Projecting future land cover scenarios is critical also for assessing the impact on carbon mass. The traditional method has been to use a uniform carbon figure across a region (Figure 8). In our case, we have a uniform carbon figure for all forested areas per hectare and we can simply calculate the loss of carbon mass due to projected deforestation. However, using a proxy of forest land cover, we can refine this process by redistributing the gross carbon mass associated with all forested areas based on the forest’s NDVI (Figure 9) so that the intensity of the NDVI will be our spatial proxy, i.e., the higher the NDVI, the more carbon. Using the projected land cover map to 2015, we identified only the deforested areas and redistributed the gross carbon mass using the NDVI (Figure 10). Assuming business as usual, the table shows the potential additional loss of carbon mass using a non-uniform carbon mass surface (Figure 11). Using modeling tools such as GIS, we can present various scenarios with differing potential impacts for designing carbon offset projects while simultaneously managing biodiversity.
great project! I was wondering how you applied the carbon density surface to your NDVI? i.e. what relationship are you using to have higher carbon for higher NDVI?
ReplyDelete-aurelie shapiro
WWF
It was applied by using the DISAGGREGATE module in the IDRISI Taiga GIS and RS software. This module redistributes the total amount of carbon density following the spatial pattern of NDVI. It is assumed that the is a linear relationship between the NDVI and the carbon density.
ReplyDeleteStefano
I think there are two fundamental issues that may be incorrect with this approach.
ReplyDelete1) NDVI- In theory is related to LAI and growth. EVI is fPAR and growth. So, what you are really saying here is that more LAI equals more biomass.
The issue I see for REDD project development is whether you are using Landsat-based NDVI or MODIS-based NDVI. Landsat-based NDVI is obviously higher spatial resolution, and geographers like to use it. For REDD PDD's, you can very simply develop a baseline landcover change map with 2 landsat images, but you still need the biomass map. The issue with landsat style sensors is that you are lucky to get one cloud free image every five years. However, for REDD projects post validation, you must monitor emissions and removals at the site on an annual basis prior to verification of a credit every 5 years or so. This means that you need to be aware of change over both space and time on an annual basis, so REDD projects are really more fundamental to ecology rather than geography. Most people don't know this about post-validation requirements because there has still been no verification of a REDD credit under VCS or CCB as of today. If you are using MODIS NDVI data, the real issue is the date that you use to interpolate NDVI to biomass with your method. For example, MODIS NDVI images for the same date between 2001-2009 can vary inter-annually at up to 40%. This is without deforestation / forest deg. Woody biomass stocks normally don't change inter-annually at these levels, but vegetation growth (i.e., LAI) does. This is because of growing season phenology is not based on a Julian calendar. Or in other words, LAI changes annually with or without deforestation / degradation and this is because of climate change. This means that when you monitor biomass stocks inter-annually with MODIS data, you have to be very careful not to to confused with the inter-annual impacts of climate change on growth phenology vs. human induced impacts through deforestation / forest deg. So, I wonder whether this method is at all feasible for REDD project or if it is really better suited for a masters-level research project.
2) Real world data. Have you ever correlated your interpolated data with real biomass plots? From my experience, there is normally a negative relationship between NDVI/EVI and woody biomass stocks. Meaning, there is normally greater vegetation growth / production in young forests at lower biomass than in older with higher biomass, because older forests grow at slower rates and do not grow forever into the sky. This would essentially mean your assumption is the inverse of reality.
Ultimately, if you / IDRISI sell the method as scientifically sound for REDD project development, you are assuming a certain liability in what you are selling. If REDD project developers follow your method, but the results are proven wrong when they sell a REDD credit, or if the project developers get sued for selling bad REDD credits to polluters, you / IDRISI may get sued if you are selling snake oil.
Hi John,
ReplyDeleteSorry for the tardy response.
The purpose of using NDVI to disaggregate Carbon for the study presented here was to show the capability of the tool to use spatial measure of carbon (as opposed to a uniform measure of carbon to the whole forest cover—that IDRISI also supports). At the moment of the study spatial maps of carbon (such as Saatchi’s Amazon Basin Aboveground Live Biomass Distribution Map: 1990-2000) were not available and therefore it was decided to disaggregate the constant carbon value based on NDVI as a form of demonstrating the capabilities of the Land Change Modeler tool.
Since ecologists can’t sample biomass at every portion of the Earth, the idea of Saatchi’s AGLB map is to establish the relationship between the ecological field studies with remotely sensed information such as MODIS-LAI, MODIS-NDVI, QSCAT, SRTM, MODIS VCF, Land Cover etc. to develop a model of AGLB distribution. Many maps of carbon are being developed applying different approaches, including Saatchi’s, Asner’s and IMAZON’s biomass estimation, to name a few. It is important to evaluate the sensitivity of baseline values to the biomass data input for REDD projects.
As a final note, Clark Labs have implemented the carbon baseline development methodology developed by the BioCarbon Fund. This tool is flexible and allows the user to either input a carbon map or a homogeneous carbon value for each forest class under consideration. The practitioner should evaluate the accuracy and validity of the biomass maps or constant values used and the implications of such choice to the final project baseline.
I liked your project. It is valuable and informative. Same question clicked in my mind. If you need any assistance regarding Predictive modeling then we are here for you.
ReplyDeleteIt is assumed that the is a linear relationship between the NDVI and the carbon density. Child Modeling Denver
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