Crop identification by means of seasonal statistics of RapidEye time series

Zillmann, Erik; Weichelt, Horst

doi:10.1109/agro-geoinformatics.2014.6910572

Cited by 3 publications

(6 citation statements)

References 27 publications

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“…The importance of VIs in crop classification have been mentioned in previous works (Bannari, Morin et al 1995, Zillmann andWeichelt 2014). VIs show better sensitivity to green vegetation detection than each spectral band separately.…”

Section: Vegetation Indicesmentioning

confidence: 82%

“…According to (Löw, Schorcht et al 2012, Ianninia, Molijn et al 2013, Zillmann and Weichelt 2014, vegetation indices, multisensor integration and multi temporal images utilization can improve crop classification accuracy. In this work, RE images, in combination with S1 C-band (VV Singlepolarization), L8, SRTM DEM data and several derived vegetation indices employed for classification.…”

mentioning

confidence: 99%

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An Investigation into Maize Lethal Necrosis Severity Mapping in Kenya using RapidEye and Landsat-8

Jozani¹,

Thiel²,

Hahn³

et al. 2018

Preprint

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Introduction Total population of the Kenya at 2014 was 45.5 Million individuals that the share of rural and urban area was 74.4% and 25.6% respectively. Within the crop income category, maize with horticultural crops, covering 14% of total household income in Kenya. At the same time, coffee and tea account for a combined 5.6% of total gross income. In the mid-1970s maize accounted for about 35% of the value of total crop production in Kenya while in the late 1990s, it reduced to 28% of the value of total crop production (Olwande, Sikei et al. 2009). Since 2008 the area under cereal production increased ~ 647,762 ha and the cereal production reached 1,446,328 tonnes. Maize farming is the backbone of food

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Section: Vegetation Indicesmentioning

confidence: 82%

mentioning

confidence: 99%

An Investigation into Maize Lethal Necrosis Severity Mapping in Kenya using RapidEye and Landsat-8

Jozani¹,

Thiel²,

Hahn³

et al. 2018

Preprint

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“…A possible solution to map the complex maize cropping systems accurately in Africa and specifically in Kenya is to use a multi-sensor and multitemporal mapping approach coupled with robust and effective machine learning classification algorithms (Forkuor et al, 2015;Ianninia et al, 2013;Zillmann & Weichelt, 2014). Previous studies have emphasized that spectral vegetation indices (VIs) can enhance the multi-source crop classification results.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Maize Lethal Necrosis (MLN) severity and cropping systems mapping in agro-ecological maize systems in Bomet, Kenya utilizing RapidEye and Landsat-8 Imagery

Jozani

Thiel

Abdel‐Rahman

et al. 2020

Geology, Ecology, and Landscapes

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Spatiotemporal information on crops and cropping systems can provide useful insights into disease outbreak mechanisms in croplands. In September 2011, a severe outbreak of Maize Lethal Necrosis (MLN) disease was reported in Bomet County, Kenya. We aimed to detect severely MLN-infected fields and discriminate mono, inter, and continuous maize cropping systems. We collected in-situ MLN severity observations and acquired multi-date and multisensor data viz., RapidEye (RE), Sentinel-1 (S1), digital elevation model (DEM), and Landsat-8 (L8) imagery. A hierarchical classification approach was used to map the cropping systems and severely MLN-infected fields during the short rainy season (September 2014-February 2015) using the random forest (RF), one-class support vector machine (OCSVM) and biased SVM (BSVM) classifiers. RF showed better performance when a balanced multi-class dataset was available. Both OCSVM and BSVM did not lead to an accurate high severity MLN class separation. Moreover, the BSVM classifier was able to separate the mono and intercropping systems. During the long rainy season (March-August 2015), only maize crop data were available, hence the BSVM as one class classifiers (OCC) was used and maize fields were successfully mapped even with high confusion rate. Furthermore, the distribution of maize intercropping system increased in low rainfall sites, and the continuous cropping system limited to only 31% of total maize cropland.

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“…[1][2][3] Remote sensing data have shown the potential to map crop types. [1][2][3] Remote sensing data have shown the potential to map crop types.…”

Section: Introductionmentioning

confidence: 99%

“…1 Object-based image analysis technologies (OBIA) can be used to identify these parcels. 1 Object-based image analysis technologies (OBIA) can be used to identify these parcels.…”

Section: Introductionmentioning

confidence: 99%

Potential of multitemporal Gaofen-1 panchromatic/multispectral images for crop classification: case study in Xinjiang Uygur Autonomous Region, China

Hao

Wang

Niu

2015

J. Appl. Remote Sens

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Gaofen-1 panchromatic/multispectral (GF-1 PMS) data have both high spatial and temporal resolutions, and this research aims at evaluating the potential of GF-1 PMS data for crop classification. Three PMS images (at days 110, 192, and 274) were acquired in Manas County of Xinjiang. The images were first segmented and all objects were then visually interpreted based on ground reference data. Some indices and textual features were then extracted at the object level. Subsequently, the Jeffries-Matusita (JM) distance was employed to estimate the class separability among all pair-wise comparisons of each time period. Afterward, a random forest algorithm was used to calculate importance scores of all features and classify crop types for every possible image combination. Additionally, to evaluate the influence of feature number on classification accuracy, features were added one by one based on the importance of scores. The result showed that GF-1 PMS images with high-spatial resolution had the potential to identify the boundary of the crop fields. Relatively high JM distance (above 1.5) and classification accuracy (above 90%) indicated that day 192 image contributed the most to the crop identification in the study area. For multi-image combinations, days 110 to 192 combination can achieve high overall accuracy (around 93%) and more images cannot substantially improve the classification performance. As for features, normalized difference vegetation index and near infrared (NIR) band had the highest importance scores and textual features contributed to distinguishing tree from crop land. Finally, classification accuracy increased together with the augmentation of feature number when only a few features were used. After accuracies reached saturation points, however, more features only slightly improved the classification performance.

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Crop identification by means of seasonal statistics of RapidEye time series

Cited by 3 publications

References 27 publications

An Investigation into Maize Lethal Necrosis Severity Mapping in Kenya using RapidEye and Landsat-8

An Investigation into Maize Lethal Necrosis Severity Mapping in Kenya using RapidEye and Landsat-8

Investigation of Maize Lethal Necrosis (MLN) severity and cropping systems mapping in agro-ecological maize systems in Bomet, Kenya utilizing RapidEye and Landsat-8 Imagery

Potential of multitemporal Gaofen-1 panchromatic/multispectral images for crop classification: case study in Xinjiang Uygur Autonomous Region, China

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