EO-Based Forest Cover Change Mapping Services have been Improved
The Cameroon REDD Pilot Project demonstrated improvement needs related to Forest Cover Change mapping in the Congo Basin Region. Thus, the following research activities have been realized within REDDAF:
- Methodologies were developed to combine various multi-temporal and multi-scale optical sensor data with radar sensor data (in order to fill the gaps induced by frequent cloud cover in tropical areas).
- A robust radiometric calibration technique was developed in order to facilitate classification and to improve the usability of multi-sensor data.
- Additionally, an approach to radiometrically correct areas affected by cloud shadows was developed to enhance the use of historic image data.
Test Areas in Cameroon & CAR
The methods were developed in an area in the Center Province of Cameroon and subsequently were tested in an area within the Central African Republic. The methods developed have been integrated into optimized and cost efficient operational processing chains.
Integrating Optical and SAR Data
As optical data often is affected by clouds and cloud shadows, alternative approaches are investigated to fill gaps in the optical data coverage. We have investigated the potential for using SAR data for deforestation mapping and a processing chain was implemented for both pre-processing and classification of PALSAR data. For a proper integration of optical and SAR data, geometric congruence of the data sets is a prerequisite. Typically, this can be achieved by time-consuming manual tie point selection, which is difficult in homogeneous forested areas. In order to reduce these manual efforts, a fully automatic image matching procedure using the mutual information method was developed. The results matching PALSAR and Landsat images show that the RMSE could be reduced from over 80 m to less than 10 m in both X and Y direction. Tests have been done with a wide variety of different data sets with similar results. This pre-processing step is important for both deforestation and also for degradation mapping.
Left image: before matching; right image: after matching
Following the successful pre-processing, a method has been developed for the cloud gap filling. The classification-based trainer was developed, which allows filling classification gaps caused by clouds or sensor failures in optical data by using SAR data without much manual effort.
Left image: Forest (purple), nonforest (blue) classification result from optical data with gaps
Right image: Information gaps filled by automated SAR classification using the classification-based trainer
Adjusting Areas Affected by Cloud Shadows
For maximizing the use of optical satellite data, a method was developed to adjust areas affected by cloud shadows. The method is based on morphological characteristics and radiometrically enhances areas covered by cloud shadows (see Figure below). Correction is performed by histogram matching to the surrounding area. The corrected areas may be integrated into classification procedures and thus avoid separate classification or even visual interpretation of these areas, resulting in a time- and cost-saving processing line.
Cloud shadow compensation on Landsat image subset (clouds masked out in black). Left image: Landsat input image (with cloud shadow examples). Right image: Cloud shadow compensation.
Development and Testing of EO Methods
A main objective in the R&D of REDDAF was to develop viable Earth Observation (EO) methods for mapping degradation in the Congo Basin by overcoming the technical restrictions related to:
- frequent cloud cover in tropical forest areas and
- complex degradation patterns in tropical forest areas.
The main task was to develop tools and processing chains which allow mapping:
- the extent of degraded areas and
- the degree of degradation.
Methods were developed to make use of various sensor data for degradation mapping. An approach based on spectral mixture analysis was enhanced by introducing multi-temporal aspects based on optical data (time series analysis). Furthermore, options for 3D mapping were investigated in order to detect the gaps in the forest canopy induced by logging activities. This 3D approach is based on SAR data.
Test Areas in Cameroon & CAR
The methods for degradation mapping were developed in the Pallisco concession site in South-East Cameroon. In the second step, the methods were transferred to map a prototype area in CAR.
Mapping Degradation Areas by Means of Time Series Analysis
A multi-temporal method for forest degradation mapping caused by selective logging was developed. Based on visual interpretation of very high resolution optical data as reference, a classification was performed. The time series analysis builds on a multi-temporal stack of spectral mixture analysis (SMA) fraction images. The classifier used is a minimum distance classifier. For the training, the temporal curves from the reference data were used. The pixel-based result of this classification (gaps, roads, skid trails, etc) was then refined by context analysis (distance to logging roads / settlements). Finally, the classified pixels were aggregated to larger areas of potential forest degradation with a minimum mapping unit of 5 ha. An example mapping result for a small subset of the prototype area in CAR is shown in the Figure below.
Left image: Landsat image 2001 in CAR.
Right image: Mapping result (non-forest areas in white and degradation areas in red) superimposed on Landsat image 2001.
3D Mapping of Forest Canopy to Detect Degradation
Furthermore options for 3D mapping of forest canopy to detect degradation have been investigated. The work comprised InSAR processing, radargrammetric processing, and the extraction of degradation areas from digital surface models. Results show that from CSK data a 3D model could be derived, which in combination with the existing SRTM model clearly shows gaps and roads as features of degradation (dark patches in the right Figure below).These features were then aggregated to areas of degradation. The accuracy assessment of this aggregated result compared to visual interpretation showed an overall accuracy of 82%.
Left image: Test area for 3D degradation mapping, Rapideye data from December 2011. Red: areas affected by degradation; blue: extent of 3D model from CSK data.
Right image: Difference model from CSK 3D generated with the SRTM difference approach.
Direct Biomass Assessment
Direct Biomass Assessment
EO-Based Methodologies to Directly Assess Above-Ground-Biomass
The objective of the biomass study is to develop, test and provide improved methodologies to directly assess above-ground-biomass in the Congo Basin Region using radar EO data. The activities aim to develop transferrable methodology to provide mapping products that contain gridded and georeferenced values of Above Ground Biomass (AGB). The approach focuses on improving existing image processing and biomass inversion methods to fully exploit the potential of the currently available data (ALOS PALSAR-L-band). The work, based on existing efforts conducted by CESBIO, will be tested and refined using in situ data collected in Cameroon.
Improving the State-of-the-Art Methods
In this project, we propose to assess methods beyond the state of the art in biomass mapping in Cameroon, for improvement in several aspects:
- Development of methods which can be generalized on other sites, and relying on a limited number of in-situ data.
- Improvement of the spatial resolution of the biomass map: multi-temporal and dual polarized ALOS PALSAR data are be used for speckle filtering without degrading the spatial resolution.
- Improvement of the inversion results using Bayesian inversion and/or the Support Vector Regression (SVR) methods.
The processing chain is achieved mostly using free software for practical implementation. The methods are applied to ALOS-PALSAR data of the prototype region (120 x 130 km) to provide biomass maps for years 2007-2008-2009-2010. Changes in biomass are currently analysed in more details.
Research Areas in Cameroon & CAR
The first study region is located in Adamawa region, central Cameroon, centered around Mbakaou lake near the departmental capital Tibati, encompassing the Mbam Djerem National Park. This region was chosen as it extends across a range of tropical vegetation types, from humid forests contiguous with the Congo Basin tropical forest belt in the south to savanna with narrow gallery forests in the north. Test regions will be selected in a later phase also in CAR.
Biomass Map for Prototype Region Completed
The Figure below shows the biomass map of the prototype region. The map is composed of a class of water and digital values of biomass displayed in six classes. This biomass map highlights the dense humid forests (Mbam and Djerem National Park) with biomass > 150 ton/ha, and the gallery forests in the savanna, with biomass lower than 100 ton/ha. It should be noted that large area of gallery forests and transitional forests (with biomass reaching 60-80 ton/ha) contain a large amount of carbon stocks, which is often neglected in carbon estimates.
Figure 1: Biomass map of the region of Adamawa (about 120 km x 130 km), central Cameroon. The biomass value for each 25 m pixel is in ton/ha. It is derived from 4 FBD ALOS-PALSAR data, acquired on June 18, 2010 (East track) and July 5, 2010 (West track). ALOS PALSAR data acquired in 2007, 2008, and 2009 have been used in addition for speckle filtering, to enhance the data radiometric quality and the product spatial resolution.
Figure 2 - extract of Figure 1: Biomass map of the 20 km x 20 km region North of Mbam and Djerem National Park, centered at 5° 57’17’’ N and 12° 46’ 08’’ E
Further Research Needed at Different Levels
- The map is currently validated using ground data collected from January to March 2012 by MESA-Consult.
- The refinement of the inversion algorithm aiming at estimating biomass at a refined spatial resolution (e.g. 15 m) will continue.
- The stocks of carbon in the savanna-forest ecotone will be quantified and their changes between 2007-2010 will be assessed.
- The method will be applied and assessed in Central African Republic.