A Tool to Explore Spectral, Spatial and Temporal Features of Smallholder Crops : powerpoint

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Abstract

We present a crop characteristics database plus web-based open data exploration tool as one of the results produced by the STARS project (www.stars-project.org). STARS aims to address the information scarcity around smallholder farming in Africa and Asia through the use of high-resolution satellite images. We conducted a number of studies in sites in W and E Africa as well as S Asia, which brought together fieldwork-derived and image-derived characteristics of farm fields into a central database, which we call the Crop Spectrotemporal Signature Library (CSSL). We present its structure and contents.

The CSSL does not hold image data, but it does hold statistical characterizations derived from analyzing both multispectral and panchromatic images through a fully automated workflow. Consequently, we obtained a decent number of vegetation indices and their in-field variability, a number of other spectral characteristics, as well as a number of GLCM-based textural characteristics (different lags, different angles). We continue to enrich that list with other image-based analytics.
Thus, on the imaging side, our analysis produced various tens of characteristics of farm fields that are either spectral or textural in nature, while fieldwork produced a number of in situ agronomic measurements, characterizing crop growth and field maintenance. All such data was semi-synchronously collected throughout the crop season at regular two-week intervals. Our philosophy is that a collection of this nature can support studies in crop identification, farm field delineation, farm practice detection and other crop-related phenomena in smallholder contexts.

We thus also present an online exploration tool that allows inspection of characteristics and their correspondences, and invite the larger scientific community to start using this resource, which accommodate time series comparisons, for instance, between different vegetation indices and textural or in situ measurements. We invite the scientific audience to use the tool, and those conducting image-based projects on smallholder farming, to contribute to its baseline through collaboration with us to enrich it with more crops, more years, and a wider geographic coverage.
Original languageEnglish
Pagess1-s22
Publication statusPublished - 27 Sep 2017
EventEarth Observation Open Science 2017 Conference

- Frascati, Italy
Duration: 25 Sep 201728 Sep 2017
https://livestream.com/ESA/OpenScience2017

Conference

ConferenceEarth Observation Open Science 2017 Conference

CountryItaly
CityFrascati
Period25/09/1728/09/17
Internet address

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smallholder
crop
farm
vegetation index
fieldwork
panchromatic image
multispectral image
in situ measurement
time series
resource

Cite this

de By, R. A., Zurita-Milla, R., Pasha Zadeh M., P., & Calisto, L. F. (2017). A Tool to Explore Spectral, Spatial and Temporal Features of Smallholder Crops : powerpoint. s1-s22. Earth Observation Open Science 2017 Conference

, Frascati, Italy.
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title = "A Tool to Explore Spectral, Spatial and Temporal Features of Smallholder Crops : powerpoint",
abstract = "We present a crop characteristics database plus web-based open data exploration tool as one of the results produced by the STARS project (www.stars-project.org). STARS aims to address the information scarcity around smallholder farming in Africa and Asia through the use of high-resolution satellite images. We conducted a number of studies in sites in W and E Africa as well as S Asia, which brought together fieldwork-derived and image-derived characteristics of farm fields into a central database, which we call the Crop Spectrotemporal Signature Library (CSSL). We present its structure and contents.The CSSL does not hold image data, but it does hold statistical characterizations derived from analyzing both multispectral and panchromatic images through a fully automated workflow. Consequently, we obtained a decent number of vegetation indices and their in-field variability, a number of other spectral characteristics, as well as a number of GLCM-based textural characteristics (different lags, different angles). We continue to enrich that list with other image-based analytics.Thus, on the imaging side, our analysis produced various tens of characteristics of farm fields that are either spectral or textural in nature, while fieldwork produced a number of in situ agronomic measurements, characterizing crop growth and field maintenance. All such data was semi-synchronously collected throughout the crop season at regular two-week intervals. Our philosophy is that a collection of this nature can support studies in crop identification, farm field delineation, farm practice detection and other crop-related phenomena in smallholder contexts.We thus also present an online exploration tool that allows inspection of characteristics and their correspondences, and invite the larger scientific community to start using this resource, which accommodate time series comparisons, for instance, between different vegetation indices and textural or in situ measurements. We invite the scientific audience to use the tool, and those conducting image-based projects on smallholder farming, to contribute to its baseline through collaboration with us to enrich it with more crops, more years, and a wider geographic coverage.",
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de By, RA, Zurita-Milla, R, Pasha Zadeh M., P & Calisto, LF 2017, 'A Tool to Explore Spectral, Spatial and Temporal Features of Smallholder Crops : powerpoint' Earth Observation Open Science 2017 Conference

, Frascati, Italy, 25/09/17 - 28/09/17, pp. s1-s22.

A Tool to Explore Spectral, Spatial and Temporal Features of Smallholder Crops : powerpoint. / de By, R.A.; Zurita-Milla, R.; Pasha Zadeh M., P.; Calisto, L.F.

2017. s1-s22 Earth Observation Open Science 2017 Conference

, Frascati, Italy.

Research output: Contribution to conferenceOtherOther research output

TY - CONF

T1 - A Tool to Explore Spectral, Spatial and Temporal Features of Smallholder Crops : powerpoint

AU - de By, R.A.

AU - Zurita-Milla, R.

AU - Pasha Zadeh M., P.

AU - Calisto, L.F.

PY - 2017/9/27

Y1 - 2017/9/27

N2 - We present a crop characteristics database plus web-based open data exploration tool as one of the results produced by the STARS project (www.stars-project.org). STARS aims to address the information scarcity around smallholder farming in Africa and Asia through the use of high-resolution satellite images. We conducted a number of studies in sites in W and E Africa as well as S Asia, which brought together fieldwork-derived and image-derived characteristics of farm fields into a central database, which we call the Crop Spectrotemporal Signature Library (CSSL). We present its structure and contents.The CSSL does not hold image data, but it does hold statistical characterizations derived from analyzing both multispectral and panchromatic images through a fully automated workflow. Consequently, we obtained a decent number of vegetation indices and their in-field variability, a number of other spectral characteristics, as well as a number of GLCM-based textural characteristics (different lags, different angles). We continue to enrich that list with other image-based analytics.Thus, on the imaging side, our analysis produced various tens of characteristics of farm fields that are either spectral or textural in nature, while fieldwork produced a number of in situ agronomic measurements, characterizing crop growth and field maintenance. All such data was semi-synchronously collected throughout the crop season at regular two-week intervals. Our philosophy is that a collection of this nature can support studies in crop identification, farm field delineation, farm practice detection and other crop-related phenomena in smallholder contexts.We thus also present an online exploration tool that allows inspection of characteristics and their correspondences, and invite the larger scientific community to start using this resource, which accommodate time series comparisons, for instance, between different vegetation indices and textural or in situ measurements. We invite the scientific audience to use the tool, and those conducting image-based projects on smallholder farming, to contribute to its baseline through collaboration with us to enrich it with more crops, more years, and a wider geographic coverage.

AB - We present a crop characteristics database plus web-based open data exploration tool as one of the results produced by the STARS project (www.stars-project.org). STARS aims to address the information scarcity around smallholder farming in Africa and Asia through the use of high-resolution satellite images. We conducted a number of studies in sites in W and E Africa as well as S Asia, which brought together fieldwork-derived and image-derived characteristics of farm fields into a central database, which we call the Crop Spectrotemporal Signature Library (CSSL). We present its structure and contents.The CSSL does not hold image data, but it does hold statistical characterizations derived from analyzing both multispectral and panchromatic images through a fully automated workflow. Consequently, we obtained a decent number of vegetation indices and their in-field variability, a number of other spectral characteristics, as well as a number of GLCM-based textural characteristics (different lags, different angles). We continue to enrich that list with other image-based analytics.Thus, on the imaging side, our analysis produced various tens of characteristics of farm fields that are either spectral or textural in nature, while fieldwork produced a number of in situ agronomic measurements, characterizing crop growth and field maintenance. All such data was semi-synchronously collected throughout the crop season at regular two-week intervals. Our philosophy is that a collection of this nature can support studies in crop identification, farm field delineation, farm practice detection and other crop-related phenomena in smallholder contexts.We thus also present an online exploration tool that allows inspection of characteristics and their correspondences, and invite the larger scientific community to start using this resource, which accommodate time series comparisons, for instance, between different vegetation indices and textural or in situ measurements. We invite the scientific audience to use the tool, and those conducting image-based projects on smallholder farming, to contribute to its baseline through collaboration with us to enrich it with more crops, more years, and a wider geographic coverage.

M3 - Other

SP - s1-s22

ER -