Research

Deforestation Mapping

EO-Based Forest Cover Change Mapping Services will be 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 will be realized within REDDAF:

• Methodologies will be 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).
• Another aim is to develop robust radiometric calibration techniques to improve the usability of multi-sensoral data.
• Additionally, a combined spectral-geometric approach for cloud and cloud shadow detection is developed and tested in order to enhance interpretability of optical data.

Test Areas in Cameroon & CAR

The methods are developed in an area in the Center Province of Cameroon. Later on they will be tested in an area within the Central African Republic. The methods developed shall be 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. 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 based on morphological characteristics which 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.

Degradation Mapping

Development and Testing of EO Methods

A main objective in the R&D of REDDAF is 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 is to develop tools and processing chains which allow mapping:

• the extent of degraded areas and
• the degree of degradation.

Methods are developed to make use of various sensor data for degradation mapping. An approach based on spectral mixture analysis is enhanced by introducing multi-temporal aspects. Furthermore, options for 3D mapping are investigated in order to detect the gaps in the forest canopy induced by logging activities. Finally, the methods developed shall be integrated into optimized and cost efficient operational processing chains.

Test Areas in Cameroon & CAR

The methods for degradation mapping are developed in a Pallisco concession site in South-East Cameroon. Subsequent tests will take place in CAR.

Development of Improved SMA Analysis

A detailed concept was elaborated for developing a method for forest degradation mapping caused by selective logging, which overcomes the need for optical data shortly after the logging activity, which often may be restricted by cloud cover. Cuurently, an improved spectral mixture analysis (SMA) method (Asner et al., 2002) is developed which integrates multi-temporal regeneration signals. Thus, not only new gaps with bare soil may be used for mapping degradation, but also regeneration areas with new vegetation are considered in time series. This method is tested in the Pallisco concession site in South-Eastern Cameroon with recent degradation activities. New acquisition of additional relevant remote sensing data for the test site is currently on the way.

Testing Options for 3D Mapping

Furthermore options for 3D mapping of forest canopy to detect degradation have been investigated. The work comprises 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).

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

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.