Regenerated/Restored Area Methodology

The implementation of sustainable activities in ABF funded projects are monitored by measuring areas showing improved biophysical conditions through increases in vegetation cover over time. TerraBio uses satellite images and remote sensing to compare gains in vegetation before and after implementation, and moving forward. At Horta da Terra, differences in vegetation were monitored at the syntropic system and the restoration area for a 5-year period between 2017 to 2021. This time period was chosen to serve as a historical baseline to compare with for the following years of TerraBio implementation. Areas of improved biophysical conditions are calculated as the number of hectares detected as canopy cover gain within the intervention site(s).

Reporting details

We calculate this metric for each intervention site.

Table 1. Summary of reporting details

ABF KPI TerraBio metric Reporting Unit Measurement Unit
Improved Biophysical Conditions Regenerated/Restored Area within intervention sites Total ha

Methods Overview

  • Remote Sensing Product: change map
  • Input Data: Landsat time series (NDFI), tree cover mask, ?potential forest?
  • Methodology: LandTrendr, image interpretation in CEO, area analysis
  • Computing Environment: GEE, CEO for validation/area estimation
  • Output: Map with 5 classes:
    • Stable forest
    • Non-forest
    • Degradation
    • Deforestation
    • Regeneration
  • Note: early iterations of this anlysis used the term “vegetation gain”, but this has been amended to “canopy cover gain” to clarify that we are considering tree growth rather than grasses and shrus as the intervention we want to evaluate. We do not use the basic term of “forest gain” because we are including the possibile intervention types and tree growth of: naturally regenerated forest, planted forest, and agroforestry (e.g. shaded coffee).

Regenerated/Restored Monitoring Methods

Monitoring increases in canopy cover helps estimate and visualize which areas have been gaining tree vegetation through time. The areas are mapped using the LandTrendr algorithm, which leverages time series analysis using Landsat imagery data. The algorithm aims to filter out inter-annual noise in spectral signals and generate trajectory-based time series estimates. It accomplishes this by simplifying multi-year spectral trajectories into several straight-line segments that capture the progressing changes of the signal (Kennedy et al., 2018).

LandTrendr builds an image collection with one image per year that is a medoid-based composite of Landsat 5, 7, and 8 images. Here we used images from the dry months of July through September due to the persistent cloud coverage over the Amazon region.

The canopy cover gain algorithm was parameterized to estimate the “greatest” gain and to detect upward trends in the spectral signature of forested areas.

Area Estimation

Intro to area estimation (Olofsson et al. 2014).

We leveraged high- and medium-resolution optical imagery (Planet NICFI mosaics (Planet 2021), Google Earth Pro basemaps, Sentinel-2 (Farr et al. 2007, ESA 2015), and Landsat Collections) and ancillary datasets (MapBiomas products, the Global Forest Canopy Height (Potapov et al 2021), and NDFI time series) to assess regrowth using visual interpretation methods. Visual interpretation was done in Collect Earth Online (CEO), a free and open-source web-based tool that facilitates data collection and validation (Saah et al. 2019). The interpreter utilized a decision tree approach for classifying the validation samples.

For the validation of the disturbance and regeneration maps, the sample points were 30 by 30 meters square, mimicking the map output pixel sizes. The validation of both change maps (disturbance and regeneration) was done using the same set of points. We also used a simple semi-random sampling design with proportional class distribution where we extracted 600 points from both maps, therefore, 100 points for each class: degradation, stable non-forest, stable forest, deforestation, regeneration, different events (Table S1). A “different events” category was used to capture locations where the mapping methods determined a different class for the same location. This class can include regeneration events on non-forest areas followed by degradation or deforestation or disturbance events, most likely deforestation, followed by regeneration. The accuracy metrics (overall, user, and producer accuracies) and unbiased area estimates for each class were calculated through the ratio estimator approach [68] for when the strata are different from the map classes since we used the same sample points for disturbance map and regeneration map.

Farr, T. G. et al., 2007, The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, doi:10.1029/2005RG000183

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