We show how moderate resolution data from the Multiangle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System Terra satellite can be interpreted.

Slides:



Advertisements
Similar presentations
SNPP VIIRS green vegetation fraction products and application in numerical weather prediction Zhangyan Jiang 1,2, Weizhong Zheng 3,4, Junchang Ju 1,2,
Advertisements

Evaluating Calibration of MODIS Thermal Emissive Bands Using Infrared Atmospheric Sounding Interferometer Measurements Yonghong Li a, Aisheng Wu a, Xiaoxiong.
Deep Blue Algorithm: Retrieval of Aerosol Optical Depth using MODIS data obtained over bright surfaces 1.Example from the Saharan Desert. 2.Deep Blue Algorithm.
Goal: estimate sub-pixel woody shrub fractional cover at landscape scales Approach: evaluate the Simple Geometric Model (GM) against the 631 nm directional.
Mike Barnsley & Tristan Quaife, UWS. GLC2000, Ispra, March Estimating Land Surface Biophysical Properties using SPOT-4 VGT Mike Barnsley and Tristan.
The Effects of Site and Soil on Fertilizer Response of Coastal Douglas-fir K.M. Littke, R.B. Harrison, and D.G. Briggs University of Washington Coast Fertilization.
SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom.
Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,
The CHRIS/PROBA Jornada Experiment: Exploitation of Data from the CHRIS Mark J. Chopping Department of Earth and Environmental Studies Montclair State.
Extracting Atmospheric and Surface Information from AVIRIS Spectra Vijay Natraj, Daniel Feldman, Xun Jiang, Jack Margolis and Yuk Yung California Institute.
A 21 F A 21 F Parameterization of Aerosol and Cirrus Cloud Effects on Reflected Sunlight Spectra Measured From Space: Application of the.
Xin Kong, Lizzie Noyes, Gary Corlett, John Remedios, Simon Good and David Llewellyn-Jones Earth Observation Science, Space Research Centre, University.
BOSTON UNIVERSITY GRADUATE SCHOOL OF ART AND SCIENCES LAI AND FPAR ESTIMATION AND LAND COVER IDENTIFICATION WITH MULTIANGLE MULTISPECTRAL SATELLITE DATA.
Questions How do different methods of calculating LAI compare? Does varying Leaf mass per area (LMA) with height affect LAI estimates? LAI can be calculated.
ESTEC July 2000 Estimation of Aerosol Properties from CHRIS-PROBA Data Jeff Settle Environmental Systems Science Centre University of Reading.
Important science questions addressed by the NASA Carbon Cycle and Ecosystems program include “How are the Earth’s carbon cycle and ecosystems changing.
October 28, 2004C Pools from EOS MISR & MODIS1 Carbon Pools in Desert Grasslands from EOS: First Meeting Jornada Experimental Range, Las Cruces, NM, October.
Update on MISR applications to Shrub Abundance Mapping in Desert Grasslands Mark Chopping, Lihong Su, Albert Rango, Debra P.C. Peters, John V. Martonchik,
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
What is RADAR? What is RADAR? Active detecting and ranging sensor operating in the microwave portion of the EM spectrum Active detecting and ranging sensor.
Goal: To improve estimates of above- and belowground C pools in desert grasslands by providing more accurate maps of plant community type, canopy structural.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.
Introduction OBJECTIVES  To develop proxies for canopy cover and canopy closure based on discrete-return LiDAR data.  To determine whether there is a.
An Object Oriented Algorithm for Extracting Geographic Information from Remotely Sensed Data Zachary J. Bortolot Assistant Professor of Geography Department.
Chapter 4: How Satellite Data Complement Ground-Based Monitor Data 3:15 – 3:45.
Mapping Forest Canopy Height with MISR We previously demonstrated a capability to obtain physically meaningful canopy structural parameters using data.
MAPPING SNOW AND ICE FROM GEOSTATIONARY SATELLITES: GETTING READY FOR GOES-R Peter Romanov 1,2 and Dan Tarpley 1 1 Office of Research and Applications,
Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Karnieli: Introduction to Remote Sensing
Biomass Mapping The set of field biomass training data and the MODIS observations were used to develop a regression tree model (Random Forest). Biomass.
Abstract: Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor.
Generating fine resolution leaf area index maps for boreal forests of Finland Janne Heiskanen, Miina Rautiainen, Lauri Korhonen,
Maria Val Martin and J. Logan (Harvard Univ., USA) D. Nelson, C. Ichoku, R. Kahn and D. Diner (NASA, USA) S. Freitas (INPE, Brazil) F.-Y. Leung (Washington.
Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew #, P.
Canada Centre for Remote Sensing Field measurements and remote sensing-derived maps of vegetation around two arctic communities in Nunavut F. Zhou, W.
Spectral classification of WorldView-2 multi-angle sequence Atlanta city-model derived from a WorldView-2 multi-sequence acquisition N. Longbotham, C.
Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.
In Situ and Remote Sensing Characterization of Spectral Absorption by Black Carbon and other Aerosols J. Vanderlei Martins, Paulo Artaxo, Yoram Kaufman,
BIOPHYS: A Physically-based Algorithm for Inferring Continuous Fields of Vegetative Biophysical and Structural Parameters Forrest Hall 1, Fred Huemmrich.
On the Use of Geostationary Satellites for Remote Sensing in the High Latitudes Yinghui Liu 1, Jeffrey R. Key 2, Xuanji Wang 1, Tim Schmit 2, and Jun Li.
Wildfire Plume Injection Heights Over North America: An Analysis of MISR Observations Maria Val Martin and Jennifer A. Logan (Harvard Univ., USA) Fok-Yan.
The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry.
14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural.
Geometric optical (GO) modeling of radiative transfer in plant canopy Xin Xi.
Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.
Measuring Vegetation Characteristics
An Examination of the Relation between Burn Severity and Forest Height Change in the Taylor Complex Fire using LIDAR data from ICESat/GLAS Andrew Maher.
Retrieval of Cloud Phase and Ice Crystal Habit From Satellite Data Sally McFarlane, Roger Marchand*, and Thomas Ackerman Pacific Northwest National Laboratory.
Retrieval of biomass burning aerosols with combination of near-UV radiance and near -IR polarimetry I.Sano, S.Mukai, M. Nakata (Kinki University, Japan),
MISR Geo-registration Overview Brian E. Rheingans Jet Propulsion Laboratory, California Institute of Technology AMS Short Course on Exploring and Using.
Objectives The Li-Sparse reciprocal kernel is based on the geometric optical modeling approach developed by Li and Strahler, in which the angular reflectance.
2004 ARM Science Team Meeting, March Albuquerque, New Mexico Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada.
Updated Cover Type Map of Cloquet Forestry Center For Continuous Forest Inventory.
Introduction: Our goal is to provide maps of woody plant crown cover and canopy height in the arid southwest US using moderate resolution NASA Earth Observing.
BRDF/ALBEDO GROUP Román, Schaaf, Strahler, Hodges, Liu Assessment of Albedo Derived from MODIS at ChEAS - Park Falls ChEAS 2006 Meeting: June 5 - June.
References: 1)Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B., Generating vegetation.
Landsat Satellite Data. 1 LSOS (1-ha) 9 Intensive Study Areas (1km x 1km) 3 Meso-cell Study Areas (25km x 25km) 1 Small Regional Study Area (1.5 o x 2.5.
DEFINITION LEAF AREA INDEX is defined as one half the total foliage
Aboveground Biomass, Mg ha -1 < 1.0 Red band bidirectional reflectance data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS)
Date of download: 6/24/2016 Copyright © 2016 SPIE. All rights reserved. Study area of Colorado Plateau with black dots for SNOTEL locations and digital.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Study on Dust Storms Climatological Trends, transportation paths and Sources Identification.
J. C. Stroeve, J. Box, F. Gao, S. Liang, A. Nolin, and C. Schaaf
TOA Radiative Flux Estimation From CERES Angular Distribution Models
Kostas Andreadis and Dennis Lettenmaier
Using MISR to Map Woody Plant Canopy Crown Cover, Height, and Biomass
A Multi-angle Aerosol Optical Depth Retrieval Algorithm for GOES
Sources of Variability in Canopy Spectra and the Convergent Properties of Plants Funding From: S.V. Ollinger, L. Lepine, H. Wicklein, F. Sullivan, M. Day.
Presentation transcript:

We show how moderate resolution data from the Multiangle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System Terra satellite can be interpreted through a simple geometric-optical model (SGM) to retrieve forest crown cover, mean canopy height, and biomass. These are important parameters in western forests that are increasingly vulnerable to wildfire with earlier melting of the snow pack. MISR Level 1B2 Terrain radiance data from overpasses in May and June 2002 over SE Arizona and S New Mexico were atmospherically corrected using MISR aerosol data and the bidirectional reflectance factors (BRF) were mapped to a 250 m grid. The background angular response in the MISR viewing plane was estimated prior to model inversion using the isotropic, geometric, and volume scattering weights of a LiSparse-RossThin kernel driven model, plus nadir camera blue, green and near-infrared reflectance factors. Calibration of these relationships was effected using woody plant cover estimates obtained from Ikonos 1 m panchromatic imagery in the USDA, ARS Jornada Experimental Range. The SGM was adjusted against red band data in all nine MISR views (view zenith angles <=70.5°) using the Praxis algorithm. Acknowledgments: This work was supported by NASA grant NNG04GK91G to MJC under the EOS program. Thanks are owed to David Diner, MISR PI. Data credits: NASA/JPL and LARC/ASDC. More at Fractional crown cover is calculated by adjusting retrieved crown radius (r) with fixed tree number density () -- exploiting sensitivity to brightness. Canopy height is calculated by adjusting retrieved b/r with fixed h/b, where b is vertical crown radius and h is crown center height above the reference plane -- exploiting sensitivity to BRF shape (Figure 2). The starting point for the inversions was r = 0.25 and b/r = 0.2, with the fixed parameters set to (), 2.0 (h/b), 0.09 (leaf reflectance) and 2.08 (crown leaf area index). When results from three adjacent overpasses were tested against data extracted from US Forest Service maps of biomass (estimated from cover x height.), forest cover, and canopy height, coefficients of determination were 0.76, 0.58 and 0.53 after filtering for error on model fitting and cloud/cloud- shadow contamination (N=547; all significant at the 99% level). The RMSE of the estimates was 2.8 m for canopy height, 35.6 Mg ha -1 (15.9 tons / acre) for biomass, and 0.17 for fractional cover (dimensionless). Correlations are a monotonic function of RMSE on model fitting. Figure 3 shows the retrieved distributions against the US Forest Service data for the Inter-mountain West. Figure 4 shows the effects of imposing RMSE thresholds on the results. Mapping Forest Canopy Structure and Biomass in the Southwestern US with MISR Mark Chopping 1, Lihong Su 1, Gretchen Moisen 2, Andrea Laliberte 3, Albert Rango 3, and John V. Martonchik 4 1 Earth & Environmental Studies, Montclair State University, Montclair, NJ 2 USDA, US Forest Service Rocky Mountain Research Station, Ogden, UT 3 USDA, ARS Jornada Experimental Range, New Mexico State University, Las Cruces, NM 4 NASA/JPL, Pasadena, CA The results from nine Terra overpasses were merged using min(RMSE) as the selection criterion to produce almost cloud-free cover, mean height, and biomass maps. Figures 5-8 show the results in maps that also include values for woody shrubs in desert grasslands. The technique also provides a map of understory density, estimated via the background brightness (not shown). We have shown that MISR data can be interpreted through a hybrid geometric-optical model to provide maps of canopy crown cover, height, and biomass over large areas with good accuracy. To our knowledge, these results are a unique application of moderate resolution EOS data. Figure 1. The method used to perform SGM inversions. (a) (b) Figure 2. The effects of changing (a) fractional crown cover with fixed = 0.and maintaining canopy height at 3.0 m (b) crown shape (b/r), maintaining h/b fixed at a typical value of >50.0 Tons/acre Aboveground Biomass 0.00 >110.0 Mg ha Fig. 5: Regional Aboveground Biomass 1.00 >20.00 meters Mean Canopy Height < 1.00 Fig. 6: Regional Mean Canopy Height Shrubs Crown Cover Bright white areas show anomalies (high error on model fitting) Forest Fig. 7: Regional Forest Crown Cover Figure 3. (a) Retrievals vs. US Forest Service Map Data (IW-FIA maps). A threshold of RMSE < 0.01 was used to filter the data set, retaining 547 points. Figure 4. The effect of RMSE filtering on R 2 and N for biomass (bio), crown cover (crn), and weighted height (wht) (b)(c) (a) Fig. 8: RMSE on Model Fitting 0.019>0.140 Rejected (mostly desert grassland) RMSE < Accepted (includes almost all forest) The large area with high RMSE is White Sands National Monument (gypsum dunes and alkali flats) 50 km < 6 > 60 Estimated Biomass (tons/acre) Arizona New Mexico Jornada Sevilleta Fig 9: Reference: USDA Forest Service map showing estimated forest biomass for the Inter- mountain West on a hill-shaded background; the Arizona / New Mexico border; and the locations of the Jornada Experimental Range and the Sevilleta National Wildlife refuge (NM).