Apr 3, 2014 - solving. ⢠crop area estimation. ⢠crop yield forecasting. ⢠environmental impact analysis. [source: FAS USDA]. Crop map. (Kyiv oblast, 2013) ...
Regional Scale Crop Mapping Using Multitemporal Satellite Imagery Nataliia Kussul1, Sergii Skakun1, Andrii Shelestov1,2, Mykola Lavreniuk3, Bogdan Yaylimov1, Olga Kussul4 1 Space
Research Institute NAS and SSA Ukraine 2 National University of Life and Environmental Sciences of Ukraine 3 Taras Shevchenko National University of Kyiv 4 National Technical University of Ukraine “KPI”
ISRSE36 May 11, 2015, Berlin, Germany
Introduction • Agriculture in Ukraine – 1st world largest sunflower producer and exporter (in 2013-2014) – 8th world largest wheat producer (in 2014) [source: FAS USDA]
• Crop mapping – important input within many problems solving • crop area estimation • crop yield forecasting • environmental impact analysis
Crop map (Kyiv oblast, 2013) ISRSE36 2015
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Objectives • To develop crop classification methodology in Ukraine with multi-temporal optical images and multitemporal multi-polarization SAR images using a neural network ensemble
+ Multi-temporal optical images
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Multi-temporal SAR images
Crop maps
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Ground Observations • Along the roads surveys, 2013-2015 № 1 2 3 4 5 6 7 8 9 10 11 12 13 ISRSE36 2015
Class Artificial Winter wheat Winter rapeseed Spring crops Maize Sugar beet Sunflower Soybeans Other cereals Forest Grassland Bare land Water
2013 2014 Polygons Polygons No. % No. % 6 1.6 17 2.8 51 13.2 126 20.4 12 3.1 36 5.8 9 2.3 45 7.3 87 22.5 76 12.3 8 2.1 18 2.9 30 7.8 31 5.0 60 15.5 109 17.6 32 8.3 12 1.9 17 4.4 35 5.7 48 12.4 68 11.0 10 2.6 11 1.8 16 4.1 34 5.5
Total
386
Data randomly divided into training (50%) and testing (50%) sets
2014
Kyiv oblast (area 28,131 km2)
618 4
Restoration of Missing Data Missing
Input X1
X2
X3
Nan
X5
Nan SOM: selection of neuron winner
Missing components are taken from neuron winner
wi1
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wi2
wi3
wi4
wi5
wi6
X1
X2
X3
Nan
X5
Nan
wl1
wl2
wl3
wl4
wl5
wl6
Only valid components are considered for finding a neuron winner
Classification Methodology • •
Ensemble of neural networks (multilayer perceptron - MLP) MLP training: minimized the crossentropy error function N
K
E ( w 1 ,..., w K ) ln p (T | w 1 ,..., w K ) t nk ln y nk
Samples randomly divided into training (50%) and testing (50%) sets
•
Restoration of missing data due to clouds and shadows using a selforganizing Kohonen maps (SOMs)
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Inputs to classifiers
n 1 k 1
t – target output, y – MLP output
•
•
Ensemble – averaging a-posteriori probability of individual networks
– Landsat-8 (TOA or TOC) • bands 2-7
– Proba-V (TOC) • bands 1-4 + NDVI
– Sentinel-1
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1 L l p pi L l 1 e i
k * arg max pke k 1, K
• • • •
bands 1-2 (VV+VH) DN -> backscatter coeff filter: Lee (5 x 5) ortho-rectification 6
Satellite Data. Landsat-8 (2013) Landsat-8 time-series (6 images)
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16.04.13
2.05.13
18.05.13
19.06.13
5.07.13
6.08.13
A true color composition of Landsat-8 bands 4-3-2. SR reflectance values are scaled from 0 to 0.15
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Satellite Data. Landsat-8 (2014) Landsat-8 time-series (6 images) 3.04.14
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6.06.14
8.07.14
A true color composition of Landsat-8 bands 4-3-2. SR reflectance values are scaled from 0 to 0.15
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Satellite Data. Landsat-8 (2014) Landsat-8 time-series (6 images) 10.09.14
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12.10.14
28.10.14
A true color composition of Landsat-8 bands 4-3-2. SR reflectance values are scaled from 0 to 0.15
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Satellite Data. Proba-V (2014) Proba-V time-series (7 images) 05.04.14
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18.04.14
25.05.14
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Satellite Data. Proba-V (2014) Proba-V time-series (7 images (and 15.09.14)) 30.06.14
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1.08.14
6.09.14
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Satellite Data. Sentinel-1 (2015) Sentinel-1 time-series (3 images) 18.03.15
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30.03.15
11.04.15
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Crop Maps 2013 Landsat-8
Probability 1
Comparison to official statistics 0 № 2 3 5 6 7 8
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Class Winter wheat Winter rapeseed Maize Sugar beet Sunflower Soybeans
Official statistics, x 1000, ha
Landsat-8, Relative x 1000, ha error, %
187.3
184.5
-1.5
46.7
59.9
28.3
291.7 15.5 108.2 145.9
342.4 11.2 117.6 168.5
17.4 -27.9 8.7 15.5
OA = 85.3% 13
Crop Maps 2014 Landsat-8
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OA = 88.1%
Proba-V
OA = 90.3%
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Crop Maps 2015 (early-season) Landsat-8
Sentinel-1
Kyiv oblast (area 28,131 km2) ISRSE36 2015
Zhytomyr oblast (area 29,832 km2)
OA = 80.7%
OA = 65.7%
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Comparison experiment Joint Experiment of Crop Assessment and Monitoring (JECAM) under FP7 Grant “Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM” (SIGMA)
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Conclusions • Method of restoring data allows using time series methodology for classification large territories • Ensemble of neural networks allows classify multitemporal SAR images and multi-temporal optical images from different satellites
• The proposed approach is compared to: – existing classifiers in Google Earth Engine – cropland mapping approaches over five JECAM sites
and is applied for classification: – whole territory of Ukraine (1990, 2000, 2010) – Kyiv oblast (2013-2015) and Zhytomyr oblast (2015) ISRSE36 2015
• The obtained map is also comparison to official statistics
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References
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Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine S.Skakun, N.Kussul, A.Y. Shelestov, M.Lavreniuk, O. Kussul IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - 2015. - DOI: 10.1109/JSTARS.2015.2454297.
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Regional scale crop mapping using multi-temporal satellite imagery N. Kussul, S. Skakun, A. Shelestov, M. Lavreniuk, B. Yailymov, O. Kussul International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. – 2015. - P. 45-52.
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Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine A. Kolotii, N. Kussul, A. Shelestov, S. Skakun, B. Yailymov, R. Basarab, M. Lavreniuk, T. Oliinyk, V. Ostapenko International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. – 2015. - P. 39-44. 18
References
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Analysis of Applicability of Neural Networks for Classification of Satellite Data S.V. Skakun, E.V. Nasuro, A.N. Lavrenyuk, and O.M. Kussul J. Autom. Inf. Sci., vol. 39, no. 3, pp. 37-50, 2007.
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Reconstruction of Missing Data in Time-Series of Optical Satellite Images Using Self-Organizing Kohonen Maps S. Skakun, and R. Basarab J. Autom. Inf. Sci., vol. 46, no. 12, pp. 19–26, 2015.
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The use of satellite SAR imagery to crop classification in Ukraine within JECAM project N. Kussul, S. Skakun, A. Shelestov, and O. Kussul in: IGARSS 2014, 13-18 July 2014, pp. 1497–1500, 2014.
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Thank you!
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