Bragg scatter from a moderately rough surface, which is seen through a layer of vertically oriented scatterers. The model is shown to represent the behavior of polarimetric backscatter from a tropical forest and two temperate forest sites by applying it to data from the National Aeronautic and Space Agency/Jet Propulsion Laboratory's Airborne SAR (AIRSAR) system. Scattering contributions from the two basic scattering mechanisms are estimated for clusters of pixels in polarimetric SAR images. The solution involves the estimation of four parameters from four separate equations. This model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem. The model is used to develop an understanding of the ground-trunk double-bounce scattering that is present in the data, which is seen to vary considerably as a function of incidence angle. Two parameters in the model fit appear to exhibit sensitivity to vegetation canopy structure, which is worth further exploration. Results from the model fit for the ground scattering term are compared with estimates from a forward model and shown to be in good agreement. The behavior of the scattering from the ground-trunk interaction is consistent with the presence of a pseudo-Brewster angle effect for the air-trunk scattering interface. If the Brewster angle is known, it is possible to directly estimate the real part of the dielectric constant of the trunks, a key variable in forward modeling of backscatter from forests. It is also shown how, with a priori knowledge of the forest height, an estimate for the attenuation coefficient of the canopy can be obtained directly from the multi-incidence-angle polarimetric observations. This attenuation coefficient is another key variable in forward models and is generally related to the canopy density.
temperate forests↗
Integration of topographic data with synthetic aperture radar data for determining forest properties in mountainous terrain
The elevation gradients affecting tropical forest stand characteristics are presently studied in light of multipolarization airborne SAR data. A 'rubber sheeting' computer code was used to georeference the SAR data sets to the digital elevation data. The TOPO code from NASA's NSTL generated the terrain slope and aspect angle data from the terrain elevation data set; computed local incidence angles were used to delete those data areas that were shadowed, and to produce local incidence angle data that were not shadowed. The results obtained demonstrate that the SAR data are related to the elevation gradient.
Wu, Shih-Tseng↗
Future carbon emissions from global mangrove forest loss
Mangroves have among the highest carbon densities of any tropical forest. These ‘blue carbon’ ecosystems can store large amounts of carbon for long periods, and their protection reduces greenhouse gas emissions and supports climate change mitigation. Incorporating mangroves into Nationally Determined Contributions to the Paris Agreement and their valuation on carbon markets requires predicting how the management of different land-uses can prevent future greenhouse gas emissions and increase CO2 sequestration. We integrated comprehensive global datasets for carbon stocks, mangrove distribution, deforestation rates, and land-use change drivers into a predictive model of mangrove carbon emissions. We project emissions and foregone soil carbon sequestration potential under ‘business as usual’ rates of mangrove loss. Emissions from mangrove loss could reach 2391 Tg CO2 eq by the end of the century, or 3392 Tg CO2 eq when considering foregone soil carbon sequestration. The highest emissions were predicted in southeast and south Asia (West Coral Triangle, Sunda Shelf, and the Bay of Bengal) due to conversion to aquaculture or agriculture, followed by the Caribbean (Tropical Northwest Atlantic) due to clearing and erosion, and the Andaman coast (West Myanmar) and north Brazil due to erosion. Together, these six regions accounted for 90% of the total potential CO2 eq future emissions. Mangrove loss has been slowing, and global emissions could be more than halved if reduced loss rates remain in the future. Notably, the location of global emission hotspots was consistent with every dataset used to calculate deforestation rates or with alternative assumptions about carbon storage and emissions. Our results indicate the regions in need of policy actions to address emissions arising from mangrove loss and the drivers that could be managed to prevent them.
Maria F. Adame↗
Dual-Frequency Interferometric SAR Observations of Tropical Rain-Forests
Repeat-pass, interferometric, radar observations of tropical rain-forests colected by the Shuttle Imaging Radar SIR-C in Rondonia, Brazil reveal C-band (5.6-cm) radar signals completely decorelate over forest, whereas L-band (24-cm) signals remain strongly coherent over the intire landscape. At L-band, the difference in elevation between forest and clearings is within the 8-m height noise of the data. Atmospheric delays are large, however, forming kilometer-sized anomalies with a with a 1.2-cm rms one way. Radar interferometric studies of humid tropics miust be conducted at long radar wavelengths, with kilometric baselines or with two antennas operating simultaneously.
radar↗
Evaluation of Sentinel-1A Data For Above Ground Biomass Estimation in Different Forests in India
Use of remote sensing data for mapping and monitoring of forest biomass across large spatial scales can aid in addressing uncertainties in carbon cycle. Earlier, several researchers reported on the use of Synthetic Aperture Radar (SAR) data for characterizing forest structural parameters and the above ground biomass estimation. However, these studies cannot be generalized and the algorithms cannot be applied to all types of forests without additional information on the forest physiognomy, stand structure and biomass characteristics. The radar backscatter signal also saturates as forest parameters such as biomass and the tree height increase. It is also not clear how different polarizations (VV versus VH) impact the backscatter retrievals in different forested regions. Thus, it is important to evaluate the potential of SAR data in different landscapes for characterizing forest structural parameters. In this study, the SAR data from Sentinel-1A has been used to characterize forest structural parameters including the above ground biomass from tropical forests of India. Ground based data on tree density, basal area and above ground biomass data from thirty-eight different forested sites has been collected to relate to SAR data. After the pre-processing of Sentinel 1-A data for radiometric calibration, geo-correction, terrain correction and speckle filtering, the variability in the backscatter signal in relation tree density, basal area and above biomass density has been investigated. Results from the curve fitting approach suggested exponential model between the Sentinel-1A backscatter versus tree density and above ground biomass whereas the relationship was almost linear with the basal area in the VV polarization mode. Of the different parameters, tree density could explain most of the variations in backscatter. Both VV and VH backscatter signals could explain only thirty and thirty three percent of variation in above biomass in different forest sites of India. Results also suggested saturation of the Sentinel-1A backscatter signal around hundred tonnes per hectare for VV polarization and one hundred and forty five tonnes per hectare for VH polarization. The presentation will highlight the above results in addition to potentials and limitations of Sentinel-1A data for retrieving forest structural parameters. Also, background information on different forest types of India, biomass variations and forest type mapping efforts in the region will be presented.
Data↗
Satellite Observation of El Nino Effects on Amazon Forest Phenology and Productivity
Climate variability may affect the functioning of Amazon moist tropical forests, and recent modeling analyses suggest that the carbon dynamics of the region vary interannually in response to precipitation and temperature anomalies. However, due to persistent orbital and atmospheric artifacts in the satellite record, remote sensing observations have not provided quantitative evidence that climate variation affects Amazon forest phenology or productivity, We developed a method to minimize and quantify non-biological artifacts in NOAA AVHRR satellite data, providing a record of estimated forest phenological variation from 1982-1993. The seasonal Normalized Difference Vegetation Index (NDVI) amplitude (a proxy for phenology) increased throughout much of the basin during El Nino periods when rainfall was anomalously low. Wetter La Nina episodes brought consistently smaller NDVI amplitudes. Using radiative transfer and terrestrial biogeochemical models driven by these satellite data, we estimate that canopy-energy absorption and net primary production of Amazon forests varied interannually by as much as 21% and 18%, respectively. These results provide large-scale observational evidence for interannual sensitivity to El Nino of plant phenology and carbon flux in Amazon forests.
Asner, Gregory P.↗
Dual-Frequency Interferometric SAR Observations of a Tropical Rain-Forest
Repeat-pass, interferometric, radar observations of tropical rain-forest collected by the Shuttle Imaging Radar C (SIR-C) in the state of Rondonia, Brazil, reveal signal coherence is destroyed at C-band (5.6-cm) in the forest, whereas L-band (24-cm) radar signals remain strongly coherent over the entire landscape. At L-band, the rms difference in inferred topographic height between the forest and adjacent clearings is 5 m, equivalent to the height noise. Atmospheric delays are large, however, forming kilometer-sized anomalies with a 1.2-cm rms one way. Radar interferometric studies of the humid tropics must therefore be conducted at long radar wavelengths, with kilometric baselines or with two antennas operating simultaneously.
Rignot, E.↗
Dual-Frequency Interferometric SAR Observations of a Tropical Rain-Forest
Repeat-pass, interferometric, radar observations of tropical rain-forest collected by the Shuttle Imaging Radar C (SIR-C) in the state of Rondonia, Brazil, reveal signal coherence is destroyed at C-band (5.6-cm) in the forest, whereas L-band (24-cm) radar signals remain strongly coherent over the entire landscape. At L-band, the rms difference in inferred topographic height between the forest and adjacent clearings is 5 m, equivalent to the height noise. Atmospheric delays are large, however, forming kilometer-sized anomalies with a 1.2-cm rms one way. Radar interferometric studies of the humid tropics must therefore be conducted at long radar wavelengths, with kilometric base-lines or with two antennas operating simultaneously.
Rigot, E.↗
Remote Characterization of Biomass Measurements: Case Study of Mangrove Forests
Accurately quantifying forest biomass is of crucial importance for climate change studies. By quantifying the amount of above and below ground biomass and consequently carbon stored in forest ecosystems, we are able to derive estimates of carbon sequestration, emission and storage and help close the carbon budget. Mangrove forests, in addition to providing habitat and nursery grounds for over 1300 animal species, are also an important sink of biomass. Although they only constitute about 3% of the total forested area globally, their carbon storage capacity -- in forested biomass and soil carbon -- is greater than that of tropical forests (Lucas et al, 2007). In addition, the amount of mangrove carbon -- in the form of litter and leaves exported into offshore areas is immense, resulting in over 10% of the ocean's dissolved organic carbon originating from mangroves (Dittmar et al, 2006) The measurement of forest above ground biomass is carried out on two major scales: on the plot scale, biomass can be measured using field measurements through allometric equation derivation and measurements of forest plots. On the larger scale, the field data are used to calibrate remotely sensed data to obtain stand-wide or even regional estimates of biomass. Currently, biomass can be calculated using average stand biomass values and optical data, such as aerial photography or satellite images (Landsat, Modis, Ikonos, SPOT, etc.). More recent studies have concentrated on deriving forest biomass values using radar (JERS, SIR-C, SRTM, Airsar) and/or lidar (ICEsat/GLAS, LVIS) active remote sensing to retrieve more accurate and detailed measurements of forest biomass. The implementation of a generation of new active sensors (UAVSar, DesdynI, Alos/Palsar, TerraX) has prompted the development of new tecm'liques of biomass estimation that use the combination of multiple sensors and datasets, to quantify past, current and future biomass stocks. Focusing on mangrove forest biomass estimation, this book chapter has 3 main objectives: a) To describe in detail the field methodologies used to derive accurate estimates of biomass in mangrove forests b) To explain how mangrove forest biomass can be measured using several remote sensing techniques and datasets c) To give a detailed explanation of the measurement challenges and errors that arise in each estimate of forest biomass
Fatoyinbo, Temilola E.↗
The Forest, The Fly, and the Virus?
All known outbreaks of Ebola have been linked to tropical forests. We undertook a study of environmental conditions associated with Ebola hemorrhagic fever after preliminary reports strongly suggested that simultaneous outbreaks occurred, during two limited time periods in the 1970s and 1990s, immediately following sudden transitions between dry and wet seasons.
Tucker, Compton J.↗
Mapping Land Cover Types in Amazon Basin Using 1km JERS-1 Mosaic
During the Global Rain Forest (GRFM) project, JERS-1 SAR satellite was used to map the humid tropical forests of the world.
JERS-1↗
How Did I Get Here?
What country was responsible for much of the early work in laser altimetry, who was the first scientist to look at a vegetation profile generated by a laser, who first related laser ranges to forest structure? The purpose of this paper is to look back 3+ decades ago when forestry lidar was in its formative years and to give credit to those who first thought to employ a laser to measure the Earth's surface. As with much scientific research, advances were often made independently and concurrently in a number of countries. Functioning lasers were first demonstrated in 1960 in the USA and in 1961 in the USSR, but research into the use of lasers as forest measurement tools did not begin for another IS years. Initially, with respect to Earth resources, lasers were employed to measure sea ice surface roughness, to make near-shore bathymetric measurements, to penetrate forests to make detailed topographic measurements, and to fluoresce oceanic phytoplankton for surface current studies. Some of these early studies noted that forest profiles were evident but in fact added noise to topographic retrievals. Trees became the signal rather than noise in 1976 when researchers in the Canadian Forest Service went to an IUFRO conference in Oslo and reported on efforts to better estimate timber volume in tropical forests. They employed large-scale airphotos (1:500 up to 1:4000) to measure the top of the tropical canopy, an avionic radar to measure the location of the ground beneath the canopy, and a barometric sensor to record aircraft/radar height above a datum (e.g., sea level). Their radar did not work well in dense vegetation and mentioned in passing that a laser altimeter had worked well in Canadian forests. But in a report 2 years later they reported that, on a study area in Costa Rica, the radar ground line was much improved and no mention is made of a lidar altimeter. In the USSR in 1977, Russian researchers felled a birch and a spruce, aimed a helium-neon, 0.63 micron laser with a spot size of approx 25mm, at the horizontal trees, produced a profilograph, compared it to tape measurements, and concluded that, with increased power, such a laser could be mounted on an aircraft to remotely measure forest canopies. In 1979, they mounted their He-Ne laser on an AN-2 biplane and acquired their fIrSt airborne profiles. Scientists with TRANARG, a mapping/surveying company in Caracas, Venezuela, reported on 1976 flights employing a helium-neon lidar to collect over 11,000 km of lidar profiles spaced 1.5 km apart in order to construct a topographic map to help site a new reservoir. They noted 35-40 m median canopy heights with emergents up to 55 m in their profiles. These studies and others that utilize these height and canopy density metrics for forest mensuration are reviewed in this glance backwards at the history of forestry lidar.
Nelson, Ross↗
Airborne remote sensing of forest biomes
Airborne sensor data of forest biomes obtained using an SAR, a laser profiler, an IR MSS, and a TM simulator are presented and examined. The SAR was utilized to investigate forest canopy structures in Mississippi and Costa Rica; the IR MSS measured forest canopy temperatures in Oregon and Puerto Rico; the TM simulator was employed in a tropical forest in Puerto Rico; and the laser profiler studied forest canopy characteristics in Costa Rica. The advantages and disadvantages of airborne systems are discussed. It is noted that the airborne sensors provide measurements applicable to forest monitoring programs.
Sader, Steven A.↗
Relating Vegetation Aerodynamic Roughness Length to Interferometric SAR Measurements
In this paper, we investigate the feasibility of estimating aerodynamic roughness parameter from interferometric SAR (INSAR) measurements. The relation between the interferometric correlation and the rms height of the surface is presented analytically. Model simulations performed over realistic canopy parameters obtained from field measurements in boreal forest environment demonstrate the capability of the INSAR measurements for estimating and mapping surface roughness lengths over forests and/or other vegetation types. The procedure for estimating this parameter over boreal forests using the INSAR data is discussed and the possibility of extending the methodology over tropical forests is examined.
Saatchi, Sassan↗
Quantifying Long-Term Changes in Carbon Stocks and Forest Structure from Amazon Forest Degradation
Despite sustained declines in Amazon deforestation, forest degradation from logging and firecontinues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arcof deforestation. Limited data on the magnitude of carbon losses and rates of carbon recoveryfollowing forest degradation have hindered carbon accounting efforts and contributed to incompletenational reporting to reduce emissions from deforestation and forest degradation (REDD+). Wecombined annual time series of Landsat imagery and high-density airborne lidar data to characterizethe variability, magnitude, and persistence of Amazon forest degradation impacts on abovegroundcarbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of thecarbon stocks in intact forests, and differences persisted even after 15 years of regrowth. Incomparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD.Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a historyof one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD,respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires,selective logging before burning did not explain additional variability in modeled ACD loss andrecovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure thatcan aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recoveredfaster than attributes of canopy structure that are critical for biodiversity in tropical forests, includingthe abundance of tall trees. We provide the first comprehensive look-up table of emissions factors forspecific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon lossand recovery trajectories provide an important foundation for assessing the long-term contributionsfrom forest degradation to regional carbon cycling and advance our understanding of the currentstate of frontier forests.
Rappaport, Danielle I.↗