Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery

Melville, Bethany; Fisher, Adrian; Lucieer, Arko

doi:10.1016/j.jag.2019.01.013

Cited by 36 publications

(30 citation statements)

References 49 publications

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“…The advantage of UAV images over satellite images is their ability to provide local thematic information with much higher spatial and temporal resolutions [15]. UAV images with ultra-high spatial resolution [16,17] can improve the discrimination capability of various surface objects, leading to an increase in the number of detectable targets. Compared with satellite images, low-cost flexible control of unmanned aerial systems (UAS) enables easier acquisition of images at the desired times between sowing and harvesting of crops [12,15,16].…”

Section: Introductionmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification.

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Section: Introductionmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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“…It commonly provides spatially continuous data (raster data sets) over large geographic areas at scales where ground‐based data collection is simply not plausible (Pettorelli et al ., 2014 a ). Land cover (Scarth et al ., 2015; Melville, Fisher, & Lucieer, 2019), ground cover (Bastin, 2014), vegetation mapping (Sparrow & Leitch, 2009), flooding, fire location, severity and frequency (Maier, Ludeker, & Gunther, 1999; Edwards, Russell‐Smith, & Maier, 2018), and vegetation structure (Gill et al ., 2017; Scarth et al ., 2019) are just a few environmental phenomena that are routinely correlated with changes in spectral reflectance. Calibration of surrogate measures and biological/environmental variables are often identified from previous targeted monitoring work (Bunce et al ., 2007).…”

Section: An Explanation Of Monitoring Typesmentioning

confidence: 99%

Effective ecosystem monitoring requires a multi‐scaled approach

et al. 2020

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Ecosystem monitoring is fundamental to our understanding of how ecosystem change is impacting our natural resources and is vital for developing evidence‐based policy and management. However, the different types of ecosystem monitoring, along with their recommended applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecosystem monitoring, leading to poor program development, implementation and outcomes. In an effort to develop a more consistent and clear understanding of ecosystem monitoring programs, we here review the main types of monitoring and recommend the widespread adoption of three classifications of monitoring, namely, targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables questions relating to where and when ecosystem change is occurring to be addressed. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the causes of ecosystem change. The classification system is flexible and can incorporate different interests, objectives, targets and characteristics as well as different spatial scales and temporal frequencies, while also providing valuable structure and consistency across distinct ecosystem monitoring programs. To support our argument, we examine the ability of each monitoring type to inform on six key types of questions that are routinely posed for ecosystem monitoring programs, such as where and when change is occurring, what is the magnitude of change, and how can the change be managed? As we demonstrate, each type of ecosystem monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, scientists and land managers can design programs best suited to their needs. Finally, we assert that for our most serious environmental challenges, it is essential that we include information from each of these monitoring scales to inform on all facets of ecosystem change, and this is best achieved through close collaboration between the scales. With a renewed understanding of the importance of each monitoring type, along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges.

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“…Several recent studies have used UAVs to estimate FVC, either on their own or in conjunction with coarser-resolution satellite images, in environments ranging from the northern Fennoscandian Arctic [63] to the Qinghai-Tibetan Plateau [64,65] to Australian rangelands [66]. Approaches have included random forest regression and object-based image analysis [66], spectral unmixing [66,67], and upscaling of binary vegetated-unvegetated classifications from UAV imagery to satellite imagery using vegetation indices [63], an approach similar to the one used in this study. Several of these studies show that FVC estimates are more accurate when upscaled to 30-m resolution or coarser, with lower FVC accuracy at finer spatial resolution [63,64,66].…”

Section: Introductionmentioning

confidence: 99%

“…Approaches have included random forest regression and object-based image analysis [66], spectral unmixing [66,67], and upscaling of binary vegetated-unvegetated classifications from UAV imagery to satellite imagery using vegetation indices [63], an approach similar to the one used in this study. Several of these studies show that FVC estimates are more accurate when upscaled to 30-m resolution or coarser, with lower FVC accuracy at finer spatial resolution [63,64,66].…”

Section: Introductionmentioning

confidence: 99%

“…While most prior studies have used UAVs to estimate FVC in grasslands, shrublands, or agricultural environments (e.g., Reference [63,64,66,67]), this study uses UAV imagery to quantify woody vegetation cover in a desert riparian forest. Such research contributes to the growing understanding of the extent of dryland woody cover, which recent studies have shown to be much higher than previously estimated by using very high-resolution remotely sensed imagery [68,69].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery

et al. 2020

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Ephemeral rivers in arid regions act as linear oases, where corridors of vegetation supported by accessible groundwater and intermittent surface flows provide biological refugia in water-limited landscapes. The ecological and hydrological dynamics of these systems are poorly understood compared to perennial systems and subject to wide variation over space and time. This study used imagery obtained from an unmanned aerial vehicle (UAV) to enhance satellite data, which were then used to quantify change in woody vegetation cover along the ephemeral Kuiseb River in the Namib Desert over a 35-year period. Ultra-high resolution UAV imagery collected in 2016 was used to derive a model of fractional vegetation cover from five spectral vegetation indices, calculated from a contemporaneous Landsat 8 Operational Land Imager (OLI) image. The Normalized Difference Vegetation Index (NDVI) provided the linear best-fit relationship for calculating fractional cover; the model derived from the two 2016 datasets was subsequently applied to 24 intercalibrated Landsat images to calculate fractional vegetation cover for the Kuiseb extending back to 1984. Overall vegetation cover increased by 33% between 1984 and 2019, with the most highly vegetated reach of the river exhibiting the greatest positive change. This reach corresponds with the terminal alluvial zone, where most flood deposition occurs. The spatial and temporal trends discovered highlight the need for long-term monitoring of ephemeral ecosystems and demonstrate the efficacy of a multi-sensor approach to time series analysis using a UAV platform.

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Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery

Cited by 36 publications

References 49 publications

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Effective ecosystem monitoring requires a multi‐scaled approach

Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery

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