Documentation Revision Date: 2017-02-08
Data Set Version: V1
There are three GeoTIFF files (.tif) with this data set.
Lagomasino, D., T. Fatoyinbo, S. Lee, E. Feliciano, C. Trettin, and M.C. Hansen. 2017. CMS: Mangrove Canopy Characteristics and Land Cover Change, Tanzania, 1990-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1377
Table of Contents
- Data Set Overview
- Data Characteristics
- Application and Derivation
- Quality Assessment
- Data Acquisition, Materials, and Methods
- Data Access
Data Set Overview
This data set provides canopy height, land cover change, and stand age estimates for mangrove forests in the Rufiji River Delta in Tanzania. The estimates were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques. Landsat imagery circa 1990 and circa 2014 was used to estimate stand age between 1994 and 2014 and for forest land cover change modeling.
Project: Carbon Monitoring System (CMS)
The NASA Carbon Monitoring System (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 and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.
Spatial Coverage: Tanzania
Spatial Resolution: Canopy height (12-m x 12-m), Land cover change (30-m x 30-m), stand age (30-m x 30-m)
Temporal Coverage: Data covers the period 1990-01-01 to 2014-12-31
Temporal Resolution: One time estimate
Study Area (All latitudes and longitudes are given in decimal degrees)
|Site||Westernmost Longitude||Easternmost Longitude||Northernmost Latitude||Southernmost Latitude|
|East Africa, Tanzania||39.192352||39.497222||-7.50833||-8.035212|
There are three GeoTIFF (.tif) data files with this data set.
Table 1. Data file descriptions.
|Rufiji_Stand_Age.tif||Stand ages derived from modeling and Landsat imagery|
|Rufiji_Land_Cover_Change.tif||Land cover change derived from modeling and Landsat imagery|
|Rufiji_TDX_CHM.tif||Canopy height derived from TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements).|
GEoTIFF File Properties
Table 2. Properties common to all files.
|Datum||File type||Map units||Fill value||# Bands|
Table 3. Properties of individual files.
|Data File||min_lon||min_lat||max_lon||max_lat||# Columns||# Rows||
Pixels, units, min-max values
|Rufiji_Stand_Age.tif||39.20333||-7.948888||39.474444||-7.711666||992||829||stand age, years, 1-17||byte|
|Rufiji_Land_Cover_Change.tif||39||-8||39.50833||-7.50833||2000||2000||mangrove area, categorical, 1-3||byte|
|Rufiji_TDX_CHM.tif||39||-8||39.50833||-7.50833||4501||4501||canopy height, meters, 1-46||float|
Application and Derivation
Mangroves may only represent 3% of the global forest cover, but it has been estimated that at the current rates of degradation, these forests can release up to 10% of the total carbon emissions from deforestation worldwide (Donato et al., 2011). The large carbon stocks along with the many economical ecosystem services, high rates of degradation, and threats from rising seas, make mangrove environments important regions of interest for climate mitigation and adaptation plans (Alongi et al., 2002). Remote sensing can provide multiple independent techniques to monitor and verify forest parameters such as canopy height.
A +/- 2 m uncertainty was used for the canopy height estimates. This uncertainty was determined through previous studies.
Google Earth imagery was also used extensively as an additional reference for the circa 2014 land cover classification, but not for the circa 1990 classification, as no imagery was available for this area for any date prior to 2009.
A static +/- 1 year uncertainty was used for the stand age modeling.
Data Acquisition, Materials, and Methods
The study site was the Rufiji River Delta in Tanzania.
Canopy height estimates (meters) were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques.
The TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) mission forms a pair of satellite instruments that enable single-pass interferometry to generate a consistent global digital elevation model (DEM) (Krieger et al., 2007).
Polarimetric SAR interferometry (Pol-InSAR) technique is based on the coherent combination of both polarimetric and interferometric observables. In recent years, the quantitative models from Pol-InSAR data have been primarily demonstrated using airborne repeat-pass fully polarimetric interferometric systems over a variety of forests (e.g., boreal, temperate, and tropical forests) at L- and P-band, and more recently even at X-band. The TanDEM-X (TDX) mission allows the acquisition of satellite Pol-InSAR data at X-band without temporal decorrelation that is the most critical factor for a successful Pol-InSAR inversion in a conventional repeat-pass spaceborne or airborne SAR system (Lee and Fatoyinbo, 2015).
Mangrove forest cover change
Mangrove forest cover change was determined through of a post-classification comparison of 1990 and 2014 Landsat imagery for mangrove forest extent. To create these two classifications, training data were collected over mangrove and non-mangrove areas using image interpretation of circa 1990 and circa 2014 Landsat composites. The predominant band combination used for the visual interpretation was NIR (1.55-1.75 microm for TM, 0.85-0.88 microm for OLI), SWIR I (1.55-1.75 microm for TM, 1.57-1.65 microm for OLI), and SWIR II (2.08-2.35 microm for TM, 2.11-2.29 microm for OLI) loaded into the RGB channels, as this combination allowed us to discriminate forested areas easily. The Land Cover Change model estimated the current extent of mangroves (2014, Value = 1), gained areas of mangroves (1990-2014, Value = 2), and area of mangrove loss (1990-2014, Value = 3). No Data values are equal to zero.
Mangrove forest stand ages
Mangrove forest stand age was determined through comparison of annual mosaics of 1990 to 2014 Landsat imagery for mangrove forest extent. Google Earth imagery was also used extensively as an additional reference for the circa 2014 classification, but not for the circa 1990 classification, as no imagery was available for this area for any date prior to 2009. A static +/- 1 year uncertainty was used for the stand age modeling. This value was determined by the 1 year time overlap between annual mosaic images used in the analyses. The pixel value represents the age of the mangrove forests since 1994. No data values are equal to zero.
These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Contact for Data Center Access Information:
- E-mail: email@example.com
- Telephone: +1 (865) 241-3952
Alongi, D.M. Present state and future of the world’s mangrove forests. Environ. Conserv. 2002, 29, 331-349. http://dx.doi.org/10.1017/S0376892902000231
Donato, D.C., Kauffman, J.B., Murdiyarso, D., Kurnianto, S., Stidham, M., and Kanninen, M. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 2011, 4, 293-297. http://dx.doi.org/10.1038/ngeo1123
Krieger, G., Moreira, A., Fiedler, H., Hajnsek, I., Werner, M., Younis, M., Zink, M. TanDEM-X: A satellite formation for high-resolution SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2007, 45, 3317-3341. http://dx.doi.org/10.1109/TGRS.2007.900693
Lee, S.-K., and T.E. Fatoyinbo. 2015. TanDEM-X Pol-InSAR Inversion for Mangrove Canopy Height Estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 7, pp. 3608-3618. http://dx.doi.org/10.1109/JSTARS.2015.2431646