A REPORT ON CARBON MAPPING IN TRANS NZOIA

BACKGROUND INFORMATION 

In recent times nations around the world have shifted and are advocating  for environmental conservation and sustainable development. This shift has prompted to the sustainable development goals the 13th goal being the climate action to combat climate change and its impacts.

There has also been a lot of talks on the c02 produced and how to minimise in order to protect the ozone layer. In this project the focus is on the CO2 that has been converted successfully to carbon and stored in plants.

Our study area being Trans Nzoia it is a region that is undergoing industrialization with the main activity of people being farming. The crops that are in the area include maize, coffee, sugarcane and other crops mainly for subsistence or as market gardening.

There are various forest in the study region such as kitale forest kabolet forest and other privately owned forest. Being a conventional rainfall belt it allows for the growth of various species of trees to thrive in the two rainy seasons.

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 INTRODUCTION 

 The spatial analysis of carbon production is a vital component of understanding the dynamics of ecosystems and their contribution to carbon sequestration, a critical factor in mitigating climate change. This project aims to leverage Google Earth Engine's powerful geospatial capabilities to analyze and visualize forest carbon production within specific counties in Kenya. 

By utilizing satellite imagery and advanced geospatial techniques, the project provides valuable insights into the carbon sequestration potential of different regions, aiding informed environmental management and policy decisions. The project begins by loading and processing shapefile data that defines Kenya's administrative boundaries, specifically focusing on individual counties. This step ensures a precise selection of the study area for subsequent analysis. 

The selected county serves as the primary unit for examination and allows for localized insights into carbon production dynamics. Through the integration of Landsat 8 satellite imagery, the project analyzes the temporal dynamics of vegetation using the Normalized Difference Vegetation Index (NDVI). The NDVI calculations facilitate the estimation of Aboveground Biomass (AGB) within the selected county, providing valuable data for carbon production assessments.

 Utilizing the principles of the Inverse Distance Weighting (IDW) spatial interpolation technique, the project estimates carbon production by converting AGB values to carbon content. The methodology involves applying conversion factors to transform biomass estimates into carbon measurements. These calculations yield comprehensive insights into the carbon sequestration potential of different areas within the selected county. 

The project's visualizations include time series charts that illustrate the trends in forest carbon production over time. These charts provide an invaluable perspective on the variations and dynamics of carbon sequestration, enabling stakeholders to identify trends and potential areas of concern. Furthermore, the project integrates visualization components by displaying the spatial distribution of key variables, such as mean NDVI and total carbon production, on an interactive map.

 This visualization enhances the accessibility of the results and aids in conveying complex spatial data to a wider audience. To facilitate data sharing and accessibility, the project concludes by exporting processed carbon data and NDVI imagery to Google Drive, ensuring that the results can be further analyzed and utilized for various research purposes. In essence, the project combines advanced geospatial technologies with ecological insights to provide a comprehensive understanding of carbon production within selected Kenyan counties.

 This endeavor contributes to the broader global effort to quantify and manage carbon sequestration, thereby playing a significant role in addressing climate change and promoting sustainable environmental practices.

 METHODOLOGY

• Load Shapefile Data:

• Load a shapefile table as a FeatureCollection using its asset ID.Ddefine the name of the target county you want to analyze.

• • Filter and Visualize the County:

  • Filter the national FeatureCollection to select the target county using the defined county name.

  • • Print information about the selected county.

    • Visualize the selected county on the map using a cyan color.

    • Load Landsat 8 Image Collection:

    • Load a Landsat 8 image collection, filtering it based on the study area and date range.

    • • Select specific bands (NIR, Red, SWIR1, SWIR2) from the images for analysis.

    • Calculate NDVI:

    • Define a function to calculate the Normalized Difference Vegetation Index (NDVI) for each image.

    • Create a new image collection by mapping the NDVI calculation function over the original collection.

    • Estimate Aboveground Biomass (AGB):

    • Define a function to estimate Aboveground Biomass (AGB) using an example AGB estimation model.

    • Create a new image collection by mapping the AGB estimation function over the NDVI image collection.

    • Convert AGB to Carbon:

    • Define a function to convert AGB to Carbon using an example conversion factor.

    • Create a new image collection by mapping the AGB to Carbon conversion function over the AGB image collection.

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    • • Calculate and Visualize Time Series:

      • Calculate and visualize the time series of carbon production within the study area.

      • Create a chart displaying the time series of carbon values.

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      • Add Layers to Map:

      • Define visualization parameters for NDVI and carbon production.

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      • Add layers of mean NDVI and total carbon production to the map, clipped to the study area.

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      • Export Carbon Image to Google Drive:

      • Choose an image from the carbon collection for export (e.g., sum of Carbon).

      • Define the target scale (resolution) for downsampling.

      • Downsample the carbon image using the resample() function.

      • Export the downsampled carbon image to Google Drive.

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      • Export NDVI Image to Google Drive:

      • Export the original NDVI image collection to Google Drive.

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      • Loading and visualising the data in arcgis and further spatial functions on the images.

      • We downloaded the images from drive and loaded to arcmap.

      • we created random points and sampled them which we would use in interpolation after removing all null values..

      • We sampled the points and interpolated using IDW to make the data continuous and smooth.

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      • FINDINGS

        
        

        
        

        Findings for Trans Nzoia County:The in-depth spatial analysis of carbon production within Trans Nzoia County, Kenya, has yielded valuable insights into the distribution and dynamics of carbon storage within its forested ecosystems.

        • The analysis has revealed distinct carbon storage patterns within Trans Nzoia County. Areas characterized by dense vegetation, including forests and wooded areas, exhibited higher carbon storage levels. This emphasizes the importance of these regions as significant carbon sinks within the county.

        • Temporal Variations in Carbon Sequestration: The project's time series analysis showcased temporal fluctuations in carbon sequestration. These variations were influenced by seasonal changes in vegetation growth, precipitation, and other climatic factors. Such temporal dynamics highlight the need for continuous monitoring to capture nuances in carbon storage trends.

        • • Local Implications for Environmental Management: The findings carry practical implications for local environmental management strategies. Trans Nzoia County can utilize these insights to prioritize the preservation of carbon-rich areas, plan reforestation initiatives in regions with lower carbon storage, and implement policies to promote sustainable land use practices.

          • Contributing to Climate Change Mitigation: By understanding the nuances of carbon storage within Trans Nzoia County, this project contributes to broader climate change mitigation efforts. The data underscores the county's role in sequestering carbo