Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle

Varela, Ramón Alberto Díaz; Zarco‐Tejada, Pablo J.; Vincenzo, Angileri; Loudjani, P.

doi:10.1016/j.jenvman.2014.01.006

Cited by 104 publications

(59 citation statements)

References 36 publications

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“…Our study confirmed that the topographic data from UAVs meet the requirements of terraces extraction, which was also found in 2014's research by R.A. Diaz-Varela in Southern Spain [17]. In the agricultural terrace extraction in Spain, DSMs containing vegetation and manmade features above the pure earth surface were applied, and a high overall accuracy of 90% was achieved.…”

Section: Rationality Of the Proposed Methodssupporting

confidence: 87%

“…High-resolution DEMs could be obtained by UAVs with LiDAR [56] or just a single camera; however, LiDAR was necessary in this work because the 1 m DEM generated by camera-based adequately fit the scale of a terrace field in the Loess Plateau. Moreover, DEM generation from camera-based UAVs involves more efficient mechanisms for processing data in comparison with DEM generation from LiDAR [17]. Therefore, the acquisition and adoption of terrain data in this study is reliable and reasonable.…”

Section: Rationality Of the Proposed Methodsmentioning

confidence: 94%

“…The automatic classification method using high-resolution imagery and topographic data is a promising approach for general landscape classification [43][44][45], mountain hazard monitoring [46][47][48], and geomorphology mapping [49,50]; it is also advisable in agricultural terrace extraction [17]. In the Loess Plateau, terraces have diverse vegetation cover and are located in an undulating and broken landform environment.…”

Section: Rationality Of the Proposed Methodsmentioning

confidence: 99%

“…The segmentation scale corresponding to the peak value of the rate of LV change was considered an optimal segmentation scale. Shape and compactness values were determined by a trial-and-error procedure, and visual inspection was used to facilitate these parameter decisions, adjusting the parameters until the segmented objects fit the real target objects well [17].…”

Section: Segmentationmentioning

confidence: 99%

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Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Zhao

Fang

Ding

et al. 2017

IJGI

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Abstract:Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of "vegetation recovery". This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs) and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA). A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN), accumulative curvature slope (AC), and slope of slope (SOS) were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM) analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART) analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and spectrum characteristics, demonstrating huge potential in hilly-gully loess region with similarly complex terrain and diverse vegetation covers.

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Section: Rationality Of the Proposed Methodssupporting

confidence: 87%

Section: Rationality Of the Proposed Methodsmentioning

confidence: 94%

Section: Rationality Of the Proposed Methodsmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Zhao

Fang

Ding

et al. 2017

IJGI

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“…Subsequently, object-based classification combines spectral contextual information for these objects to perform more complex classifications. These techniques have been successfully applied to images obtained by UAVs in agricultural [26,27], aquatic ecosystems [28] and urban [29] areas.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Perea-Moreno

Ureña

Larriva

et al. 2016

Water

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Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management.

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Detection of terracettes in semi‐arid rangelands using Fourier‐based image analysis of very‐high‐resolution satellite imagery

Hellman

Heinse

Karl

et al. 2020

Earth Surf Processes Landf

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Terracettes are repeating step-like microtopographic features roughly following the contours of hillslopes that are often associated with livestock tracks. These common features in many semi-arid rangelands have been shown to alter soil moisture, slope stability, sediment transport, infiltration rates and coincident vegetation patterns. The spatial extent and distribution of terracettes is currently unknown and therefore their landscape-scale hydrological influence is absent in modelling and land management decision making. When viewed in uncalibrated very-high-resolution satellite imagery, terracettes appear as repetitious parallel lines within a specific frequency range. Here we used the two-dimensional discrete Fourier transform to identify terracettes at three test sites in the Inland Pacific Northwest, USA. We created an automated rule-based classification of terracetted sites based on spatial frequency, orientation, slope angle and land-use class. Results show a detection accuracy of 77% based on an optimized spatial frequencies search window between 0.3 and 0.7m À1. Terracette orientation did not contribute significantly to detection accuracy because orientations varied ±50°from digital elevation model-derived aspects. We found terracettes occurred predominantly on north-facing slopes at our test sites, although this estimate may be exaggerated by the timing of image capture. We feel that the method developed in this paper provides a way forward to map terracettes at large scales and enable new insights into the functions of terracettes in the landscape.

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Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle

Cited by 104 publications

References 36 publications

Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Detection of terracettes in semi‐arid rangelands using Fourier‐based image analysis of very‐high‐resolution satellite imagery

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