E – Farm Markt Plac
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Received: 22 November 2016 / Revised: 14 February 2017 / Accepted: 16 February 2017 / Published: 24 February 2017
The concept of improving observation of ALS using support from relevant disciplines. Based on agricultural remote sensing, new sensing techniques from citizen science such as crowdsourcing and human sensing are applied to expand the potential of traditional household surveys in acquiring the human-land integrated information. Abbreviations in the figure: VGI: Volunteered Geographic Information; SAGI: Satellite, Aerial, and Ground Integrated agricultural remote sensing. The details of the concepts are elaborated in this section below. Some elements of the figure are adopted from internet.
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The overview of the eFarm system. The diagram presents a closed loop of information sensing: remoted-sensed images provide a basemap of land parcel information while the observed land use information and manager’s characteristics are added to the land parcels thought eFarm. The illustrated basemap was adopted from Google Map displaying an agricultural area in Qianjiang City, Central China. See a color-blinded figure in the Supplementary Materials.
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Visualization of a basemap in the eFarm app based on a timely acquired UAV image.
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Creating a land parcel polygon based on a manual-drawing process.
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Visualization of land activities on a land parcel.
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Solutions from eFarm for improving the observation of ALS.
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Application of the CroPaDy model based on the human–land integrated information.
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Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies.View Full-Text
Keywords: smartphone; human sensing; social sensing; crowdsourcing; agriculture; land use; citizen science smartphone; human sensing; social sensing; crowdsourcing; agriculture; land use; citizen science
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Figure 1
The concept of improving observation of ALS using support from relevant disciplines. Based on agricultural remote sensing, new sensing techniques from citizen science such as crowdsourcing and human sensing are applied to expand the potential of traditional household surveys in acquiring the human-land integrated information. Abbreviations in the figure: VGI: Volunteered Geographic Information; SAGI: Satellite, Aerial, and Ground Integrated agricultural remote sensing. The details of the concepts are elaborated in this section below. Some elements of the figure are adopted from internet.
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The overview of the eFarm system. The diagram presents a closed loop of information sensing: remoted-sensed images provide a basemap of land parcel information while the observed land use information and manager’s characteristics are added to the land parcels thought eFarm. The illustrated basemap was adopted from Google Map displaying an agricultural area in Qianjiang City, Central China. See a color-blinded figure in the Supplementary Materials.
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Visualization of a basemap in the eFarm app based on a timely acquired UAV image.
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Creating a land parcel polygon based on a manual-drawing process.
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Visualization of land activities on a land parcel.
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Solutions from eFarm for improving the observation of ALS.
Xem thêm: Giáo Án Kể Chuyện Sáng Tạo Chủ Đề Thực Vật, Giáo Án Dạy Trẻ Kể Chuyện Sáng Tạo
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Application of the CroPaDy model based on the human–land integrated information.
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