Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes

Tetteh, Gideon Okpoti; Gocht, Alexander; Erasmi, Stefan; Schwieder, Marcel; Conrad, Christopher

doi:10.1109/access.2021.3105903

Cited by 15 publications

(6 citation statements)

References 63 publications

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“…However, we did not observe higher abundances of field breeders in landscapes with smaller fields, even if SWF amount was low. As we excluded fields smaller than 0.5 ha because of the low accuracies of segmentation results (Tetteh et al., 2021; Text S2), mean field size is likely overestimated, especially in fine‐grained landscapes. This might limit the explanatory power of estimated effects for landscapes with small field sizes.…”

Section: Discussionmentioning

confidence: 99%

“…However, several fields of the same crop in direct vicinity might appear as a single field, and field borders that act as important refuges for farmland birds might be missed (Figure S2A). We aimed to approximate realistic management units (Figure S2B) by applying the multiresolution segmentation algorithm (Baatz & Schäpe, 2000) in the eCognition software (Trimble Germany GmbH, 2019) to a time series of monthly Sentinel‐1 and Sentinel‐2 composites (Tetteh et al., 2021). We interpreted segments as individual fields and calculated arithmetic mean field size as a measure of configurational crop heterogeneity based on all segments intersecting a CBBS plot (for details, see Text S2).…”

Section: Methodsmentioning

confidence: 99%

“…Sentinel-1 and Sentinel-2 composites (Tetteh et al, 2021). We interpreted segments as individual fields and calculated arithmetic mean field size as a measure of configurational crop heterogeneity based on all segments intersecting a CBBS plot (for details, see Text S2).…”

Section: Land Cover Data and Landscape Metricsmentioning

confidence: 99%

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Woody semi‐natural habitats modulate the effects of field size and functional crop diversity on farmland birds

Frank,

Hertzog,

Klimek

et al. 2024

Journal of Applied Ecology

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Agricultural intensification has simplified landscape composition and configuration, which has led to biodiversity declines. Increasing landscape‐wide crop heterogeneity can promote farmland biodiversity. However, knowledge is still lacking on how the effects of configurational and compositional crop heterogeneity (i.e. field size and crop diversity) are modulated by the amount of semi‐natural habitats in the landscape, especially across large scales. We tested how mean field size and functional crop diversity affect farmland bird diversity and abundance over three consecutive years, and how these effects are modulated by the amount of small woody features (SWF) in the landscape. We related data from a national bird monitoring scheme to field‐level information from a novel, high‐resolution remote sensing‐based crop type map. Smaller field sizes and higher functional crop diversity were not generally associated with a higher diversity or abundance of farmland birds. Associations varied with species' breeding habitat preferences and were modulated by the amount of SWF. In landscapes with a low SWF amount, species diversity and the abundance of species breeding in field edges or shrubs were negatively associated with increasing field size. However, where the amount of SWF was high, larger field size was associated with higher species diversity and abundance of field and shrub breeders. Diversity increased with higher functional crop diversity, as did the abundance of non‐field breeders in landscapes with a medium to high SWF amount. Field size tended to have a stronger effect on bird diversity and abundance than functional crop diversity. Policy implications: National and EU agricultural policies should adopt a landscape perspective by considering the amount of semi‐natural habitats when designing biodiversity‐enhancing measures that target field size and functional crop diversity. In landscapes with low SWF amount, decreasing field sizes may be particularly effective to promote farmland bird diversity and the abundance of non‐field breeders. In landscapes with a medium to high SWF amount, increasing functional crop diversity is likely more effective than reducing field sizes. Field and shrub breeders may be promoted by maintaining landscapes with large fields, only if these offer a high SWF amount, low agronomic yield potential and low productivity.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Woody semi‐natural habitats modulate the effects of field size and functional crop diversity on farmland birds

Frank,

Hertzog,

Klimek

et al. 2024

Journal of Applied Ecology

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“…Especially for management-related stages, i.e., seeding and harvest, this can lead to a mixture of temporal profiles, where patterns for the corresponding stages could occur twice or blend into each other. A possible solution for future work would be the use of more sophisticated field delineation approaches that can account for management practices (e.g., Tetteh et al, 2021).…”

Section: Limitations and Outlookmentioning

confidence: 99%

A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level

Lobert¹,

Löw²,

Schwieder³

et al. 2023

Preprint

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Information on crop phenology is essential when aiming to better understand the impacts of climate and climate change, management practices, and environmental conditions on agricultural production. Today’s novel optical and radar satellite data with increasing spatial and temporal resolution provide great opportunities to provide such information. However, so far, we largely lack methods that leverage this data to provide detailed information on crop phenology at the field level. We here propose a method based on dense time series from Sentinel-1, Sentinel 2, and Landsat 8 to detect the start of seven phenological stages of winter wheat from seeding to harvest. We built different feature sets from these input data and compared their performance for training a one-dimensional temporal U-Net. The model was evaluated using a comprehensive reference data set from a national phenology network covering 16,000 field observations from 2017 to 2020 for winter wheat in Germany and compared against a baseline set by a Random Forest model. Our results show that optical and radar data are differently well suited for the detection of the different stages due to their unique characteristics in signal processing. The combination of both data types showed the best results with 50.1% to 65.5% of phenological stages being predicted with an absolute error of less than six days. Especially late stages can be predicted well with, e.g., a coefficient of determination (R²) between 0.51 and 0.62 for harvest, while earlier stages like stem elongation remain a challenge (R² between 0.06 and 0.28). Moreover, our results indicate that meteorological data have comparatively low explanatory potential for fine-scale phenological developments of winter wheat.Overall, our results demonstrate the potential of dense satellite image time series from Sentinel and Landsat sensor constellations in combination with the versatility of deep learning models for determining phenological timing.

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“…A comprehensive overview of existing segmentation-evaluation methods as well as their advantages and disadvantages was given by Zhang et al (2008), and more recently by Johnson and Ma (2020) and Jozdani and Chen (2020). A few works have been published dealing with image segmentation based on Sentinel-1 and Sentinel-2 data (e.g., Tetteh et al, 2021;Petrushevskyet al, 2022). Novelli et al (2017) Liu et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Multiresolution Segmentation for Extracting Plastic Greenhouses From Deimos-2 Imagery

Jiménez-Lao

Aguilar

Ladisa

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Accurate greenhouse mapping can support environment monitoring and resource management. In an object-based image analysis (OBIA) approach focused on plastic covered greenhouses (PCG) classification, the segmentation is a crucial step for the goodness of the final results. Multiresolution segmentation (MRS) is one of the most used algorithms in OBIA approaches, being greatly enabled by the advent of the commercial software eCognition. Therefore, in addition to the segmentation algorithm used, it is very important to count on tools to assess the quality of segmentation results from digital images in order to obtain the most similar segments to the real PCG objects. In this work, several factors affecting MRS such as the type of input image and the best MRS parameters (i.e., scale, compactness and shape), have been analysed. In this regard, more than 2800 segmentations focused on PCG land cover were conducted from four pre-processed Deimos-2 very high-resolution (VHR) satellite orthoimages taken in the Southeast of Spain (Almería). Specifically, one multispectral and one pansharpened Deimos-2 orthoimages, both with and without atmospheric correction were tested in this work. The free access AssesSeg command line tool, based on a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to determine the best MRS parameters for all the VHR satellite images. According to both the supervised discrepancy measure ED2 and visual perception, the best segmentation on PCG was obtained over the atmospherically corrected pansharpened Deimos-2 orthoimage, achieving very good results.

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Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes

Cited by 15 publications

References 63 publications

Woody semi‐natural habitats modulate the effects of field size and functional crop diversity on farmland birds

Woody semi‐natural habitats modulate the effects of field size and functional crop diversity on farmland birds

A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level

Multiresolution Segmentation for Extracting Plastic Greenhouses From Deimos-2 Imagery

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