Satellite data as a predictor for monitoring tree health and stress in reforestation projects

In many parts of the world, reforestation is an ongoing activity, but due to the deforestation processes (e.g. the change in soil conditions, agricultural expansion, and infrastructure expansion such as urbanisation or road building), the success rate of replanting is far from sure; therefore, it is essential to:

• have a good idea of the pre-planting conditions at the location,
• monitor the growth,
• improve the growing conditions whenever possible, and
• adapt the site selection criteria

In the proposed method, it is not possible to change the site selections of already planted locations, but it is possible to monitor the selected location and check under which conditions the trees are growing best. 

Several data sources are identified to predict plant health and stress, first to establish a baseline and, from this baseline, project into the future (short and mid-term). We compute the main vegetation index (NDVI) from the high-resolution image data provided by Planet (through the NICFI Basemaps for Tropical Forest Monitoring program). The historical NDVI values are obtained from the Sentinel 2 (and potentially LandSat) data at lower resolutions. Environmental conditions are added to the stress index by extracting the relevant meteorological parameters from the ERA5 database (temperature and precipitation) to compute the drought indices (e.g. KBDI/SPI/SPIE) and water availability (AWC) with the dominant soil type, supplemented with supporting indices from the satellite data (e.g. NDWI/SAVI/EVI-2).

For reforestation projects, it is vital to monitor the impact of environmental parameters on plant health and stress, and to assist with the forest maintenance of the sites, we built time series models for temperature, precipitation, and various vegetation indices to create a baseline for site-specific growing conditions. Deep Learning (DL) models like semantic segmentation based on Convolutional Neural Network (CNN) can be built on top of it using transfer learning to extract the features from pre-trained models using large (global) datasets. The model can not only predict tree health but can also be used to predict growing conditions in the near future by flagging out potential dry periods before they happen.

The high-resolution remote sensed products are available in the (sub)tropical zone [30N - 30S], while the lower resolution products and the ERA5 data have a global cover. The test sites in this study are example sites, but the developed method can be applied to any reforestation monitoring project. The result of the analysis is a near-term growth indicator, which can be used to adjust the growing conditions of the site, as well as assist with the site selection for new reforestation projects (based on the established baseline and predictions).

The next step, after validation, is to create a dashboard where the user can select any location (within the data domain) and construct the baseline and prediction, based on available information.