Trees Counting

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Product Category

• Land use
• Land cover
• Natural Disaster
• Climate Change
• Coast Management 

• Marine
• Earth's Surface Motion

Financial Domains

• Investment management 
• Risk Analysis 
• Insurance management 
• Green finance

User requirements 

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

Description

Tree counting require VHR satellite imagery to accurately and efficiently determine the number of trees within a specified area. This approach offers significant advantages over traditional ground-based methods, as it enables rapid, cost-effective, and large-scale tree counting without the need for time-consuming field surveys. This data, coupled with the ability to monitor changes over time, aids in assessing the value of forestry assets, estimating timber volume for investment or insurance purposes, and evaluating the environmental impact of forestry investments.

Spatial Coverage Target

Asset level

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

The process starts by inspecting optical VHR imagery (≤ 0.5 m) to identify a sample of individual trees to build a training dataset for a deep learning model. Then a deep learning model to be trained in optimoum way to detect the individual tree and, subsequently, identify the number of trees.

Input data sources

Optical: VHR based on the availability like Pleiades 1A/1B & NEO, WorldView2&3, and SPOT6/7

Radar: N.A

Supporting data:  N.A

Accessibility

Optical VHR imagery: commercially available on demand from EO service providers.

Spatial resolution

Optical VHR: ≤ 0.5 m

Frequency (Temporal resolution)

Optical VHR: Sub-daily to Daily

Latency

< 1 Day

Geographical scale coverage

Globally

Delivery/ output format

Data type: Raster

File format: GeoTIFF

Accuracies

Thematic accuracy: 70-80%

Spatial accuracy: 1.5-2 pixels of input data

Constraints and limitations

• Cloud presence
• Cost of VHR imagery
• Lack of training data
• Global inconsistency due to the diversity of tree species.
• Limitations in homogeneous forests where the trees are connected.

User's level of knowledge and skills to extract information and perform further analysis on the EO products.

Skills: Essential

Knowledge: Essential

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