Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform

Wang, Haozhou; Han, Dong; Mu, Yun; Jiang, Lina; Yao, Xueling; Bai, Yongfei; Lu, Qi; Wang, Feng

doi:10.1016/j.agrformet.2019.107665

Cited by 27 publications

(24 citation statements)

References 46 publications

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“…We used five widely used indices, producer's accuracy, user's accuracy, mean accuracy of different classes, kappa coefficient, and overall accuracy (OA), to evaluate the classification accuracy (Talebi et al, 2014;Dobrinić et al, 2021;Wang et al, 2019;Man et al, 2020). The producer's accuracy is the ratio of the number of correctly classified objects to validation objects for a class, and the user's accuracy is the ratio of the number of correctly classified objects to classified objects for a class (Talebi et al, 2014;Dobrinić et al, 2021).…”

Section: Algorithm Training and Validationmentioning

confidence: 99%

“…The mean accuracy is the average of the producer's accuracy and user's accuracy. The kappa coefficient uses information about the entire error matrix to evaluate the classification accuracy and is calculated as (Wang et al, 2019;Man et al, 2020)…”

Section: Algorithm Training and Validationmentioning

confidence: 99%

“…For example, Gašparović and Dobrinić (2021) used satellite synthetic aperture radar images to identify water, bare soil, forest, and low vegetation, and Furberg et al (2020) used satellite images to analyze the changes in urban grassland and forest. However, the acquisition of satellite images is strongly affected by the atmospheric cloud conditions (Gašparović and Dobrinić, 2021;Wang et al, 2019), and the accuracy and precision are limited by the spatial resolution (Bartesaghi-Koc et al, 2020;Wang et al, 2019), especially in urban areas with complex features (Furberg et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…However, the costs of airborne vehicles are prohibitive and they require logistics management (Bartesaghi-Koc et al, 2020). Nowadays, the cost of UAVs is decreasing (Wang et al, 2019) and UAVs can carry a variety of sensors (e.g., multispectral cameras, LiDAR, and thermal infrared cameras) that can obtain targeted high-resolution data on a centimeter scale (Jiang et al, 2021). The amount of research on GI using UAV data has been increasing; for example, urban GI thermal effects (Khalaim et al, 2021), GI vegetation health (Dimitrov et al, 2018), and the classification of plant species (Fan and Lu, 2021;Jiang et al, 2021;Miura et al, 2021) have been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Jia

Cui

Liu

2022

Front. Environ. Sci.

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Green infrastructure (GI), such as green roofs, is now widely used in sustainable urban development. An accurate mapping of GI is important to provide surface parameterization for model development. However, the accuracy and precision of mapping GI is still a challenge in identifying GI at the small catchment scale. We proposed a framework for blue-green-gray infrastructure classification using machine learning algorithms and unmanned aerial vehicle (UAV) images that contained digital surface model (DSM) information. We used the campus of the Southern University of Science and Technology in Shenzhen, China, as a study case for our classification method. The UAV was a DJI Phantom 4 Multispectral, which measures the blue, green, red, red-edge, and near-infrared bands and DSM information. Six machine learning algorithms, i.e., fuzzy classifier, k-nearest neighbor classifier, Bayes classifier, classification and regression tree, support vector machine (SVM), and random forest (RF), were used to classify blue (including water), green (including green roofs, grass, trees (shrubs), bare land), and gray (including buildings, roads) infrastructure. The highest kappa coefficient was observed for RF and the lowest was observed for SVM, with coefficients of 0.807 and 0.381, respectively. We optimized the sampling method based on a chessboard grid and got the optimal sampling interval of 11.6 m to increase the classification efficiency. We also analyzed the effects of weather conditions, seasons, and different image layers, and found that images in overcast days or winter days could improve the classification accuracy. In particular, the DSM layer was crucial for distinguishing green roofs and grass, and buildings and roads. Our study demonstrates the feasibility of using UAV images in urban blue-green-gray infrastructure classification, and our infrastructure classification framework based on machine learning algorithms is effective. Our results could provide the basis for the future urban stormwater management model development and aid sustainable urban planning.

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Section: Algorithm Training and Validationmentioning

confidence: 99%

Section: Algorithm Training and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Jia

Cui

Liu

2022

Front. Environ. Sci.

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“…In this study, the canopy of trees, shrubs, and herbaceous vegetation and bare soil was accurately classified using the object-based SVM classification method based on a UAV RGB image, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively, which indicates that super high-resolution UAV images can replace time-consuming and labor-intensive manual field surveys and obtain highly effectively canopy structural parameters in sparsely vegetated area. Other studies have also shown that high-resolution UAV inversion of vegetation canopy coverage has high accuracy and has the potential to replace field surveys [6,57].…”

Section: Potential Of Using High-resolution Remote Sensing Images For Agb Estimation Of Sparse Mixed Forestmentioning

confidence: 99%

Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

et al. 2021

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Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t∙ha−1 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t∙ha−1 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t∙ha−1 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t∙ha−1 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas.

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Improved understanding of vegetation dynamics and wetland ecohydrology via monthly UAV‐based classification

Wu,

Tetzlaff,

Daempfling

et al. 2023

Hydrological Processes

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Vegetation classification is an essential prerequisite for understanding vegetation‐water relations at a range of spatial scales. However, in site‐specific applications, such classifications were mostly based on a single Unmanned Aerial Vehicle (UAV) flight, which can be challenging in grasslands and/or herbaceous‐dominated systems, as those communities are small in size and highly mixed. Here, we conducted monthly UAV flights for two years in a riparian wetland in Germany, with acquired imagery used for vegetation classification on a monthly basis under different strategies (with or without auxiliary information from other flights) to increase understanding in ecohydrology. The results show that multi‐flight‐based classification outperformed single‐flight‐based classification due to the higher classification accuracy. Moreover, improved sensitivity of temporal changes in community distribution highlights the benefits of multi‐flight‐based classification ‐ providing a more comprehensive picture of community evolution. From reference to the monthly community distribution, we argue that a combination of two or three flights in early‐ and late‐summer is enough to achieve comparable results to monthly flights, while mid‐summer would be a better timing in case only one flight is scheduled. With such detailed vegetation mapping, we further interpreted the complex spatio‐temporal heterogeneity in NDVI and explored the dominant areas and developmental progress of each community. Impacts from management (mowing events) were also evaluated based on the different responses between communities in two years. Finally, we explored how such vegetation mapping could help understand landscape ecohydrology, and found that the spatio‐temporal distribution of minimal soil moisture was related to NDVI peaks of local community, while grass distribution was explained by both topography and low moisture conditions. Such bi‐directional relationships proved that apart from contributing to an evidence base for wetland management, multi‐flight UAV vegetation mapping could also provide fundamental insights into the ecohydrology of wetlands.

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Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform

Cited by 27 publications

References 46 publications

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

Improved understanding of vegetation dynamics and wetland ecohydrology via monthly UAV‐based classification

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