Mapping Physiognomic Types of Indigenous Forest using Space-Borne SAR, Optical Imagery and Air-borne LiDAR

Abstract

Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from the European Space Agency (ESA) Sentinel-1 and 2 missions, Advanced Land Orbiting Satellite (ALOS) PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest. A five-fold cross-validation (repeated 100 times) of ground data showed that the highest classification accuracy of 80.5% is achieved for bands 2, 3, 4, 8, 11, and 12 from Sentinel-2, the ratio of bands VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) from Sentinel-1, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on optical bands alone was 72.7% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.4%. The classification accuracy is sufficient for many management applications for indigenous forest, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management.

Integrating plot‐based and remotely sensed data to map vegetation types in a New Zealand warm‐temperate rainforest

“…from cloud cover) were ignored. We included seven bands shown to distinguish broad NZ forest types nationally (Appendix S1; Dymond et al, 2019). Red edge and short‐wave infrared (SWIR) bands with a native spatial resolution of 20 m were pan‐sharpened to 10 m using a 7 × 7 local correlation filter (Dymond & Shepherd, 2004), choosing the 10‐m band that provided the best correlation for each pixel.…”

Comparative Analysis of Single-View and Multi-View Airborne SAR Positioning Error and Course Planning for Multi-View Airborne SAR Optimal Positioning

“…Synthetic aperture radar (SAR) has the advantages of all-sky and all-weather imaging, and is an important tool for high-resolution earth observation [1][2][3]. Accurate target location is one of the important uses of SAR [4,5], and SAR image positioning is an effective means to obtain three-dimensional position information of the target, which has important application value in digital elevation model (DEM) generation of imaging area, topographic map drawing and accurate interpretation of military targets.…”

New Zealand Environmental Data Stack (NZEnvDS): A standardised collection of spatial layers for environmental modelling and site characterisation

“…Here we describe Version 1.0 of the New Zealand Environmental Data Stack (NZEnvDS), a package of 72 environmental layers comprising 41 climate variables, eight soil variables, 18 topographic/terrain variables, and six geographical distance variables. Currently we do not include layers depicting land use and land cover, or remotely sensed variables depicting vegetation properties, but these could be included in future versions because they are known to be good predictors of biodiversity patterns (Müller et al 2015;Dymond et al 2019). Layers are provided at 100 m resolution and comprise all the existing layers from LENZ, and additional layers calculated from the source climate variables, including equivalents to all 19 WorldClim variables.…”