Crop Field Boundary Delineation using Historical Crop Rotation Pattern

Rahman, Md. Shahinoor; Di, Liping; Yu, Zhiqi; Yu, Genong; Tang, Jinshan; Lin, Li; Zhang, Chen; Gaigalas, Juozas

doi:10.1109/agro-geoinformatics.2019.8820240

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…Moreover, these thresholds may vary across many factors such as geographic location, crop types, and seasonality. Crop field boundaries derived from remote sensing data and products can also be helpful for field-level crop mapping and damage assessment [116]. Often yield data are available at field level instead of pixel level.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Rahman

2020

Agriculture

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This article reviews case studies which have used remote sensing data for different aspects of flood crop loss assessment. The review systematically finds a total of 62 empirical case studies from the past three decades. The number of case studies has recently been increased because of increased availability of remote sensing data. In the past, flood crop loss assessment was very generalized and time-intensive because of the dependency on the survey-based data collection. Remote sensing data availability makes rapid flood loss assessment possible. This study groups flood crop loss assessment approaches into three broad categories: flood-intensity-based approach, crop-condition-based approach, and a hybrid approach of the two. Flood crop damage assessment is more precise when both flood information and crop condition are incorporated in damage assessment models. This review discusses the strengths and weaknesses of different loss assessment approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat are the dominant sources of optical remote sensing data for flood crop loss assessment. Remote-sensing-based vegetation indices (VIs) have significantly been utilized for crop damage assessments in recent years. Many case studies also relied on microwave remote sensing data, because of the inability of optical remote sensing to see through clouds. Recent free-of-charge availability of synthetic-aperture radar (SAR) data from Sentinel-1 will advance flood crop damage assessment. Data for the validation of loss assessment models are scarce. Recent advancements of data archiving and distribution through web technologies will be helpful for loss assessment and validation.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Rahman

2020

Agriculture

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“…These systems may offer valuable and accurate datasets, but they often rely on historic administrative maps, manual field delineations based on satellite or airborne imagery, and in-situ mappings via Global Positioning Systems (GPS) tracking [4]. However, manual approaches are time-consuming, costly, and often subjective, and therefore of limited use in regions with frequently changing field structures or cropping patterns [4,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The accurate mapping of the location, size, and shape of individual fields is important for the implementation of precision agriculture, crop yield estimations, resource planning, and environmental impact analysis [4,9,11]. It enables insights into the degree of mechanization, agricultural practices, and production efficiency in a region, and allows better implementation of administrative policies such as subsidy payments and insurance [4,12].…”

Section: Introductionmentioning

confidence: 99%

“…Examples include the use of edge detection and morphological decomposition to segment fields in Landsat imagery or training random forest classifiers on image features such as mean reflectance values [21,22]. Rahman et al followed a different approach by attempting to delineate fields using statistical data on crop rotation patterns [9]. Other studies intended to extract field boundaries.…”

Section: Introductionmentioning

confidence: 99%

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Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

Wagner

Oppelt

2020

Remote Sensing

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Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage.

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“…Field mapping is a topic of increasing relevance. Knowledge of field sizes, shapes, and distributions is valuable for a wide variety of topics, ranging from precision agriculture to biodiversity mapping, agricultural policy, yield estimation, and cropland classification [1][2][3]. Today, monitoring is mostly based on field campaigns, administrative maps, and manual airborne or satellite imagery interpretation [1].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction

Wagner

Oppelt

2020

Remote Sensing

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Field mapping and information on agricultural landscapes is of increasing importance for many applications. Monitoring schemes and national cadasters provide a rich source of information but their maintenance and regular updating is costly and labor-intensive. Automatized mapping of fields based on remote sensing imagery may aid in this task and allow for a faster and more regular observation. Although remote sensing has seen extensive use in agricultural research topics, such as plant health monitoring, crop type classification, yield prediction, and irrigation, field delineation and extraction has seen comparatively little research interest. In this study, we present a field boundary detection technique based on deep learning and a variety of image features, and combine it with the graph-based growing contours (GGC) method to extract agricultural fields in a study area in northern Germany. The boundary detection step only requires red, green, and blue (RGB) data and is therefore largely independent of the sensor used. We compare different image features based on color and luminosity information and evaluate their usefulness for the task of field boundary detection. A model based on texture metrics, gradient information, Hessian matrix eigenvalues, and local statistics showed good results with accuracies up to 88.2%, an area under the ROC curve (AUC) of up to 0.94, and F1 score of up to 0.88. The exclusive use of these universal image features may also facilitate transferability to other regions. We further present modifications to the GGC method intended to aid in upscaling of the method through process acceleration with a minimal effect on results. We combined the boundary detection results with the GGC method for field polygon extraction. Results were promising, with the new GGC version performing similarly or better than the original version while experiencing an acceleration of 1.3× to 2.3× on different subsets and input complexities. Further research may explore other applications of the GGC method outside agricultural remote sensing and field extraction.

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Crop Field Boundary Delineation using Historical Crop Rotation Pattern

Cited by 11 publications

References 9 publications

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction

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