Trees Counting - gap analysis

P08: Trees counting

Download the product sheet gap analysis 

Maturity score

Mean: 2.4

STD: 0.70

Constraints and limitations

·  Cloud presence

·  Machine learning model uncertainty

Relevant user needs

UN31: Need to link tree planting parcels to estimate the number of trees planted.

R&D gaps

·  Cost of Very High Resolution (VHR) satellite imagery which is essential for the product.

·  Global inconsistency due to the diversity of tree species.

·  Limitations in homogeneous forests where the trees are connected to each other.

·  The lack of local in-situ data to train and validate the models.

·  Lack of spectral resolution to differentiate between tree species

Potential improvements drivers

·  Advances in AI models to detect and count individual trees.

·  Datasets for training and validating the models.

·  Price models for commercial EO data.

·  Fusion of hyperspectral and multispectral EO data.

Utilisation level review

Utilisation score

Mean: 2.14

STD: 0.64

No utilisation:

Unawareness of the existence of this EO product

Low utilisation

·  Higher cost of using the commercial EO product.

·  The current method (manually counting for a sample area and multiplying up to estimate the whole area) is considered good enough in terms of accuracy, reliability, and price.

·  Ground truth data is not sufficient for counting individual trees.

Medium utilisation

·  Unawareness of the existence of the best available commercial EO product with better specifications.

·  Higher cost of using the best available commercial EO product .

High utilisation

Critical gaps related to relevant user needs

Utilisation gap

UN31: Need to link tree planting parcels to estimate the number of trees planted