Client: Ministry for the Environment
Riparian areas are the strips of land beside drains, streams, rivers, and lakes. Riparian planting has multiple environmental benefits including water filtration, erosion prevention, moderation of water flow, shading waterways, providing habitat for indigenous species, and keeping livestock out of waterways.
As part of the Ministry for the Environment’s Mahi mō te Taiao | Jobs for Nature programme a combined Pattle Delamore Partners/Lynker Analytics team assessed the suitability of different remote sensing methods including satellite, airborne and unmanned aerial systems to monitor these ecosystems.
Figure 1. Schematic of typical riparian zones (adapted from Dairy NZ, 2014).
For each sensor, we developed Machine Learning (ML) techniques to identify and map the extent of plants within five distinct and differentiated riparian systems. Planting data was collected in the field with the field schema including detailed vegetation categories to adequately record riparian survival.
Figure 2. Modelling results by sensor in 5 riparian test sites
Based on overall visual comparison and model accuracy scores, the UAV model provided the best detection however the Maxar model offered the most consistent visual output and the highest mean average accuracy. The first stage of the project therefore determined that high resolution satellite imagery from Maxar was the most suitable for a scalable monitoring system. Stage 2 involved the implementation of a Maxar model over 141 sites in the Taranaki region.
We used a combination of methods to prepare training data. First, PDP botanists generated a fully human annotated training dataset using super-pixels, then we used a remote sensing method called the Vegetation Texture Index (VTI) to create training data. The remote sensing VTI model was prepared using a combination of spectral indices and a texture index.
Figure 3. Remote Sensing training
We then combined these datasets to create the final training dataset which was used as input into a convolutional neural network or deep learning model to achieve semantic segmentation of the riparian ecosystem.
Figure 4. Model workflow
The final prediction model reported an overall classification accuracy of 72% against the validation data, however, with masking known waterways the model accuracy rises to 80% with unvegetated, woody and grass classes detected with F1 scores between 0.8 and 0.9. The model had difficulty resolving narrow features such as streams and herbaceous strips which affected the unvegetated and herbaceous classes. Masking of waterways, including narrow streams boosts the accuracy considerably and was recommended as a pre-processing step.
In conclusion, the developed model achieved the project objectives of determining riparian planting survival with an acceptable accuracy, mapping riparian vegetation into 6 distinct botanical classes. The method can be applied to predict riparian planting survival across New Zealand.
Deliverables:
Data outputs include the modelled riparian vegetation classification maps and related attributes including coverage of each class and the plant stature classification. The output includes a feature class with detailed planting information of each site.
The Technical Memo for the project is located within the Ministry for the Environment publications.