Data collection

Using the Google Earth Engine (GEE) platform, Landsat TM/OLI series remote sensing data from 1990 to 2022 were collected, covering TM5, ETM+7, OLI8, and OLI9. Key spectral bands (near-infrared, red, and green light) were fused to ensure high-quality data for subsequent analysis.

  • Remote sensing images with ≤10% cloud cover were selected for radiometric and atmospheric corrections through batch processing.
  • Thematic information extraction leveraged band combinations that emphasized vegetation characteristics (e.g., NIR bands for vegetation reflectance peaks).
  • Limitations in resolution (spatial, temporal, and spectral) can introduce errors, necessitating robust corrections (radiometric/geometric).
  • Data fusion between satellites was crucial for achieving consistent long-term datasets, but this step required additional processing to align temporal and spatial resolutions.
Family showing their successful fish harvest through using the fish trap in a pond.
East and South Africa
Global Programme
Sustainable Fisheries and Aquaculture
The challenge
Our idea
Crafting the fish trap
Trials
Results
Family showing their successful fish harvest through using the fish trap in a pond.
East and South Africa
Global Programme
Sustainable Fisheries and Aquaculture
The challenge
Our idea
Crafting the fish trap
Trials
Results
Academic Communication

Results were disseminated via an academic paper in Ocean-Land-Atmosphere Research (a Science Partten Journal) and shared in AAASScience WeChat Public (Official Media of American Association for the Advancement of Science in China). The findings were also included as a case study in the Yangtze River Delta Pilot Site and included in the support of major research projects on oceanography by the National Natural Science Foundation (NSFC).

By systematically integrating remote sensing data, deep learning, and ecological analysis, the project has significantly advanced wetland conservation methodologies, offering scalable solutions for biodiversity preservation,  biological invasion control,  and ecosystem management globally.

Key Drivers of Vegetation Evolution

Exploring and analysing the key drivers of vegetation spatial and temporal distribution is of practical significance for monitoring the expansion of coastal wetland vegetation, species diversity conservation and sustainable management of coastal wetlands.

  • Spartina alterniflora was influenced by marine factors (e.g., salinity and wave height).
  • Phragmites australis and Suaeda salsa were driven by rainfall, anthropogenic activities, and interspecific competition.

Understanding these factors supports better management of invasive species and promotes biodiversity conservation.

Spatial and Temporal Characteristics Analysis of Wetland Vegetation

Using spatial-temporal data, the long-term distribution characteristics of wetland vegetation were analyzed. Tools such as the landscape pattern index, migration modeling, and expansion/decline dynamics identified distinct trends:

  • Spartina alterniflora patches showed high aggregation and a decreasing trend.
  • Phragmites australis and Suaeda salsa patches displayed lower aggregation, higher fragmentation, and increasing trends.
  • Vegetation migration patterns revealed significant spatial and temporal heterogeneity, with vegetation distributed in bands along the terrestrial gradient.
Data Quantification and Database Establishment

A comprehensive database was created, integrating remote sensing-derived vegetation cover with key environmental, climate, and human activity data. This includes metrics such as soil salinity, sea surface temperature, seawater salinity, and aquaculture pond locations, forming a robust foundation for further analyses.

Wetland Vegetation Type Identification

A Gaussian function was applied to vegetation index time series to extract candidate features, while a deep learning algorithm identified three major vegetation types (Spartina alterniflora, Phragmites australis, and Suaeda salsa). Field validation confirmed the model's accuracy, enabling precise vegetation classification from 1990 to 2022.

Data collection

Using the Google Earth Engine (GEE) platform, Landsat TM/OLI series remote sensing data from 1990 to 2022 were collected, covering TM5, ETM+7, OLI8, and OLI9. Key spectral bands (near-infrared, red, and green light) were fused to ensure high-quality data for subsequent analysis.

Evolve

Based on results from monitoring data and facilitated feedback discussions with the village grazing committees, rangeland restoration activities are identified as appropriate. This often requires the existing village grazing plan to be adapted and evolve with the changing state of the rangelands. For example, in Ngoley village, data collected over two years indicated one particularly problematic species (Sphaeranthus - locally called “Masida”) that proliferated significantly during a prolonged dry season and limited the regrowth of palatable species after the rains. To prevent further proliferation, an uprooting plan was designed and implemented based on the best practices for removing this particular species. Immediately after the first round of uprooting, the data show a drop in the species frequency and subsequent months of monitoring provide further evidence to suggest that native, palatable grasses are recovering in the treated plots. These targeted interventions directly contribute to GBF Target 1 by integrating biodiversity considerations into local planning and land use, and Target 2 by restoring degraded ecosystems. Furthermore, by improving ecological function and resilience, these efforts enhance the rangeland’s capacity to withstand climate variability, supporting both biodiversity and the well-being of local communities.

A close working relationship with village grazing committees is critical to develop, refine, and implement rangeland management plans. Where village grazing committees do not already exist, following existing government and traditional village structures, APW helps facilitate their formation, building capacity to manage rangelands. While there is incentive to sustainably manage grasslands, the implementation of restoration activities can be arduous. APW provides financial incentives in the form of stipends that expedite interventions while providing an additional benefit to the community members who participate. 

APW has learned the importance of working not just with village-level committees but also with larger ward-level governments. Many villages in northern Tanzania share rangeland or have adjacent pastures. Thus, it is necessary to work with neighboring villages to ensure continuity in management and connectivity of ecological benefits. Since adjacent villages may compete for high-quality rangeland, cooperative management of neighboring grazing areas is imperative. As villages are added to the program, gaps in ward-level management are filled by APW and other partners, moving one step closer to ensuring connectivity in a landscape shared by people, livestock, and wildlife.

In 2020, APW began conducting harmonization meetings that bring together different stakeholders from the village level, wards, divisions, districts, regions, different ministries, parastatal institutions, and NGOs among other stakeholders to discuss and streamline different agendas in regards to rangeland management in their different areas of work and also influence policy.