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NASA NTRS2021

Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data

Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias and for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with and for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ . Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

Tiangang Yin↗

NASA NTRS1982

Advances in information extraction techniques

Sundry recent developments are presented which show some potential for affecting the automatic extraction of information from remotely sensed data. Pattern representations more abstract than Euclidean vector spaces offer some hope of unifying structural and decision theoretical approaches. The estimation of expected classification error rates is becoming more sophisticated and rigorous, but useful finite-sample results for nonparametric distributions appear unobtainable. Focus on computational complexity allows comparison of algorithms, while software engineering techniques reduce the effort necessary to develop and maintain complex image processing systems. Advances in computer systems architecture, commercial database technology, and man-machine communications should be closely monitored by the remote sensing community. A NASA-sponsored recommendation for research directions in mathematical pattern recognition are offered.

Nagy, G.↗

NASA NTRS1998

Fitting a Two-Component Scattering Model to Polarimetric SAR Data

Classification, decomposition and modeling of polarimetric SAR data has received a great deal of attention in the recent literature. The objective behind these efforts is to better understand the scattering mechanisms which give rise to the polarimetric signatures seen in SAR image data. In this Paper an approach is described, which involves the fit of a combination of two simple scattering mechanisms to polarimetric SAR observations. The mechanisms am canopy scatter from a cloud of randomly oriented oblate spheroids, and a ground scatter term, which can represent double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants or Bragg scatter from a moderately rough surface, seen through a layer of vertically oriented scatterers. An advantage of this model fit approach is that the scattering contributions from the two basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. The solution involves the estimation of four parameters from four separate equations. The model fit can be applied to polarimetric AIRSAR data at C-, L- and P-Band.

Freeman, A.↗

NASA NTRS1996

A Three-component Scattering Model for Polarimetric SAR Data

SAR data AIRSAR backscatter scattering↗

NASA NTRS2011

Characterizing Vegetation Cover in Global Savannas with an Annual Foliage Clumping Index Derived from the MODIS BRDF Product

The global savanna biome is characterized by enormous diversity in the physiognomy and spatial structure of the vegetation. The foliage clumping index can be calculated from bidirectional reflectance distribution function (BRDF) data. It measures the response of the darkspot reflectance to increased shadow associated with clumped vegetation and is related to leaf area index. Clumping index theoretically declines with increasing woody cover until the tree canopy begins to become uniform. In this study, clumping index is calculated for Moderate Resolution Imaging Spectroradiometer BRDF data for the Australian tropical savanna, the tropical savannas of South America, and the tropical savannas of east, west and southern Africa and compared with site-based measurements of tree canopy cover, and with area-based classifications of land cover. There were differences in sensitivity of clumping index between red and near-infrared reflectance channels, and between savanna systems with markedly different woody vegetation physiognomy. Clumping index was broadly related to foliage cover from historical site data in Australia and in West Africa and Kenya, but not in Southern Africa nor with detailed site-based demographic data in the cerrado of Brazil. However, clumping index decreased with proportion of woody cover in land cover datasets for east Africa, Australia and the Colombian Llanos. There was overlap in the range of clumping index values for forest, cerrado and campo land covers in Brazil. Clumping index was generally negatively correlated with percentage tree cover from the MODIS Vegetation Continuous Fields product, but regional differences in the relationship were evident. There were large differences in the frequency distributions of clumping index from savanna, woody savanna and grassland land cover classes between global ecoregions. The clumping index shows differing sensitivity to savanna woody cover for red and NIR reflectance, and requires regional calibration for application as a universal indicator.

Hill, Michael J.↗

NASA NTRS2010

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

Joiner, J.↗

NASA NTRS2004

Spaceborne microwave remote sensing of seasonal freeze-thaw processes in theterrestrial high l atitudes : relationships with land-atmosphere CO2 exchange

Landscape transitions between seasonally frozen and thawed conditions occur each year over roughly 50 million square kilometers of Earth's Northern Hemisphere. These relatively abrupt transitions represent the closest analog to a biospheric and hydrologic on/off switch existing in nature, affecting surface meteorological conditions, ecological trace gas dynamics, energy exchange and hydrologic activity profoundly. We utilize time series satellite-borne microwave remote sensing measurements from the Special Sensor Microwave Imager (SSM/I) to examine spatial and temporal variability in seasonal freeze/thaw cycles for the pan-Arctic basin and Alaska. Regional measurements of spring thaw timing are derived using daily brightness temperature measurements from the 19 GHz, horizontally polarized channel, separately for overpasses with 6 AM and 6 PM equatorial crossing times. Spatial and temporal patterns in regional freeze/thaw dynamics show distinct differences between North America and Eurasia, and boreal forest and Arctic tundra biomes. Annual anomalies in the timing of thawing in spring also correspond closely to seasonal atmospheric CO2 concentration anomalies derived from NOAA CMDL arctic and subarctic monitoring stations. Classification differences between AM and PM overpass data average approximately 5 days for the region, though both appear to be effective surrogates for monitoring annual growing seasons at high latitudes.

Sensor Microwave Imager (SSM/I)carbon cycle↗

NASA NTRS1977

The Maximum Likelihood Estimation of Signature Transformation /MLEST/ algorithm

The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.

Thadani, S. G.↗

NASA NTRS1983

Mapping and monitoring renewable resources with space SAR

The SEASAT-A SAR and SIR-A imagery was examined to evaluate the quality and type of information that can be extracted and used to monitor renewable resources on Earth. Two tasks were carried out: (1) a land cover classification study which utilized two sets of imagery acquired by the SEASAT-A SAR, one set by SIR-A, and one LANDSAT set (4 bands); and (2) a change detection to examine differences between pairs of SEASAT-A SAR images and relates them to hydrologic and/or agronomic variations in the scene.

Ulaby, F. T.↗

NASA NTRS2020

Sensor Chip Assembly (SCA) Test Report

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 90 K (1.0V bias voltage) and 95 K (0.5V bias voltage) are also reported. For all cases, the frame time used is 2.764 seconds. Reference pixel correction was applied to every raw frame.

WFIRST SCA TEST REPORT↗

NASA NTRS2020

SCA Test Report: H4RG-20828 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below.The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 90 K (1.0V bias voltage) and 95 K (0.5V bias voltage) are also reported. For all cases, the frame time used is 2.764 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗

NASA NTRS2020

SCA Test Report, H4RG-20829 WFIRST

Laddawan R Miko↗

NASA NTRS2020

SCA Test Report: H4RG-20833 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below.The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 90 K (1.0V bias voltage) and 95 K (0.5V bias voltage) are also reported. For all cases, the frame time used is 2.764 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗

NASA NTRS2020

SCA Test Report: H4RG-20849 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. For all cases, the frame time used is 2.764 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗

NASA NTRS2020

SCA Test Report: H4RG-21224 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm.This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below.The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗

NASA NTRS2020

SCA Test Report: H4RG-21225 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test,the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗

NASA NTRS2020

SCA Test Report: H4RG-21317 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm.This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗

NASA NTRS2020

SCA Test Report: H4RG-21319 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 µm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗