As Community Fisheries operate under a government mandate engaging the local authorities - especially the Fisheries Administration Cantonment and local Commune officials - is critical to the success of any engagement with a CFi. Local authorities need to understand, and support, Conservation International’s engagement with a CFi. First, we meet with the Fisheries Administration at national and local levels, presenting our programme, and identifying potentially suitable CFi’s. These meetings build relationships with senior government officials and obtain information such as local contacts within potential CFi’s. Having established high level support we contact local authorities such as the commune and in briefing them of our approach gain an appreciation of each CFi’s current status, challenges and opportunities.
Establishing the support from senior government officials is an essential first step. Then the local authority’s participation is required as they participate in the planning process and provide official recognition of legal documents associated with CFi development. Ideally the implementation team can build on established links with relevant local authorities. However, they must understand the context within which these local authorities operate and how our CFi development activities enhance the local authorities’ roles and responsibilities. This process needs to be conducted by senior project staff with experience in government relations.
Early engagement with local authorities is important as their involvement is crucial to the success of any engagement with a CFi. They will also provide the project team with additional information on CFi capacity and increase the likelihood of successful engagement with a CFi.
Drones play a pivotal role in the 3LD-Monitoring system, complementing other data collection methods.Drones are essential tools in partner countries to fortify technical skills among local staff. These skills encompass flight planning, navigation and image evaluation. The drone monitoring aims to empower project staff to capture data tailored for photogrammetric analyses, from which crucial geoinformation emerges.
The drone mapping methodology encompasses five stages, with the first two focusing on drone operations:
Mapping mission preparation (desktop work)
Mapping mission execution (fieldwork)
Development of Digital Surface Model (DSM) & Orthomosaic generation (desktop work)
Data analysis and refinement (desktop work)
Integration into the prevailing data system (desktop work)
Drone data aids in evaluating indicators linked to carbon/biomass, such as mortality rates and forest types. Notably, with the application of allometric equations and proper characterization of the land type, above-ground biomass estimations of trees can be determined.
Drones with pre-set flight planning capability ensure seamless orthophoto creation from individual images. This enables individual snapshots to seamlessly merge into an orthophoto (aerial photograph corrected for distortions, allowing accurate measurements). It's also vital to consider the availability of these drones in the local markets of partner countries. Leveraging local knowledge by involving local academia is paramount in this process. They can provide essential allometric equations, grounded in tree height, that facilitate precise biomass calculations.
Drones generate high resolution images, allowing a detailed overview of land cover changes, tree survival and erosion rates, among others. Combined with field data, drone-based monitoring is strengthened, guaranteeing a sound monitoring.
The heterogeneity of trees and vegetation density often hinders a sound extraction of common key points between the images, which is necessary to estimate the heights and other indicators. In this regard, increasing the overlap between images to a minimum of 85 % frontal and side overlap can improve the extraction of key points. Also, increasing the flight height of the drone reduces perspective distortion, which facilitates the detection of visual similarities between overlapping images. However, too much overlapping, i.e., high overlapping percentages result in higher amount of data, making data processing more time intensive.
Another aspect already mentioned is the availability of suitable drones in the partner countries. Importing drones to the respective countries is difficult, and bureaucratic barriers persist.
Satellite data forms the bedrock of the 3LD-Monitoring system, harnessing the capabilities of open-source imagery from the Copernicus Sentinel-2 and LANDSAT satellites. An algorithm, meticulously developed by Remote Sensing Solutions (RSS) GmbH, revolutionizes this process. Users can seamlessly submit the shapefile of their area of interest, prompting the algorithm to automatically fetch and analyze relevant data. A spectrum of robust analyses are conducted including the 5-year vegetation trend using NDVI for assessing vegetation gains or losses, 5-year vegetation moisture analysis through NDWI, and a nuanced 5-year rainfall trend evaluation. Additionally, the algorithm facilitates the visualization of vegetation changes since the inception of the project, bolstering the monitoring framework with dynamic insights. Satellite data, a vital component of the 3LDM-Monitoring system, leverages open-source imagery from the Copernicus Sentinel-2 mission and LANDSAT satellites. For predefined areas, this data is automatically fetched and analyzed for specific parameters. Key analyses include a 5-year vegetation trend using NDVI as a proxy for vegetation gains or losses, a 5-year vegetation moisture trend through NDWI, and a 5-year rainfall trend. In addition vegetation changes from project start can be visualized.
Effective use of this building block hinges on users drawing and saving areas in GIS platforms like QGIS. Additionally, enhancing the shapefile with project specifics, such as start dates and FLR type, optimizes analysis. Proper training in these skills ensures accurate data input and tailored monitoring, making capacity building in these areas essential if not present.
While satellite data, especially open-source, offers broad insights, its capability for species identification is highly restricted, if not unattainable. This limitation emphasizes the indispensable role of field work in discerning species composition and characteristics. Additionally, understanding the innate constraints of satellite imagery, especially with young tree plantations, reinforces the need for integrating field and drone data to gain a comprehensive view of forest terrains.
