In the IFEM module “GIS for NRM” taught by Nicole Albert, Theo Driftmann, Uri Weingarten und Charlotte Fournet worked on Ecosystem Service Valuation/Accounting (ESV)” through the application of GIS related methodology.

ESV is a largely discussed concept since some see great potential in the notion of assessing the underlying monetary value of ecosystems to be able to make more meaningful decisions.  Modern science and technological advancement made it possible to visualize the complex reciprocal relationships of ecosystems and socio-economic systems, opening the door for broad scale analysis. A few examples of “Ecosystem Services (ESS)” are provision of habitat, crop pollination, carbon storage and sequestration, water purification, recreation and sediment retention.

Lately, scientific and ethical concerns have been raised, claiming that gaps and biases in the evaluation might underestimate the importance of natural capital.  Some refer to the high level of complexity within ecosystems, subjectivity in valuation and problems of quantification for entangled parameters (Yang, 2018). However, since Costanza first introduced the idea in 1997, measures have been taken to further refine and develop more effective approaches that can be replicated internationally. The integration of ESV into decision-making processes by governments and organisations might provide effective tools to attend societal needs in the present and future.


One of these tools, is the “InVest-Toolset” developed by the NatCap-Program of Stanford University which features a variety of models. For the student research the “Carbon Sequestration- Model” was chosen to estimate the total amount of carbon present and to analyse the changes for the timeframe of 2000-2014 within the “Queen Elizabeth National Park” in Uganda. The model requires input of different data sources, such as a land cover map, a biophysical table (relating the different land cover types to the amount of carbon), estimates for market prices of a metric ton of carbon and more. Land cover maps based on remote sensing data (Sentinel 5, 30*30m) with LC classes “Forest”, “Grassland”, “Wetland”, “Cropland”, “Settlement” and “Other” were acquired from the RCMRD. The biophysical table was built on literature research and the estimated price of a metric ton of carbon was taken from Tol´s publication “The economic effects of climate change” (Tol, 2009).

The product was pre-processed within QGIS to match the precise requirements of the model. In addition to that, the output of the model had to be postprocessed with ArcGIS for further analysis. The output of the model can be visualized immediately, since .tif files are provided for the monetary valuation [$/pixel], carbon pools [t/ha/pixel] and the respective changes during the observed time period. (see Figure 1&2) A summary for the monetary value is also provided as html-file.


Through the model a loss of 2.644.638 metric tons of carbon within the time frame of 2000 and 2014 was estimated. This equates to a monetary loss of 116.667.322 $. This value is based on the concept of damage costs associated with the release of an additional ton of carbon, also referred to as the social cost of carbon (EPA, 2013). Furthermore, a change of LC classes was observed. There is a significant increase in “Crop- and Grassland” and a corresponding decline of “Forests” and “Wetlands”. The loss of total amount of carbon can be explained by the fact that LC classes “Forest” and “Wetlands” are much more significant carbon pools, storing 75% of the carbon while only accounting for 32% of the whole area covered. Therefore, these LC classes hold specific value for the QENP in terms of carbon storage and sequestration.


Certainly, the InVest-Model for “Carbon Storage and Sequestration” can provide the user with a useful tool for a quick estimation of the given situation.  On top of that, more variables can be added into the modelling. The user-guide comes with sources and examples for data needs, which proved to be very helpful. Furthermore, questions are likely to be answered within the online forum.  

A point of critique is that the results of the model are cumulative. This becomes an obstacle within the further analysis. The changes in carbon pools and the respective monetary values are depicted within the map, but they are not directly linked to the different LC classes. This leads to a rather unprecise result if one is interested in more diversified results. A point of interest could be the share of ES-types for overall value/change of value provided, area covered by certain ES-types and the share of carbon pools/share of carbon pools. Additionally, one should know that the model is a simplification of reality and the results should be interpreted with care. There are three main limitations (Sharp et al, 2021):

– An oversimplification of the carbon cycle, only taking changing carbon values into account when one LC class is moving into another.

– The model can only be as precise as the input data. Most of the time during the research project was spent with inquiry and refinement of data sources. Hence, the accuracy of the results depends more on the acquisition of meaningful and precise data than actual work with the model. – Carbon sequestration usually describes a non-linear trend, while the model assumes a linear change of carbon. It is stated that a linear trend of carbon sequestration tends to underestimate the actual value, due to discounting.


To sum up, the InVest-Model “Carbon Storage and Sequestration” is a great tool to get an overview about the situation within a given research site. The combination of the model and remote sensing products allows a rapid and convenient spatial analysis for various parameters.

However, to achieve more precise results an extended amount of time should be invested into to collection of meaningful and fine-grained data. On top of that, field surveys should be taken into consideration to verify the results of the model. Optionally, another approach should be taken if one is eager to have more options and freedom to depict and analyse the collected data.   

Text: Theo Driftmann


EPA. (2013). “Social cost of carbon”. United States Environmental Protection Agency (EPA): Washington, DC, USA.

Sharp, R., Tallis, H. T., Ricketts, T., Guerry, A. D., Wood, S. A., Chaplin-Kramer, R., … & Vogl, A. L. (2014). “InVEST user’s guide. The Natural Capital Project: Stanford”, CA, USA.

Tol, Richard S. J. (2009): “The economic effects of climate change”. In: Journal of economic perspectives 23 (2), S. 29–51.

Yang, Q., Liu, G., Casazza, M., Campbell, E. T., Giannetti, B. F., & Brown, M. T. (2018). “Development of a new framework for non-monetary accounting on ecosystem services valuation. Ecosystem Services”, 34, 37–54. doi:10.1016/j.ecoser.2018.09.006