High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision

By Jonathan P. Dandois, Erle C. Ellis. 2013

539 citations recorded by the source corpus.

High spatial resolution three-dimensional (3D) measurements of vegetation by remote sensing are advancing ecological research and environmental management. However, substantial economic and logistical costs limit this application, especially for observing phenological dynamics in ecosystem structure and spectral traits. Here we demonstrate a new aerial remote sensing system enabling routine and inexpensive aerial 3D measurements of canopy structure and spectral attributes, with properties similar to those of LIDAR, but with RGB (red-green-blue) spectral attributes for each point, enabling high frequency observations within a single growing season. This “Ecosynth” methodology applies photogrammetric “Structure from Motion” computer vision algorithms to large sets of highly overlapping low altitude (< 130 m) aerial photographs acquired using off-the-shelf digital cameras mounted on an inexpensive (< USD$4000), lightweight (< 2 kg), hobbyist-grade unmanned aerial system (UAS). Ecosynth 3D point clouds with densities of 30–67 points m− 2 were produced using commercial computer vision software from digital photographs acquired repeatedly by UAS over three 6.25 ha (250 m × 250 m) Temperate Deciduous forest sites in Maryland USA. Ecosynth point clouds were georeferenced with a precision of 1.2–4.1 m horizontal radial root mean square error (RMSE) and 0.4–1.2 m vertical RMSE. Understory digital terrain models (DTMs) and canopy height models (CHMs) were generated from leaf-on and leaf-off point clouds using procedures commonly applied to LIDAR point clouds. At two sites, Ecosynth CHMs were strong predictors of field-measured tree heights (R2 0.63 to 0.84) and were highly correlated with a LIDAR CHM (R 0.87) acquired 4 days earlier, though Ecosynth-based estimates of aboveground biomass and carbon densities included significant errors (31–36% of field-based estimates). Repeated scanning of a 50 m × 50 m forested area at six different times across a 16 month period revealed ecologically significant dynamics in canopy color at different heights and a structural shift upward in canopy density, as demonstrated by changes in vertical height profiles of point density and relative RGB brightness. Changes in canopy relative greenness were highly correlated (R2 = 0.87) with MODIS NDVI time series for the same area and vertical differences in canopy color revealed the early green up of the dominant canopy species, Liriodendron tulipifera, strong evidence that Ecosynth time series measurements can capture vegetation structural and spectral phenological dynamics at the spatial scale of individual trees. The ability to observe canopy phenology in 3D at high temporal resolutions represents a breakthrough in forest ecology. Inexpensive user-deployed technologies for multispectral 3D scanning of vegetation at landscape scales (< 1 km2) heralds a new era of participatory remote sensing by field ecologists, community foresters and the interested public.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/652204e3f099356db0292a10142d5e8e6b78f4e4

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Citations in the corpus

Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level (2015, methodology): “Photogrammetrically produced UAV Digital Surface Models (DSM) and Canopy Height Models (CHM) that allow for detecting individual tree crowns in forests have been computed in [31] and [32].”
Unmanned Aerial Vehicles (UAVs) in environmental biology: a review (2019, methodology): “For example, Dandois and Ellis (2013) used automated UAV image acquisition techniques and highspatial resolution multispectral 3D data sets/point clouds to assess structural dynamics of forest canopy and to repeatedly predict the forest aboveground biomass and carbon content. This study was performe”
3D modelling of individual trees using a handheld camera: Accuracy of height, diameter and volume estimates (2015, background): “Like LiDAR, SfM-MVS produces spatially accurate point clouds (Dandois and Ellis, 2013; Fritz et al., 2013; Liang et al., 2014a; Morgenroth and Gomez, 2014), and thus has the potential to provide accurate estimations of tree size and architecture.”
Structure from motion photogrammetry in physical geography (2016, background): “, 2014) and aboveground forest biomass (Dandois and Ellis, 2013).”
Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests (2017, methodology): “The spectral information data provide detailed information on the 3D structural change of the forest canopy [22], and previous studies using airborne Lidar suggested that adding multispectral or hyperspectral data improved the estimation of biophysical properties [26–28].”
Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy (2017, methodology): “Other studies reported RMSEs of residuals of the bundle adjustment transformation on used GCPs as low as a few millimetres [27], or slightly over 1 cm [28], while the flight altitudes were higher—40 m and 120 m. Dandois and Ellis [36] conducted 3D mapping of vegetation dynamics with the use of Ecosy”
Assessment of Olive Tree Canopy Characteristics and Yield Forecast Model Using High Resolution UAV Imagery (2020, methodology): “LiDARs were also used for olive tree mapping and canopy characteristics extraction [12,13].”
Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest (2017, background): “Hence, in the case of close canopy forests, for accurate estimation of tree location and canopy attributes from UAV-SfM, external sources might need to be relied on for supplying DTMs [58,80,95].”
On the Use of Unmanned Aerial Systems for Environmental Monitoring (2018, unknown): “…[109,110]; plant species invasion [111]; restoration ecology [112]; disturbances [113]; phenology [114]; pest infestation in forests [115,116]; and land cover Many studies have focused on the retrieval of vegetation structural information to support forest assessment and management [118,119].”
Inventory of Small Forest Areas Using an Unmanned Aerial System (2015, methodology): “A first attempt to model forest biophysical properties by means of UAS-SfM data was presented by Dandois and Ellis [9], who used a hobbyist-grade UAS integrated with a digital camera for multi-temporal 3D and spectral data acquisition over a deciduous forest.”
Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds (2016, methodology): “For instance, Dandois and Ellis [14] have demonstrated that spectral information captured from a UAV is able to track changes in canopy relative greenness.”
Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure (2015, methodology): “The ‘Ecosynth’ UAV-SFM remote sensing tools and approach [14,22] are used to characterize forest canopy structure across three Temperate Deciduous forest sites in Maryland USA.”
Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion (2018, methodology): “Third, SfM typically has much higher point density, often by more than an order of magnitude compared to typical airborne lidar data [33], providing fine-scale detail about forest structure (e.”