LiDAR Surveys over Selected Forest Research Sites, Brazilian Amazon, 2008-2018

This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.

This data set contains 3,166 files in compressed LAS (*.laz) file format and two companion files with tabular and spatial summaries of the LAS file contents and extents for each of the tiles.

This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.

Project: Carbon Monitoring System (CMS)

The CMS is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data.

Related Publications:

Citations for the numerous related publications are given in the main reference section of this document, Section 7.

Related Datasets:

dos-Santos, M.N., and M.M. Keller. 2016. CMS: Forest Inventory and Biophysical Measurements, Para, Brazil, 2012-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1301

dos-Santos, M.N., and M.M. Keller. 2016. CMS: LiDAR Data for Forested Areas in Paragominas, Para, Brazil, 2012-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1302

Keller, M.M., P. Duffy, and W. Barnett. 2019. LiDAR and PALSAR-Derived Forest Aboveground Biomass, Paragominas, Para, Brazil, 2012. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1648

Acknowledgements:

LiDAR surveys performed through the Sustainable Landscapes project were commissioned by the United States Forest Service in collaboration with the Brazilian Enterprise for Agricultural Research (EMBRAPA) (https://www.paisagenslidar.cnptia.embrapa.br/geonetwork/srv/por/catalog.search#/home) and are archived through the Carbon Monitoring System project funded by NASA.

Spatial Coverage: Selected areas of the Amazon Basin and other regions in Brazil

Spatial Resolution:  ~ 10 points per m² provided in 1 km² tiles over key field research sites

Temporal Coverage: June 2008 to August 2018

Temporal Resolution: single acquisition over most sites (multiple acquisition over some sites)

Study Area: (all latitude and longitudes given in decimal degrees)

Site	Westernmost Longitude	Easternmost Longitude	Northernmost Latitude	Southernmost Latitude
LiDAR flightlines over Brazil	-68.2897	-39.058	-1.5794	-26.6964

 

Data File Information

This dataset contains 3,166 files in compressed LAS (*.laz) file format and two companion files, cms_brazil_lidar_tile_inventory.csv and cms_brazil_lidar_tile_inventory.kmz, with summaries of the LAS file contents. 

 

File organization and naming convention

Files are organized according to the Survey_reference_code given in Table 1 below.

For example, the reference code ANT_A01_2011_LiDAR associates the *.las data files collected at the "Floresta Estadual do Antimary" research site in 2011 with the metadata uuid shown in Table 1.

Please refer to the Sustainable Landscapes metadata for the definitions of the 3-character site codes and additional site descriptions.

 

File names begin with their respective Survey_reference_code and end with a sequential number identifying the specific 1 km2 tile data provided.

• ANT_A01_2011_laz_0.laz
• ANT_A01_2011_laz_1.laz
• …
• ANT_A01_2011_laz_11.laz

Note: not all file names follow this structure.  Please refer to specific survey reference code Sustainable Landscapes metadata for details about these alternatively-named files.

 

File format

The LAS standard is developed and maintained by the American Society for Photogrammetry and Remote Sensing (ASPRS; https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities) and is currently on version 1.4. These data are distributed in LAS versions 1.0 through 1.2. 

 

Table 1.  LiDAR survey information.

Survey_reference_code:  Associates the *.las data files collected at a research site in a given year with the metadata uuid.

utm:  Universal Transverse Mercator time zone for the specific survey reference code that is input for the Spatial Reference System (Well Known Text) provided below.

uuid:  Provides the link to the Sustainable Landscapes metadata record managed by EMBRAPA with research site and data collection details.

uuid link: https://www.paisagenslidar.cnptia.embrapa.br/geonetwork/srv/por/catalog.search#/metadata/<UUID>

For example: https://www.paisagenslidar.cnptia.embrapa.br/geonetwork/srv/por/catalog.search#/metadata/3da138cf-661c-4d2f-93bf-a8d75117df51

State*	Survey_reference_code	utm	uuid
AC	ANT_A01_2011_LiDAR	19S	3da138cf-661c-4d2f-93bf-a8d75117df51
AC	BON_A01_2013_LiDAR	19S	9e45ff2c-427d-48ec-a252-b6fb9c519ff4
AC	HUM_A01_2013_LiDAR	19S	af42b430-fe12-4045-98d1-44304c42b2c0
AC	RIB_A01_2014_LiDAR	19S	a5e59f01-c6fc-4d44-a6d1-846ec3c542e8
AC	TAL_A01_2013_LiDAR	19S	190f55bc-3fea-432e-b197-3d0c4864172e
AM	BA3_A01_2014_LiDAR	20S	2f10b906-65bf-46ec-b617-80db15452064
AM	BA3_A02_2014_LiDAR	20S	ea4a6846-25e7-4d35-a12a-6b529d5b855b
AM	BAR_A01_2014_LiDAR	20S	fc649878-1f64-4369-af0a-e3e30853e10e
AM	DUC_A01_2012_LiDAR	21S	4c65b6de-25e4-4d91-8015-d79b4c973534
AM	DUC_A01_2017_LiDAR	20S	40678cdf-d0b4-49e1-a574-c3a2e36b0afa
AM	ZF2_A01_2017_LiDAR	20S	afc4b77d-b24d-40a6-ba2b-391d3cb10344
AM	ZF2_A02_2017_LiDAR	20S	a5291df5-887b-4199-8850-1181df879fd9
BA	CON_A01_2015_LiDAR	24S	ec45e4f0-96b8-425a-a500-bc7f52c3c23d
GO	GO1_A01_2014_LiDAR	22S	c8a06ed4-e725-4674-b999-f7141d2643c4
MT	COT_A01_2011_LiDAR	21S	30293dd1-801d-4b5a-bb2e-e4bce6e845d0
MT	FN1_A01_2013_LiDAR	22S	ad364160-6076-46b3-84a7-457419f61b5a
MT	FN1_A01_2016_LiDAR	21S	2426a1ed-53f6-4344-b6a0-84468ff5c4f6
MT	FN2_A01_2013_LiDAR	21S	42fca3ef-ba02-4992-b25f-a40bca98ced8
MT	FN2_A01_2016_LiDAR	21S	b8bffa7f-ca25-452f-9fd1-ef92222a9479
MT	FN3_A01_2014_LiDAR	21S	d13c982c-3de4-450d-93c8-9133d5231817
MT	FNA_A01_2013_LiDAR	21S	b3d6585d-7ade-4a64-8baf-feec61768f00
MT	FNB_A01_2014_LiDAR	21S	a12c2729-71f3-4a3d-ac4e-746e9c414c56
MT	FNC_A01_2017_LiDAR	21S	80ba6571-0f92-436c-9e39-bb8cc9055272
MT	FNC_A02_2017_LiDAR	21S	b3c61b01-0308-40e3-8fa3-1a09a3f389f3
MT	FNC_A03_2017_LiDAR	21S	def96254-96a6-4625-b5dd-7e4569c1d381
MT	FNC_A04_2017_LiDAR	21S	a983d75d-7edc-425d-8d1b-501e257c4906
MT	FND_A01_2017_LiDAR	21S	29c05dda-2b32-4cfa-9de9-8ce5a98c7edc
MT	FND_A02_2017_LiDAR	21S	6062b245-b03c-4425-8527-b87784fce534
MT	FND_A03_2017_LiDAR	21S	e8afd457-e17f-4716-b02a-a0d1309203fb
MT	TAN_A01_2012_LiDAR	22S	4ea1d55a-fb4c-49cf-bb78-192136307420
MT	TAN_A01_2014_LiDAR	22S	82aa594b-30d5-4c70-83e2-f71809a6f703
MT	FN3_A01_2017_LiDAR	21S	257c32e8-e5b4-4808-b826-ff6d2bcf4536
PA	ANA_A01_2017_LiDAR	21S	5ad640b7-6c98-4042-9338-6be706955ca4
PA	AND_A01_2013_LiDAR	23S	465c4897-a586-4270-9232-c65ee346fcd0
PA	AND_A01_2017_LiDAR	23S	4b75ccab-e0e5-46d8-bbb0-5963943402e9
PA	CAU_A01_2017_LiDAR	22S	79f764e6-c4fc-40e5-bf8b-84b883e2ab92
PA	FST_A01_2013_LiDAR	21S	ee14db64-23b6-40f8-8a88-f446b10218cb
PA	PAR_A01_2017_LiDAR	23S	ec312711-d296-4ef2-a802-4a27cb4beb83
PA	PRG_A01_2017_LiDAR	23S	4d512f9d-fbd9-4b0e-95d7-2f8f5cca04b9
PA	SAN_A01_2014_LiDAR	21S	062144b9-69a8-420c-9d35-360d313f7adc
PA	SAN_A02_2014_LiDAR	21S	c97bd3e4-5d16-4218-9c83-33fd32d79624
PA	SFX_A01_2012_LiDAR	22S	a3bb4f79-ef32-4b28-8295-2d963a1044e5
PA	SFX_A02_2012_LiDAR	22S	fba0d5c5-d60a-483b-bc9e-cbc54a5979e0
PA	ST1_A01_2013_LiDAR	21S	c52a2c00-8ed7-4c0c-9366-e7d5facd1ced
PA	ST1_A01_2016_LiDAR	21S	e34bc4ba-674a-445d-9365-05df77c4287d
PA	ST2_A01_2013_LiDAR	21S	ca02d0b5-959b-40b9-a22d-30b8cc00ef23
PA	ST2_A01_2016_LiDAR	21S	50a301bb-4b9a-4dff-9f0c-fa4144918506
PA	ST3_A01_2014_LiDAR	21S	39f6df78-0dde-49ca-9e51-35fdf93f28b0
PA	ST3_A01_2017_LiDAR	21S	bd961041-d584-47d2-9575-548516d3bbea
PA	TAC_A01_2013_LiDAR	22S	1acdce2c-264f-4b8d-ae5d-1d7d5d14afba
PA	TAP_A01_2012_LiDAR	21S	db302dc9-3e3b-442e-98a6-4ed4163a8ca6
PA	TAP_A01_2017_LiDAR	21S	cdf55e86-4bf6-4a63-b75b-45c4b236ddb7
PA	TAP_A02_2012_LiDAR	21S	8337185e-9053-4ae4-a796-543d1d24d34b
PA	TAP_A02_2013_LiDAR	21S	1c5a0098-b3ae-40c0-bfdb-eeb813ded3f1
PA	TAP_A02_2016_LiDAR	21S	8c8192b3-139c-4d57-89dd-8a90d17d5d37
PA	TAP_A03_2012_LiDAR	21S	c21c5f32-40a1-422a-aba9-579728a8a7ee
PA	TAP_A03_2013_LiDAR	21S	c41c8541-8f4e-4fcf-8f2a-cf5ba6a2fd3d
PA	TAP_A03_2016_LiDAR	21S	77e46065-808b-4593-b0bb-e1690e482354
RO	JAM_A01_2011_LiDAR	20S	1582bba0-1eee-4c87-b1e9-d888636b67ba
RO	JAM_A02_2011_LiDAR	20S	5170c140-e3c5-4c59-8b28-6e970345acc1
RO	JAM_A02_2013_LiDAR	20S	67dd1511-3088-4721-9016-510c5f0c0961
RO	JAM_A03_2013_LiDAR	20S	01f90c54-1de6-4a87-8567-0fec94ebdb94
SC	CAG_A01_2013_LiDAR	22S	355d9e5f-4d37-4855-97cf-e85ffa060b90
SC	MMA_A01_2017_LiDAR	22S	a4d2f161-828e-4093-ac7d-e2622713c31f
SP	CAN_A01_2014_LIDAR	23S	f60019ac-749e-4343-8ab0-c517db3abf47
SP	CAN_A01_2017_LIDAR	23S	ead39fd8-c6d5-4e3b-8bb3-c96e1f708e81
SP	CAN_A02_2014_LIDAR	23S	22dddcd7-6f89-40b0-a9b7-16c23977ef28
SP	CAN_A02_2017_LIDAR	23S	26a4334f-90c9-4e97-b499-39e3082a12ca
SP	SDM_A01_2012_LiDAR	23S	592a857f-9e3f-46b8-9774-5973bcf0cabb
SP	SDM_A01_2017_LiDAR	23S	2a70b81c-fa04-4c1d-b442-7f60bafb7648

* State codes in the leftmost column refer to the Brazilian states of Acre (AC), Amazonas (AM), Bahia (BA), Goias (GO), Mato Grosso (MT), Para (PA), Rondonia (RO), Santa Catarina (SC), and Sao Paulo (SP).

 

Spatial Reference System (Well Known Text)

Note -- these data are located across six Universal Transverse Mercator (UTM) zones: 19S, 20S, 21S, 22S, 23S, and 24S.  Please refer to Table 1 for the UTM zone for a respective survey reference code and file.

The OGC standard Well Known Text representation of the UTM system used for these data:

PROJCS["WGS 84 / UTM zone __S", ### Seven zones: 19, 20, 21, 22, 23, 24     GEOGCS["WGS 84",         DATUM["WGS_1984",             SPHEROID["WGS 84",6378137,298.257223563,                 AUTHORITY["EPSG","7030"]],             AUTHORITY["EPSG","6326"]],         PRIMEM["Greenwich",0,             AUTHORITY["EPSG","8901"]],         UNIT["degree",0.01745329251994328,             AUTHORITY["EPSG","9122"]],         AUTHORITY["EPSG","4326"]],     UNIT["metre",1,         AUTHORITY["EPSG","9001"]],     PROJECTION["Transverse_Mercator"],     PARAMETER["latitude_of_origin",0],     PARAMETER["central_meridian",___], ### Varies by zone: -69, -63, -57, -51, -45, -39     PARAMETER["scale_factor",0.9996],     PARAMETER["false_easting",500000],     PARAMETER["false_northing",10000000],     AUTHORITY["EPSG","327__"], ### Varies by zone: 32719, 32720, 32721, 32722, 32723, 32724     AXIS["Easting",EAST],     AXIS["Northing",NORTH]]

 

Companion File Information

Please see the companion files cms_brazil_lidar_tile_inventory.csv and cms_brazil_lidar_tile_inventory.kmz for tabular and spatial summaries of the LAS file content. 

Both provide similar information about the LiDAR inventory:

Field	Description
filename	filename
max_lat	maximum latitude extent (decimal degrees)
min_lat	minimum latitude extent (decimal degrees)
max_lon	maximum longitude extent (decimal degrees)
min_lon	minimum longitude extent (decimal degrees)
file_type	always "pointcloud"
file_size_mb	size of the file in megabytes
file_format	always "LAS/LAZ"
version	LAS specification version (always from v1.0, v1.1, v1.2)
created	file creation date
utmzone	UTM zone given as "##S" (always from 19S, 20S, 21S, 22S, 23S, 24S)
srs	proj4 string for the UTM zone
download	direct HTTPS download link (like: https://daac.ornl.gov/daacdata/cms/LiDAR_Forest_Inventory_Brazil/data/<FILE>.laz)

These LiDAR acquisitions are meant to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance and forest recovery over time.

Checks for horizontal and vertical accuracy, and other standard quality control measures were performed by affiliated LiDAR survey vendors. Please refer to http://mapas.cnpm.embrapa.br/paisagenssustentaveis/ for vendor information. 

Project Overview

Brazilian tropical forests contain approximately one-third of the global carbon stock in above-ground tropical forest biomass. Deforestation has cleared about 15% of the extensive forest on the Brazilian Amazon frontier. Logging, and understory forest fires may have degraded a similar area of forest. In response to the potential climatic effects of deforestation, policy makers have suggested reductions in emissions through deforestation and forest degradation and enhanced forest carbon stocks (REDD+). Carbon accounting for REDD+ requires knowledge of deforestation, degradation, and associated changes in forest carbon stocks. 

Degradation is more difficult to detect than deforestation. This LiDAR inventory will continue to help researchers to quantify carbon stocks and changes and associated uncertainties in the Brazilian Amazon.

LiDAR Acquisition

LiDAR surveys were flown between June 2008 and August 2018. The data were collected and processed to point cloud files by commercial vendors under several grants, and incorporated into the broader integrated effort of the Sustainable Landscapes project. Sustainable Landscapes is supported by the United States Agency for International Development (USAID) and US Department of State. LiDAR surveys performed through the Sustainable Landscapes project were commissioned by the United States Forest Service in collaboration with the Brazilian Enterprise for Agricultural Research (EMBRAPA) and are archived through the Carbon Monitoring System project funded by NASA.

EMBRAPA maintains a metadata portal for the Sustainable Landscapes project at: https://www.paisagenslidar.cnptia.embrapa.br/webgis/.

Related Publications:

Andersen, Hans-Erik; McGaughey, Robert J.; Reutebuch, Stephen E. 2005. Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment. 94: 441-449. https://doi.org/10.1016/j.rse.2004.10.013 

Avitabile, V. , Herold, M. , Heuvelink, G. B., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J. , Ashton, P. S., Banin, L. , Bayol, N. , Berry, N. J., Boeckx, P. , Jong, B. H., DeVries, B. , Girardin, C. A., Kearsley, E. , Lindsell, J. A., Lopez-Gonzalez, G. , Lucas, R. , Malhi, Y. , Morel, A. , Mitchard, E. T., Nagy, L. , Qie, L. , Quinones, M. J., Ryan, C. M., Ferry, S. J., Sunderland, T. , Laurin, G. V., Gatti, R. C., Valentini, R. , Verbeeck, H. , Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406-1420. https://doi.org/10.1111/gcb.13139

Chen, Q.; Lu, D.; Keller, M.; Dos-Santos, M.N.; Bolfe, E.L.; Feng, Y.; Wang, C. Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data. Remote Sens. 2016, 8, 21. https://doi.org/10.3390/rs8010021 

Görgens, E. B., Soares, C. P., Nunes, M. H. and Rodriguez, L. C. (2016), Characterization of Brazilian forest types utilizing canopy height profiles derived from airborne laser scanning. Appl Veg Sci, 19: 518-527. https://doi.org/10.1111/avsc.12224

Hunter, M. O., Keller, M., Victoria, D., and Morton, D. C.: Tree height and tropical forest biomass estimation, Biogeosciences, 10, 8385–8399, 2013. https://doi.org/10.5194/bg-10-8385-2013

Hunter MO, Keller M, Morton D, Cook B, Lefsky M, Ducey M, et al. (2015) Structural Dynamics of Tropical Moist Forest Gaps. PLoS ONE 10(7): e0132144. https://doi.org/10.1371/journal.pone.0132144

Jucker, T. , Caspersen, J. , Chave, J. , Antin, C. , Barbier, N. , Bongers, F. , Dalponte, M. , Ewijk, K. Y., Forrester, D. I., Haeni, M. , Higgins, S. I., Holdaway, R. J., Iida, Y. , Lorimer, C. , Marshall, P. L., Momo, S. , Moncrieff, G. R., Ploton, P. , Poorter, L. , Rahman, K. A., Schlund, M. , Sonké, B. , Sterck, F. J., Trugman, A. T., Usoltsev, V. A., Vanderwel, M. C., Waldner, P. , Wedeux, B. M., Wirth, C. , Wöll, H. , Woods, M. , Xiang, W. , Zimmermann, N. E. and Coomes, D. A. (2017), Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob Change Biol, 23: 177-190. https://doi.org/10.1111/gcb.13388

Leitold, V., Keller, M., Morton, D.C. et al. Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+. Carbon Balance Manage 10, 3 (2015). https://doi.org/10.1186/s13021-015-0013-x

Leitold, V. , Morton, D. C., Longo, M. , dos Santos, M. N., Keller, M. and Scaranello, M. 2018. El Niño drought increased canopy turnover in Amazon forests. New Phytol, 219: 959-971. https://doi.org/10.1111/nph.15110

Longo, M., Keller, M., dos-Santos, M. N., Leitold, V., Pinagé, E. R., Baccini, A., Saatchi, S., Nogueira, E. M., Batistella, M., and Morton, D. C. ( 2016), Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon, Global Biogeochem. Cycles, 30, 1639– 1660, https://doi.org/10.1002/2016GB005465

Meyer, V., Saatchi, S., Clark, D. B., Keller, M., Vincent, G., Ferraz, A., Espírito-Santo, F., d'Oliveira, M. V. N., Kaki, D., and Chave, J.: Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes, Biogeosciences, 15, 3377–3390, 2018. https://doi.org/10.5194/bg-15-3377-2018

Yhasmin Mendes de Moura, Thomas Hilker, Alexei I. Lyapustin, Lênio Soares Galvão, João Roberto dos Santos, Liana O. Anderson, Célio Helder Resende de Sousa, Egidio Arai, Seasonality and drought effects of Amazonian forests observed from multi-angle satellite data, Remote Sensing of Environment, Volume 171, 2015, Pages 278-290, ISSN 0034-4257. https://doi.org/10.1016/j.rse.2015.10.015

Danielle I Rappaport, Douglas C Morton, Marcos Longo, Michael Keller, Ralph Dubayah, and Maiza Nara dos-Santos. 2018. Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation. Environmental Research Letters, Volume 13, Number 6, 065-113. https://doi.org/10.1088/1748-9326/aac331

Sato, L.Y.; Gomes, V.C.F.; Shimabukuro, Y.E.; Keller, M.; Arai, E.; Dos-Santos, M.N.; Brown, I.F.; Aragão, L.E.O.C. Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia. Remote Sens. 2016, 8, 839. https://doi.org/10.3390/rs8100839

Silva, C.A.; Hudak, A.T.; Vierling, L.A.; Klauberg, C.; Garcia, M.; Ferraz, A.; Keller, M.; Eitel, J.; Saatchi, S. Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest. Remote Sens. 2017, 9, 1068. https://doi.org/10.3390/rs9101068

R. Treuhaft et al., "Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer," in IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 239-243, Feb. 2015. https://doi.org/10.1109/LGRS.2014.2334140