networking friday remote sensing applications in ocean ...€¦ · the use of hyperspectral data:...

Networking Friday

Remote Sensing Applications in Ocean Monitoring, Corals and Mangrove Studies

MILTON KAMPEL, [email protected]

Networking Friday, São José dos Campos, May 15th, 2020

What brings me here today?• Much of the research effort undertaken in recent years has focused on the use of Earth

Observation data associated with the development and validation of (ocean) remote sensing products for various applications.

• The basic idea here is to comment on some EO/RS activities of INPE´s MOceanS Lab., tocomment on some relevant aspects in this field, evaluate the limitations that still exist, tofurther expand international partnerships, collaborations and personnel training at aninternational level helping AIR Centre network stakeholders to stay active, connected,informed and inspired.

2 AIR Centre Networking Friday, May 15th, 2020

Outline• Who we are? What we do?

• Remote Sensing of Coral Reefs/Benthic Mapping.• Metocean Monitoring.

• Land-Ocean Interactions.

• Remote Sensing of Mangroves.

MissionTo develop, operate and use space systems for the advancement of science, technology and applications in the areas of outer space and Earth environment, and offer innovative products and services for the benefit of Brazil.

Institutional Presentation


http://www.inpe.br/institucional/sobre_inpe/instalacoes.php#sede35

Space & Atmospheric Science Weather & Climate

Space Technology Earth System Sciences

Satellite Tracking & Control

Integration & Testing Lab.Associated Laboratories

Earth Observation

http://www.inpe.br/institucional/pesquisa_desenvolvimento/

http://www.inpe.br/institucional/sobre_inpe/missao.php

Staff 1,000 people + ~600 students + [post-docs + other fellowships]


MUX (20 m)WFI (64 m)

PAN (5 m)

CBERS-4

MUX (20 m)

Cameras MUX WPM WFI

Band 1 0.45 - 0.52 µm 0.45 - 0.52 µm 0.45 - 0.52 µm

Band 2 0.52 - 0.59 µm 0.52 - 0.59 µm 0.52 - 0.59 µm

Band 3 0.63 - 0.69 µm 0.63 - 0.69 µm 0.63 - 0.69 µm

Band 4 0.77 - 0.89 µm 0.77 - 0.89 µm 0.77 - 0.89 µm

Band 5 (PAN) 0.45 - 0.90 µm

Resolution 16 m 2 m, 8 m 55 m

CBERS-04A


Sun synchronous orbit

Altitude = 628 km

Inclination = 97.89º

Revisiting rate = 31 days

Descending node at 10h30 local time

Launch: 20th Dec. 2019

Cameras MUX WPM WFI

Manufacturer Brazil China Brazil

Type Push broom Push broom TDI Push broom

Revisiting rate 31 days 31 days 5 days

Quantization 8 bits 10 bits 10 bits

Swath 95 km 92 km 684 km


CBERS 04A

WFI (55 m)MUX (16 m)

WPM (2 m) WPM (2 m)


01-MAR-2018

Amazonia 1First MMP missionForest monitoringLaunch: 2020 (tbc)

Amazonia 1BMMP missionSABIA-Mar (Oceans)MapSARLaunch: tbd

CubeSats


Center for Weather Forecast and Climate Studies


Earth Observation

11

It involves scientific and technological knowledge in the fields of remote sensing and geoinformatics, survey of natural resources and monitoring of the environment. It conducts research, development and applications in the fields of Remote Sensing and Digital Image Processing (83 employees: 80% PhD and 15% MSc).

GEOINFORMATICS

SATELLITE MISSIONS&

DATA CENTER

REMOTE SENSING

PROGRAMS

UN SDGs

EDUCATION & DIFFUSION

AIR Centre Networking Friday, May 15th, 2020

NE Regional Center (CRN)

S Regional Center (CRS)

Amazonia Regional Center (CRA)

Education at INPE


INPE’s Environmental Monitoring Systems: Land Cover, Deforestation Alerts and Fire

Courtesy: Maurano, LE


2019 2020


BLUE AMAZON

EEZ

Continental

Shelf

EEZ +

Continental

Shelf

67% Brazilian

TerritoryAnother

Amazone

2,100,000 km2

5,700,000 km2

5,700,000 km2

Would such an approach like this possible or reasonable?


Satellite Remote Sensing• Collecting and interpreting

information about the environment and the surface of the Earth from a distance, primarily by sensing radiation that is naturally emitted or reflected by the Earth’s surface or from the atmosphere, or by sensing signals transmitted from a device and reflected back to it.

• Examples of remote-sensing methods include aerial photography, radar, and satellite imaging.


Corals/Reefs Mangroves Aquaculture Algae blooms Climate Health

Marine SpatialMapping

Bays & Estuaries

Wind PowerAir-Sea Ocean ColourFisheries Inland waters Sat Missions

Oil & Gas

Land-Ocean

Electromagnetic Spectrum


Robinson 2010

Methods and Types of Sensors

Coral Reefs Studies• Coral reefs are the most biodiverse

and productive ecosystems in marine environments.

• Human activity and natural climate trends constitute a major threat to coral reefs worldwide.

• Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future.

• In this context, monitoring programs to detect changes in reef ecosystems are essential.


https://www.nature.org/en-us/what-we-do/our-priorities/protect-water-and-land/land-and-water-stories/8-easy-ways-you-can-help-coral-reefs/

Coral Reefs Studies by RS


https://www.geospatialworld.net/news/satellite-mount-lidar-sensor-helps-researchers-develop-new-understanding-planktons/

https://eos.com/sentinel-2/Photos: MOceanS Lab.

• Remote sensing approaches to acquiring data in coral reef ecosystems are the most cost-effective and allow for synoptic monitoring of large areas, including places with difficult access.

• In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities.

RS Data Sources


Passive Sensors

Active Sensors

Purkins & Roelfsema, 2015

RS in the Visible


Good water penetration capacity

HyperspectralSensors

MultiespectralSensors

Bottom types

Bathymetry

Chlorophyll

Suspended Matter

Water Quality

Several studies

Ground truth

Visible Spectrum

Some Potentialities and Limitations• Bottom reflectance (ρb) is the

central parameter in the remote sensing of coral reefs and, depends on the physical structure and chemical substrate composition.

• Although RS has a great potential in studies of the sea bottom, extracting the reflectance spectrum from the data of orbital optical sensors is complex.

• Several processes affect the satellite signals.


Phinn et al. 2012

Physical Processes in Water Column

a. Absorption coefficients spectra (m-1) measured in a productive oceanic environment (1 mg m-3 chl-a): Total absorption (black), TSM absorption (red), water molecules (blue), phytoplankton (green), and CDOM (yellow).

b. Absorption spectra of phytoplankton (green) and TSM (red) are plotted, for a better visualization.


Kirk 2011

Light Penetration in the Water Column

Light decay modeled along water column expressed as percentage of incident light as function of depth (m).

a. Curves represent different (nm) in an environment considered as Case-1 water, where [chl-a] = 0.01 mg m-3.

b. Light at 400 nm but in different environments: Case-1 waters (chl-a=0.1 mg m-3); French Polynesia Case-1 waters (Kd=0.14 m-1); Case-2 waters in Abrolhos Coral Reef Bank, Brazil (Kd=0.18 m-1); Case-1 waters (chl-a=1 mg m-3); Case-2 waters (chl-a=0.5 mg m-3, Kd=0.3m-1, minerals concentration=0.5 g m-3).


Zoffoli et al. 2014

RS of Submerged Substrates


a. Reflectance of different substrates at 0 m depth, as function of (nm): coral sand (blue), brown algae (red) and green algae (green).

(b-d). Reflectance above water vs. (nm) simulated for the same substrates in clear Case-1 waters (chl-a = 0.05 mg m-3) at different depths: 1, 5, 20 m.

(e-g). Reflectance above water vs. (nm) simulated for the same substrates in Case-2 waters (chl-a: 1 mg m-3; aCDOM (440) = 0.3 m-1; minerals: 1 g m-3) at depths: 1, 5, 20 m.

Zoffoli et al. 2014

Multispectral & Hyperspectral Sensors(a) Reflectance spectra using a very high-

resolution spectral spectrometer –Hyperspectral.

(b) The same thing with a Multispectral spectrometer.


Phinn et al. 2012

Examples of spectral signatures of the same stretch of live coral

1. In situ reflectance from field spectrometry; 2. Simulated surface reflectance with 1.0 m of water column; 3. Reflectance obtained with the CASI-2 hyperspectral sensor; 4. Reflectance (x 10,000) obtained from the QuickBird-2 multispectral orbital sensor.


Phinn et al. 2012

Multispectral sensorsWith different spatial and temporal resolutions


Hedley et al. (2016)

Spatial vs. Temporal Resolution

Consider the type of application of interest.


Hedley et al 2016

Multispectral Application

(a, b) Geomorphological zone; (c, d) mapsof benthic classification. (b, d) show theenlarged area.

The authors applied currentprocessing algorithms toSentinel-2 data from variouslocations on the Great BarrierReef and performed directcomparisons with Landsat -8.

(a, b) RGB composition from originalimages using bands 2, 3 and 4; (c, d)bathymetric map; (e, f) RGB composition inbands 1, 2 and 3.


High resolution (~m) benthic habitat andbathymetric maps derived from WorldView-2satellite images and calibrated with in situobservations.

Study area distributed in an equatorialtransect covering 65,000 km2 of habitatsdominated by coral reefs in 11 countries.

Maps of the reefsof Gizo Island,Solomon Islands.(a) Location ofGizo Island inNew Georgia.b. True-colorWorldView-2 ofthe reefs.c. Bathymetricmap derivedfrom theWorldView-2image calibratedwith in situ data.d. Map of benthichabitat.





Coral Reefs (2020) submitted

Abrolhos Coral Reef Bank (Brazil) mapping using high

spatial resolution WorldView-2 satellite imagery

María Laura Zoffoli1,2*, Milton Kampel1, Robert Frouin3, Thais Andrade Galvão de

Medeiros1

The authors assessed the benefits of using dataacquired by the WV-2 satellite for producing thefirst bottom type map in the ACRB MarineNational Park and for characterizing landscapeecology of reefs distribution.

What is the problem?Resolving the Complexity of Coastal Waters

35 XIX SBSR, Santos, April 2019

• Extensive studies using in-itumeasurements and remote sensing imaging have shown that visible hyperspectral imaging is one of the best available tools to resolve the complexity of the coastal ocean from space.

• There is a need for high spatial (and temporal) resolution in the near costal ocean.

Hyperspectral Sensors


The use of hyperspectral data:• Allows to derive a greater number of classes with higher accuracy using classification

techniques.• Allows to simultaneously separate information on depth, bottom type and water quality

parameters.

Example of resampled coral andmacroalgae reflectance data for:

(a) Blue, green and red LandsatTM bands.

(b) Typical CASI bandconfiguration, with 10 bands.

The dashed lines show high-resolution reflectance spectra of acoral (Porites sp.) and algae(Caulerpa sp.).

Gray bars showing locations andwidths of the bands and solid linesare resampled spectra according toinstrument's bands.

Hedley, 2012

Comparison between (a) the Heron Reefpseudo RGB (b) the HOPE-LUT classificationand (c) the BRUCE classification map. RegionsA and B in (a) highlight the inner lagoon andthe reef, respectively.

Benthic map with higher classification accuracy using hyperspectral remote sensing inversion models in hyperspectral aerial images of Heron Reef, Great Barrier

Accuracy of the five depth classifiers for (a) seagrass and algae; (b) coral classes.

Hyperspectral Application


Hyperspectral Application


Evolution of the coral reef habitat classesbetween 2009 (1) and 2015 (2) for threezones (A, B and C) of the Saint-Gilles coralreef unit. Left, habitat map for the entireSaint-Gilles flat reef unit in 2015.

Approach based on the analysis ofhigh resolution multispectral andhyperspectral images.

Detect and quantify changes inbenthic cover on a highlyheterogeneous reef in thesouthwest Indian Ocean.

Thermal Infrared

• Passive detection of radiationemitted by the source object.

• Allows to derive the surface seatemperature (SST) from radiometricobservations in two bands located at~4 µm (night) and 10–13 µm (dayand night).

• Measures the skin temperature ofthe sea surface (~10 µm) - SSTskin.

• Daily global SST maps + temporalcomposites.


Schematic profile of thetemperature of the oceanclose to the surface,showing the skin layerand the depths andmeasurements relevantfor satellite SSTmonitoring.

(https://oceanservice.noaa.gov/facts/sea-surface-temperature.html)

Heron et al. 2012

https://oceanservice.noaa.gov/facts/sea-surface-temperature.html

TIR Applications


Ecological processes associated with a possibleinteraction between warming oceantemperatures and water quality

Dissolved inorganic nitrogen (DIN) stimulatesthe increase in coral symbiont populations byincreasing metabolic demands during periods ofhigh irradiance and temperature.

High levels of DIN make coral-algae symbiosisless stable leading to bleaching

(a) Historical thermal index based on the typical summermaximum sea surface temperature (TSM). (b) Water qualityindex based on terrestrial runoff rich in (DIN). (c) Thermalstress index based on the maximum SST occurring in anythree-day period. (d) Expected areas of low resistance to coralbleaching (in red).

Scott A. Wooldridge, Terence J. Done

TIR Applications


NOAA Coral Reef Observation program (CRW)has developed a global 5 km daily product suitebased on satellite observations to monitorthermal stress on coral reefs.

SST

SST Anomaly

Bleaching Hot Spots

Degree HeatingWeek (DHW)

Daily Global 5km Satellite Coral

Bleaching Heat Stress Alert Area

(7-day max)

MW - RADAR


• Detection of sea surface roughness → measurement of the backscatter of the signalactively transmitted by the remote sensor.

• Operation during day and night; not much sensitive to cloud cover.

• Backscatter parameters → indicate ocean state → transmission time and return ofradar pulse frequency change, phase difference and polarity change.

• Monitoring of the environmental conditions around coral reefs.

High Frequency (HF) and Very High Frequency (VHF)

Ground-wave Radar

Heron et al. 2012

Synthetic Aperture Radar (SAR)

HF RADAR• HF radars → remotely measure

surface currents, exploring Bragg'sresonant backscatter phenomenon.

• Networks of HF radar systems →map surface currents every hour inranges of ~ 200 km (possibly more),with horizontal resolution of ~ km.

a) Detection and monitoring of oilspills on the sea surface;

b) Evaluation of the connectivity oflarvae populations in near-realtime.


Doppler spectrum of an ocean surface current radar system (OSCR) operating at 25.4 MHz.

HF-RADAR Application


✓ Assessment of connectivity betweenMarine Protected Areas (MAPs) alongthe central California coast.

✓ Network of HF Radar stations forocean currents mapping.

✓ The authors computed 40-dayreversed trajectories during 1 yearbetween potential regions of larvalorigin and destinations withinestablished MAPs.

Maps of the hourly back-projections of waterparticles, out through 40 days (960 hours) in thepast, for each MPA region; repeated daily for each 1-km grid-point in the given MPA with forcing fromsurface currents measured by HF-radar during 2008These water particle track-points are color coded perthe legend in panel #10 to show the travel time (upthrough 40 days) the waters took to reach the MPAs.

Synthetic Aperture Radar (SAR)


✓Emit pulses of electromagnetic radiationat wavelengths of ~cm and detectbackscattered radiation from the Earth'ssurface.

✓Register the phase and amplitude ofreturned signal.

✓Oceanic phenomena that affect seasurface roughness on a small scale canproduce a detectable signal in a SARimage.

✓As e.g., wind, waves, oil slicks andseeps, other surfactants, upwelling,shallow-water bathymetry

✓With this set of parameters and relatively highspatial resolution, the use of SAR data can beuseful in monitoring environmental conditionsaround coral reefs.

Chaturvedi, 2019

Heron et al. 2012

SAR Application


Analysis of the potential of SAR images(RADARSAT-1) to detect coral reefsexposed in the Environmental ProtectionArea of Costa dos Corais, Brazil.

Differences in processes such as radarwaves interact with the surface of the seaand with emerging reefs.

1. Smooth sea surfaces are specularreflectors - little energy is returned tothe sensor - dark tones in the images.

2. Coral reefs have a more diffusescattering - greater amounts of energyreturn to the sensor - intermediateshades of gray in the images.

A) Raw RADARSAT-1 image; B) Filtered SAR image (3x3 Kuan filter)

SAR Application


Corals spawn at night, usuallyin calm environmentalconditions. These conditionscan potentially be predictedwith relative reliability.

(a) SAR features on 25/OCT/1998 and outlines ofthe features on 16/APR/1998. (b) Multibeambathymetry of the area showing the submarinechannels underlying the features in the SAR image.

Features observed in SAR image of 16/APR/1998on: (a) Vulcan and Goeree Shoals; (b) BarracoutaShoal; (c) Demarcation of the feature on VulcanShoal and isobathimetric contours obtainedfrom a nautical chart.

SAR features observed oncarbonate reefs in the TimorSea were interpreted ascaused by a coral spawningevent.

LIDAR (Light Detection and Ranging)


✓Emits laser beams at different wavelengths. Common inthe near infrared band. Sensors used for bathymetryoperate at two frequencies.

✓Installed on board manned and unmanned platforms.

✓Allows the generation of 3D-Digital Elevation Models.

✓Provides useful information to improve our knowledgeof the functional relationships betweengeomorphological structure and ecological processes inthe marine environment.

✓Provides support for coastal research and mappingefforts.

✓More specifically, it contributes to the understanding ofthe three-dimensional geomorphology of coral reefs NASA Experimental Advanced Airborne Research Lidar (EAARL).

(Brock et al. 2004)

Purkis and Brock, 2012

LIDAR Application


High-resolution Scanning Hydrographic Operational AirborneLidar Survey (SHOALS) → Used to define the morphology ofspur-and-groove structures on the fringing reef off the southcoast of Molokai, Hawaii.

Example of bathymetric data derived fromSHOALS. (A) Visualization on a shaded reliefmap of the SHOALS bathymetry. (B) Exampleof a bathymetric profile parallel to the coastalong the 10 m isobath.

Morphology of the reef and the island platform south of Molokai fromSHOALS bathymetric data and from the National Ocean Serviceoverlaid with the location of 36 transects used in the analysis.

LIDAR Application


NASA Experimental Advanced Airborne Research Lidar(EAARL)→ Used to measure the topography of shallow reefsubstrates, on a sub-metric scale.

Map depicting color-codedspot NAVD88 referencedelevations over Alina Reef(highlighted spots populatethe LIDAR raster transectselected to demonstrate theanalysis).

Wire mesh plot depicting a digitalelevation model created for AlinaReef (site e584207_n2807950_17)based upon the NASA EAARLsurvey that was conducted on 5August 2002. The viewing angle isoblique and from the southwest

LIDAR Application


Digital (a) surface, (b) intensity (532 nm wavelength),and (c) coral reef state classification derived frombathymetric LiDAR soundings (40,364 × 34,588 pixelsat 0.5 m pixel size).

A VHR airborne bathymetric LIDAR system was used tocompute the coral reef ecosystem´s surface andreflectance in combination with a multispectral cameramounted on a drone that produced a BGRorthorectified.A coral reef ecological map was generated for the firsttime at submeter scale in the lagoon of Moorea Island,French Polynesia.Five ecological states were classified through an ANNcalibrated with 275 samples to determine the class ofcoral state with satisfactory accuracy.

Spatio-temporal characterization of satellite-derived surface fields in the Abrolhos Coral Reef Bank


Sea Surface Temperature


SST


CoroaVermelha

Parcel dasParedes

Parcel deAbrolhos

Raw data AnomaliesDecadal average

Timbebas

Chlorophyll-a Concentration


CHL


CoroaVermelha

Parcel dasParedes

Parcel deAbrolhos

Raw data AnomaliesDecadal average

Timbebas

Surface Ocean Wind Vectors


OWV


Mixed Layer Depth


Euphotic Depth


MLD vs. ZEU Dynamics


Net Primary Production


Environmental Characterizations


SST - Campos CHL - AMBES ZEU - Santos MLD - SEAL

OWV - SEAL PPO - Santos


Chlorophyll-a

Ocean Wind Vectors

Precipitation

Operational MetOcean MonitoringOriginal Algorthm 2.5C Adjusted Algorthm 0.9C

Algorithms Cal/Val

Frontal Systems

Wind PPT

Sea Surface Temperature

Environmental monitoring –Bays and Estuaries

• Eutrophication index• Environmental studies• Surface circulation

NPP/VIIRS – 20/APR/2019 AVHRR/MetOp – 5/JUN/2019

ABI/GOES16– 2/FEB/2019 ASCAT/MetOpA– 2/FEB/2019 • Water mass indicator• Surface circulation• Water renewal

•Accumulated precipitation in watersheds

• Synoptic monitoring• Cold fronts passages• Atm. instabilities• Navigation


66

VIIRS– 12/AUG/2018SENTINEL 1 – 12/AUG/2018

Synergy –MultiSensors

Monitoring of coastal plumes and efluents

Sentinel2 – Landsat8 – CBERS4

Land Cover – Land Use, Urban Expansion, Vegetation Coverage


LANDSATTime series with ~300 images of Guanabara Bay

Processing showing the expansion of the urban area (in red) between 1983 (left) and 2018 (right)

RS of Mangroves

Mangroves are coastal ecosystems typical of tropical and subtropical regions subject to the tidal regime.

Considered important sources of support for the conservation of terrestrial and marine biodiversity, providing habitat, spawning grounds, nurseries and food for various animals.


Photos: Moceans Lab.

Distribution and Diversity of Mangrove Species


(World Atlas of Mangroves, Spalding et al. 2010)

Total area ~ 150,000 km² extension of mangroves

1st Indonesia 31,894 km² (20.9%)2nd Brazil with 13,000 km² (8.5%)3rd Australia with 9,910 km² (6.5%)

Mangrove Species


Laguncularia racemosa (mangue branco)

Rhizophora mangle(mangue vermelho)

Avicennia schaueriana(mangue preto)

Photos: Moceans Lab.Acrostichum aureumHibiscus pernambucensis Spartina alterniflora

Global Map of Mangroves Above Ground Biomass


The structural development of mangrove forests depends on the intensity and periodicity of numerous environmental factors.Regions with a topography subject to a wide range of tides, wide freshwater input by effluents, abundant precipitation, insolation, nutrients and sediments provide maximum structural development of mangroves.

(Hutchison et al. 2014)

AGB estimates are important for estimating mangrove productivity, determining the stock and cycling of elements in this ecosystem, forest carbon, estimating the degree of maturity, structural development and stress level.

Below Ground Biomass


Mangroves fix and store hugeamounts of carbon in the soil(49% to 98%).

Donato et al. 2011Photos: Moceans Lab.

Threatened Ecosystem


Population growth in coastal areas has led tomajor disturbances in mangroves, especiallywith deforestation and shrimp farming,which pose serious threats.One third of the Earth's mangrove forestshave already been destroyed, mainly byincreasing human population density.

Photos: Moceans Lab.

RS of Mangroves


Each type of data has advantages and disadvantages.Making it possible to obtain specific information according to the objective of each study: biomass estimation, change detection, mapping, species discrimination.

Active Systems

LIDAR SAROPTICAL IMAGERY

Passive System

Spectral Signature – VIS/NIR/SWIR


Relative Spectral Radiative Response


WorldView-2 Relative Radiative Response (nm)

Each sensor captures the energy reflected from thetarget within a wavelength range (bands).

Normalized Difference Vegetation Index


✓ Observation of the difference in reflectance of plants between the red and near infrared bands.

✓ Plant leaf pigments like chlorophyll absorb visible light (0.4-0.7μm) during photosynthesis.

✓ The cellular structures of the leaf reflect in the infrared (0.7-1.1 μm).

✓ This index can be associated with biophysical parameters of vegetation, such as biomass.

SAR System


Rosenqvist et al. (2007)

L-band Polarimetric Synthetic Aperture Radar System (PALSAR)

✓ Active system: generates its own electromagnetic energy that is transmitted from the sensor to the surface, interacting with the surface and producing a backscatter of the energy that returns to the sensor.

✓ Operates in the microwave region.✓ Data acquisition regardless of weather

conditions.

SAR System


The backscatter coefficient (σ˚) determines the amount of electromagnetic energy reflected by a resolution cell for the radar.

Types of surface-scattering associations: (a) flat-specular, (b) flat-corner reflection, (c) rough-diffuse

The intensity of energy returned tothe radar can vary depending onthe sensor and target parameters:

WavelengthPolarization (HH, VV, VH, HV)Incidence angle

Dielectric constantRoughnessGeometry of acquisition

Incident Wave Penetration Capability


The penetration of the electromagnetic wave varies dependingon the wavelength and dielectric properties of the target

Banda X: λ= 3 cm

Banda C: λ= 5 cm

Banda L: λ= 23 cm

Banda P: λ= 75 cm

Small branches and leaves

Branches, trunks and soil

SAR & Vegetation


Mechanism and behavior of forest backscatter:

A. Canopy surface backscatteringB. Interior canopy scattering, volumetricC. Canopy-soil interactionD. Double-bounce, trunk-soil interactionE. Trunk scatteringF. Soil scattering

-1st Return

-Last Return(X, Y, Z)

-2nd Return

LIDAR


✓ Light Detection and Ranging (LIDAR) is an activesensor that emits optical beams towards theground and receives the backscattered laserportion.

✓ With the synergistic use of GPS and inertialmeasurement (IMU) systems, the exact locations(x, y) and aircraft movements (roll, pitch and yaw)are documented at the time the pulse is sent andreceived.

✓ The system measures the time of the pulse pathemitted and received by the sensor.

✓ Calculates the distance covered by the returns.

✓ Making it possible to estimate the height of thevegetation.

LIDAR


It makes it possible to obtain the height of the vegetationwith high precision and thereby estimate the AGB.

LIDAR Point CloudDescriptive Metrics Digital Terrain Model

Canopy Height Model

LIDAR AGB Estimation


Auto-PLS model

AGB = 561,374.0 tonsArea of 5,432.0 hectaresAverage AGB of 103.4 t / ha.

Quantitative and qualitative information on the current biomass of the mangrove forest in the Guapimirim EPA.Carbon estimates.Contributing to decision making and formulation of public policies within the scope of coastal management and REDD+

Pereira et al. 2018


Classification of mangrove cover

PALSAR L-band image, HH HV VH VV polarization and incoherent attributes

Frequency-based contextual classification

Accuracy > 80%


Distinguish physiographic types of mangrove forests anddifferent degrees of structural development.

Physiographic mangrove types: BasinProgradation MatureGap


• Researchers: Milton Kampel, Natalia Rudorff Oliveira, Rosio Camayo

• Post-Docs: Aline M. Valerio, Francisca R. Pereira, Fabio Dall Cortivo, Amalia Detoni, Francisca Pereira, Thais Menezes

• PhD students: Philipe R. Leal, Andrea L. Oliveira, Gabriel Moiano

• MSc students: Vitor Galazzo, Raissa Teixeira, Gabriel Lucas

• BSc students: Elias R. Krausz• Former students: Maria Laura Zoffoli, Joao Felipe

C. Santos, Lucas B. Freitas, Larissa Valerio, Jean F. Silva, Gustavo Molleri, Leandro R. Freitas, FredericoRudorff

• Former Collaborators: Marcelo Franco, CaioFonteles, Catarina Cecilio, Soyla Moraes, Elaine Oliveira

• CPTEC/INPE: Luiz Augusto Machado, RogerioBatista, Mario Figueiredo, Ricardo Braga, Guilherme Andrade Monitoring Oceans from Space

[email protected]


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