SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs

06/11/2020
by   Jonathan Williams, et al.
0

Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crowns from UAV imagery; (b) machine learning of species based on spectral and textural features within individual tree crowns, and (c) automatic segmentation of orthomosaiced UAV imagery into 'superpixels', using Simple Linear Iterative Clustering (SLIC). Creating superpixels reduces the dataset's dimensionality and focuses prediction onto clusters of pixels, greatly improving accuracy. To demonstrate SLIC-UAV, support vector machines and random forests were used to predict the species of hand-labelled crowns in a restoration concession in Indonesia. Random forests were most accurate at discriminating species for whole crowns, with accuracy ranging from 79.3 species, to 90.5 contrast, support vector machines proved better for labelling automatically segmented superpixels, with accuracy ranging from 74.3 species. Models were extended to map species across 100 hectares of forest. The study demonstrates the power of SLIC-UAV for mapping characteristic early-successional tree species as an indicator of successional stage within tropical forest restoration areas. Continued effort is needed to develop easy-to-implement and low-cost technology to improve the affordability of project management.

READ FULL TEXT

page 4

page 7

page 8

page 10

page 15

page 19

page 20

page 37

research
07/17/2020

Identification of Tree Species in Japanese Forests based on Aerial Photography and Deep Learning

Natural forests are complex ecosystems whose tree species distribution a...
research
10/16/2021

Automated Remote Sensing Forest Inventory Using Satelite Imagery

For many countries like Russia, Canada, or the USA, a robust and detaile...
research
11/04/2020

Monitoring the Impact of Wildfires on Tree Species with Deep Learning

One of the impacts of climate change is the difficulty of tree regrowth ...
research
05/23/2022

Vegetation Mapping by UAV Visible Imagery and Machine Learning

An experimental field cropped with sugar-beet with a wide spreading of w...
research
07/07/2021

Urban Tree Species Classification Using Aerial Imagery

Urban trees help regulate temperature, reduce energy consumption, improv...
research
08/23/2022

Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests

Tropical forests represent the home of many species on the planet for fl...
research
01/25/2018

A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management

In this work we demonstrate a rapidly deployable weed classification sys...

Please sign up or login with your details

Forgot password? Click here to reset