Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Boryan, Claire G.; Yang, Zhengwei; Willis, Patrick; Di, Liping

doi:10.1016/s2095-3119(16)61396-5

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…These data are annually updated, raster-formatted, and geo-referenced land cover maps [23], whose accuracies for major crops range from 85% to 95% [24]. Due to its high accuracy for predicting the information of crops and identifying the field crops, the CDL data are extensively applied in Earth science research [7,25]. Therefore, this paper utilized the CDL data at 30 m spatial resolution for the year 2010 to generate the crop/non-crop maps for each of the different study sites.…”

Section: Data Descriptionsmentioning

confidence: 99%

“…We are aware that the CDL data are not 100% accurate. However, this dataset is highly accurate and was readily available at finer resolution [7] and therefore, was very appropriate for use in this project.…”

Section: Accuracy Analysis Between Upscaled Maps and Gfsad Mapsmentioning

confidence: 99%

“…As Grekousis et al [6] reported, 23 global and 41 regional land cover products based on remote sensing technologies have been produced and released for use by the Earth science community. For example, the United States Department of Agriculture (USDA), National Agriculture Statistic Service (NASS) annually produces the Cropland Data Layer (CDL) at 30 m resolution for U.S.A. [7]. Other existing maps provide essential information at different spatial resolutions including 1 km, 500 m, 300 m, 250 m, and 30 m [8], and have continued to improve in detail (spatial resolution) over time.…”

Section: Introductionmentioning

confidence: 99%

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Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps

Sun

Congalton

2018

Remote Sensing

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Rescaled maps have been extensively utilized to provide data at the appropriate spatial resolution for use in various Earth science models. However, a simple and easy way to evaluate these rescaled maps has not been developed. We propose a similarity matrix approach using a contingency table to compute three measures: overall similarity (OS), omission error (OE), and commission error (CE) to evaluate the rescaled maps. The Majority Rule Based aggregation (MRB) method was employed to produce the upscaled maps to demonstrate this approach. In addition, previously created, coarser resolution land cover maps from other research projects were also available for comparison. The question of which is better, a map initially produced at coarse resolution or a fine resolution map rescaled to a coarse resolution, has not been quantitatively investigated. To address these issues, we selected study sites at three different extent levels. First, we selected twelve regions covering the continental USA, then we selected nine states (from the whole continental USA), and finally we selected nine Agriculture Statistical Districts (ASDs) (from within the nine selected states) as study sites. Crop/non-crop maps derived from the USDA Crop Data Layer (CDL) at 30 m as base maps were used for the upscaling and existing maps at 250 m and 1 km were utilized for the comparison. The results showed that a similarity matrix can effectively provide the map user with the information needed to assess the rescaling. Additionally, the upscaled maps can provide higher accuracy and better represent landscape pattern compared to the existing coarser maps. Therefore, we strongly recommend that an evaluation of the upscaled map and the existing coarser resolution map using a similarity matrix should be conducted before deciding which dataset to use for the modelling. Overall, extending our understanding on how to perform an evaluation of the rescaled map and investigation of the applicability of the rescaled map compared to the existing land cover map is necessary for users to most effectively use these data in Earth science models.

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Section: Data Descriptionsmentioning

confidence: 99%

Section: Accuracy Analysis Between Upscaled Maps and Gfsad Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps

Sun

Congalton

2018

Remote Sensing

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“…Their work using crop frequencies were done to aid in developing frame stratifications in order to improve crop survey estimates (Boryan et al, 2017). A similar approach was followed using the annual crop type classifications for South Africa.…”

Section: Crop Frequency and Rotationmentioning

confidence: 99%

Tracking Farm Management Practices With Remote Sensing

Stals

Ferreira

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Earth observation (EO) data is effective in monitoring agricultural cropping activity over large areas. An example of such an application is the GeoTerraImage crop type classification for the South African Crop Estimates Committee (CEC). The satellite based classification of crop types in South Africa provides a large scale, spatial and historical record of agricultural practices in the main crop growing areas. The results from these classifications provides data for the analysis of trends over time, in order to extract valuable information that can aid decision making in the agricultural sector. Crop cultivation practices change over time as farmers adapt to demand, exchange rate and new technology. Through the use of remote sensing, grain crop types have been identified at field level since 2008, providing a historical data set of cropping activity for the three most important grain producing provinces of Mpumalanga, Freestate and North West province in South Africa. This historical information allows the analysis of farm management practices to identify changes and trends in crop rotation and irrigation practices. Analysis of crop type classification over time highlighted practices such as: frequency of cultivation of the same crop on a field, intensified cultivation on centre pivot irrigated fields with double cropping of a winter grain followed by a summer grain in the same year and increasing cultivation of certain types of crops over time such as soyabeans. All these practices can be analysed in a quantitative spatial and temporal manner through the use of the remote sensing based crop type classifications.

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“…Several countries in Europe have also used segments with regular geometric shapes for estimating crop area statistics through the MARS and LUCAS projects (Fuentes andGallego 1994, Gallego 2007). Much of this work has been facilitated through advancement in remote sensing and geographic information system (GIS) techniques and has led to savings in cost and time (Khan et al 2010, Atzberger 2013, Cotter et al 2010, Strand 2013, Boryan et al 2017, Carfagna and Gallego 2005.…”

Section: Introductionmentioning

confidence: 99%

Improving Paddy Rice Statistics Using Area Sampling Frame Technique

et al. 2018

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The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area, or by using the term "country" in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area.

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Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Cited by 14 publications

References 11 publications

Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps

Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps

Tracking Farm Management Practices With Remote Sensing

Improving Paddy Rice Statistics Using Area Sampling Frame Technique

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