Although a significant decrease in the global deforestation rate has been noted in the last decade, in many developing nations, the deforestation rate still remains very high [1
]. Reducing greenhouse gas (GHG) emissions from deforestation has been long identified as having the greatest potential for global climate change mitigation [2
]. This is particularly the case in developing tropical countries where the largest source of GHG emissions are attributed to land use change from forest loss [3
]. In 2005, the United Nations Framework Convention on Climate Change (UNFCCC) initiated a process to investigate how the concept of Reducing Emissions from Deforestation and forest Degradation (REDD) could help combat the challenge of climate change due to GHG emissions in forest-rich developing countries [4
]. Through REDD, countries should gain economic incentives for demonstrating quantifiable carbon emission reductions from protecting their forests [5
]. However, fundamental to the success of REDD is the availability of robust and consistent methodologies for monitoring, reporting and verification (MRVs) so that the incentives paid out can be evidence-based, and linked directly to the amount of carbon emission reduction [3
]. As initiatives for REDD in tropical countries continue to develop, the need for a forest monitoring system that is both low cost and accurate is imperative, especially for many developing countries where funding for such forest monitoring activities may not always be readily available.
Existing low-cost alternatives to field-based methods for monitoring forest cover and change rely on satellite observations. Landsat multispectral imagery provides a zero-cost data source that offers the capacity to straightforwardly map forest cover and forest cover change (e.g., [6
]). However, this passive imagery is not able to determine carbon stock with sufficient accuracy [7
]. Radar observations can estimate carbon stock and change, but are challenging to process effectively. For areas of semi-arid sparse woodland, both of these methods are limited due to the spatial resolution of freely available data—to achieve higher spatial mapping accuracies would require data that are more expensive and less readily available.
Structure from Motion (SfM) photogrammetry using digital cameras on small, low-cost Unmanned Aerial Vehicles (UAVs) is therefore a potential cost-effective alternative for areas of woodland where the woody cover is sparse or patchy (such as many of the semi-arid savanna landscapes of sub-Saharan Africa). SfM has emerged recently as an inexpensive method for extracting the 3D structure of a scene from multiple overlapping photographs using bundle adjustment procedures [8
]. The ability of SfM to generate high quality 3D point clouds similar to the ones generated from Aerial Laser Scanning (ALS) is now widely understood and has been demonstrated in a number of studies [9
]. Its potential to characterise forest structures has long been realised but has been hampered by difficulties in the SfM algorithms to accurately perform image matching in densely vegetated areas [12
]. Until recent developments in 3D Vision (namely the Graphics Processing Unit (GPU) and parallel computing), complex image matching algorithms used in SfM were deemed impractical [14
]. The upsurge in the use of UAVs within the environmental sciences has also made it practical to acquire highly redundant, fine spatial resolution (>5 megapixels) aerial photographs with a large overlap (>80%) at low cost. A range of studies has demonstrated how SfM photogrammetry can be used to generate accurate Digital Surface Models (DSMs) over canopies using high resolution images from consumer-grade cameras mounted on these low altitude platforms [15
]. The utility of SfM was successfully demonstrated in [18
] with high resolution images obtained from a kite platform for estimating Above Ground Biomass (AGB) at the plot level. In [19
] UAV imagery was used to generate dense point clouds over forested areas to demonstrate a low cost alternative to LiDAR, and in [20
], the authors created ‘hybrid’ Canopy Height Models (CHMs) from SfM DSMs and LiDAR Digital Elevation Models (DEMs) which were comparable to LiDAR CHMs. However most of the previous studies reported poor performance in a closed canopy.
This paper focusses on evaluating the capability of SfM photogrammetry applied to aerial photography data from small (<20 kg mass) Unmanned Aerial Vehicles (Figure 1
) as a potential low cost solution for REDD monitoring within developing countries. The success of SfM is governed by image resolution (which in turn depends on the quality of the camera and lens used), degree of image overlap as well as relative motion of the camera with respect to the scene [21
]. This makes small UAVs the ideal platform for SfM because they operate at distances of only a few tens of meters above the ground, providing data with sub-decimetric spatial resolution—orders of magnitude finer than space-borne sensors with the capability to resolve individual trees and plants for biomass estimation [22
]. Recently, there has been an increase in the number of studies looking at developing low cost UAVs for forestry applications e.g., [23
] developed a conservation drone for <US$2000. While UAVs do not offer global or national-level coverage, as do satellites or large aircraft, they are generally considered cheaper to use than airborne platforms when focused over comparatively small areas. Their portability and ease of use also allow the user to carry out surveys as per local user requirements, thereby offering better temporal resolution than most platforms.
Remote Sensing (RS) had been previously identified as a possible solution for REDD monitoring because of its potential to be low cost and to provide global coverage making it cost-effective at the national scale for many forest countries [27
]. Typically, RS techniques for quantifying AGB use one of two main methods: (i) deriving a statistical relationship of AGB and a remote sensing variable or (ii) by assigning typical AGB values to different land cover classifications [28
]. In Optical RS, vegetation indices such as the Normalised Difference Vegetation Index (NDVI) have been successfully used to infer carbon stocks on a global scale using regression-based models (e.g., [29
]). Although the data used in most of these studies are free (e.g., Landsat) or of high temporal resolution (e.g., MODIS), they are also at a low spatial resolution (usually ≥ 30 m) which makes them unsuitable for estimating AGB at finer scales e.g., at plot level [30
]. The higher resolution data (e.g., IKONOS) are very expensive and not cost-effective for small projects, and are not always readily available for all areas [31
]. Data acquisition for space-borne optical sensors is also subject to cloud cover and illumination [32
]. Synthetic Aperture Radar (SAR) systems have successfully used radar backscatter to infer biomass and to extract forest metrics and species types [33
]. SAR sensors do not rely on solar illumination and are not affected by cloud cover [35
]. However, the data are technically challenging to process and generally SAR does not perform well in dense canopies due to early saturation issues [36
]. Temporal resolution is also an issue with SAR, as with most space-borne techniques, since the end-user has no control over the time periods for data acquisition. Airborne LiDAR has now become the method of choice for forestry applications because of its ability to generate 3D point clouds with centimeter accuracy [37
]. The point clouds allow for the extraction of forest metrics (e.g., canopy height) which have been used extensively to infer forest biomass at both plot level and individual tree level (e.g., [38
]). For biomass studies, LiDAR is now considered superior to optical sensors because it can penetrate through the woody canopy to better establish the terrain surface, and it is not affected by solar illumination, cloud cover (when clouds are high enough to fly below) or cloud shadow [39
]. However LiDAR data are very expensive and not very cost-effective for small applications as they require mobilisation of an airborne platform that is not always geographically close to the forested areas. Repeating a LiDAR survey on a regular basis to achieve a suitable temporal resolution is thus not an option for many users of LiDAR data [40
]. While LiDAR systems are becoming smaller and more compact, they are still orders of magnitude more expensive than small scale UAVs with a digital camera.
Thus, the main challenges for most RS solutions that hamper REDD monitoring for developing countries are associated with cost, temporal resolution and spatial resolution. UAVs and SfM have a strong potential for offering a local solution which addresses most, if not all, of the identified challenges.
This study therefore had two major aims: to evaluate the output from SfM photogrammetry applied to UAV data for estimating tree height by (i) comparing SfM derivatives (i.e., point clouds, Digital Elevation Models (DEMs) and Canopy Height Models (CHMs)) with corresponding LiDAR derivatives under open canopy conditions and (ii) comparing SfM derived CHMs with ground measured tree heights under closed canopy conditions. This research aims to address the literature gap pertaining to the application of SfM from UAV aerial photography as a potential low cost solution for REDD monitoring for developing countries.