Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method

Shen, Kejian; Li, Weifang; Pei, Zhiyuan; Fei, Wang; Sun, Gaoying; Zhang, Xiaoqian; Chen, Xiwei; Ma, Shangjie

doi:10.1016/j.proenv.2015.05.007

Cited by 9 publications

(12 citation statements)

References 2 publications

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“…However, UAVs have a great potential of usage in agriculture as an alternative for the solution of multiple problems, these are presented through the production cycle from the soil preparation to harvest, in principle, the process requires to make a planning of the production system, taking into account geography and area distribution according to needs, parallel to crop development there are environmental states that make the process be susceptible to external agents conditions such as competition with other plants called weeds, [28], [29] which they weaken a crop and make it very responsive to the condition caused by pests or diseases. Remote sensing is part of a solution that uses analyzed variables by using digital photography for decision-making, some situations that can be handled are related to soil preparation [30][31][32], inventory [28], [33], monitoring crop development, distribution of variables in field as diseases, weeds, nutritional deficiencies, cover classification, irrigation and others. Soil preparation is done [30], cadastral boundaries, crop area (size, shape, border and use) are determined, as indicated by several studies [30][31][32].…”

Section: Applications Of Uavs In the Agricultural Sector Through Photmentioning

confidence: 99%

“…Remote sensing is part of a solution that uses analyzed variables by using digital photography for decision-making, some situations that can be handled are related to soil preparation [30][31][32], inventory [28], [33], monitoring crop development, distribution of variables in field as diseases, weeds, nutritional deficiencies, cover classification, irrigation and others. Soil preparation is done [30], cadastral boundaries, crop area (size, shape, border and use) are determined, as indicated by several studies [30][31][32]. Vegetation is monitored [23,30,31,34,35].…”

Section: Applications Of Uavs In the Agricultural Sector Through Photmentioning

confidence: 99%

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Image analysis aplications in precision agriculture

Moreno¹,

Tangarife²,

Díaz³

2018

Vis. Electron.

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Unmanned Aircraft Vehicles (UAVs) are currently used for multiple applications in various fields: forestry, geology, the livestock sector and security. Among the most common applications, it is worth to stand out the image acquisition, irrigation, transport, surveillance and others. The study that one presents treats of the implementations that are realized by means of aerial images acquired with UAVs directed to the farming. Images acquired until recent years had been using satellites, however due to the high costs that are incurred and low accessibility to these technologies, UAVs, have become a tool for greater precision and scope for making decisions in agriculture. Information from databases of international magazines, groups and research centers is taken to determine the current state of implementations in Precision Agriculture (PA). This article describes tasks such as: soil preparation; limits and land areas, vegetation monitoring; classification of vegetation, growth, height, plant health; diseases management, pests and weeds, fertilization and inventory developed from analysis of aerial images acquired with UAVs.

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Section: Applications Of Uavs In the Agricultural Sector Through Photmentioning

confidence: 99%

Section: Applications Of Uavs In the Agricultural Sector Through Photmentioning

confidence: 99%

Image analysis aplications in precision agriculture

Moreno¹,

Tangarife²,

Díaz³

2018

Vis. Electron.

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“…Therefore, crop acreage estimation using satellite and ground data is more precise than data generated based on ground surveys alone (George et al 1980). Currently, researchers are exploring the use of Unmanned Aerial Vehicle (UAV) technology in agricultural monitoring for various applications (Shen et al 2015;Zhang et al 2014). UAV technology has a great potential in crop monitoring and area estimation surveys at the cadastral level with the advantage that, whenever data are needed, one can generate it without much effort and inputs.…”

Section: Difficult To Remember the Past Information On Various Types mentioning

confidence: 99%

Study on Land Use Dynamics: Appropriate Methods for Change Estimation in Social Science Research

et al. 2017

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Background Studies on dynamics of land use change still rely on survey questionnaires to collect the data. This study was undertaken to explore the possibility of an appropriate method for estimating land use land cover (LULC) changes for temporal periods for which there is no direct evidence of change spatially explicit. Purpose The hypothesis of this study is that the data on land use generated by official records, empirical field studies, and analysis of satellite imagery resemble with each other. However, in case of inconsistencies the aim of the present study was to identify a scientific and effective method for obtaining precise data on land use dynamics as part of developing appropriate strategies for a sustainable land use management. Methods The land use information on taluk (block) level was collected from the official records. A questionnaire-based survey was done for collecting household data on land use during the period 2014-2016. Furthermore, satellite data were analysed for land use change estimation. In this study, the conventional methods as well as advanced techniques have been used for estimating crop area at various spatial scales. Per pixel classification techniques were used for generating thematic maps from raster data sets. Data collected from GPS were converted into a shape file for generating a signature file for classification of satellite data sets. While generating thematic maps, the maximum likelihood and parallelepiped classification methods were used. Results The inconsistencies were found between land use data obtained from field studies/official records and the data obtained from analysis of remote-sensing imagery. The satellite data outputs are validated using ground-truth data. Various accuracy assessment metrics such as overall accuracy and kappa coefficient (K hat ) are computed from the validation procedure. The overall accuracy and K hat of the generated thematic maps on LULC are found to be ranging from 79 to 84% and 0.76 to 0.83 for various points of time. However, data obtained from official records and results obtained through questionnaire-based survey could not be cross-checked for an accuracy assessment as the physical conditions of land significantly differ year by year. Conclusion An overall spatial and temporal assessment of land use data, using only a questionnaire-based survey, is not scientifically appropriate. Therefore, this study advocates a trans-disciplinary research in social science based on the convergence of social science and space science and technology.

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“…At present, the application of remote sensing imagery to agricultural work has become a trend in the development of remote sensing technology and agricultural engineering. It uses remote sensing imagery as a source of information and provides support for optimal agricultural production decisions through the processing of remote sensing imagery information [4]. Due to the rapid growth of crops and rapid changes in production demand, it is often necessary to obtain images for a specified period of time.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing image mosaic technology based on SURF algorithm in agriculture

Zhang

Li²,

Yu³

et al. 2018

J Image Video Proc.

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The remote sensing technology of unmanned aerial vehicle (UAV) is a low altitude remote sensing technology. The technology has been widely used in military, agricultural, medical, geographical mapping, and other fields by virtue of the advantages of fast acquisition, high resolution, low cost, and good security. But limited by the flying height of UAV and the focal length of the digital camera, the single image obtained by the UAV is difficult to form the overall cognition of the ground farmland area. In order to further expand the field of view, it is necessary to mosaic multiple single images acquired by UAV into a complete panoramic image of the farmland. In this paper, aiming at the problem of UAV low-altitude remote sensing image splicing, an image mosaic technique based on Speed Up Robust Feature (SURF) is introduced to achieve rapid image splicing. One hundred fifty ground farmland remote sensing images collected by UAV are used as experimental splicing objects, and the image splicing is completed by the global stitching strategy optimized by Levenberg-Marquardt (L-M). Experiments show that the strategy can effectively reduce the influence of cumulative errors and achieve automatic panoramic mosaic of the survey area.

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Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method

Cited by 9 publications

References 2 publications

Image analysis aplications in precision agriculture

Image analysis aplications in precision agriculture

Study on Land Use Dynamics: Appropriate Methods for Change Estimation in Social Science Research

Remote sensing image mosaic technology based on SURF algorithm in agriculture

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