Satellite image classification of vegetation and surface water for assessment of flood damage to agricultural crops

Transcription

1 Satellite image classification of vegetation and surface water for assessment of flood damage to agricultural crops Lisa Colson 2, Mark Lindeman 1, Dath Mita 1, Tatiana Nawrocki 2, Paulette Sandene 1, Christianna Townsend 2 1. International Production Assessment Division, Office of Global Analysis, Foreign Agricultural Service, United States Department of Agriculture 2. Inuteq, LLC

2 Satellite Monitoring of Global Agriculture Each month, the U.S. Department of Agriculture (USDA) publishes crop supply and demand estimates for the Nation and the world. Satellite imagery analysis performed by the International Production Assessment Division (IPAD) of Foreign Agricultural Service (FAS) contributes to the accuracy and reliability of forecasts. Floods and other natural disasters can abruptly change regional or global production outlook. Satellite imagery analysis assists in obtaining near-real time production damage estimates in remote regions before other local estimates become available.

3 Flood impacts on crops depend on crop calendar, flood duration and size Ancient Egypt would not have existed without preseasonal Nile River floods. Floods can destroy plants if occurred during growing seasons

4 How to quantify flood damage to agriculture remotely? Objective: Identify and overlay outlines of flood water and active agricultural crops Method: Satellite imagery time series analysis and classification Case studies: 2013 floods in Russia, China, Pakistan

5 Flooding on the Nenjiang and Songhua Rivers in Heilongjiang, China 2013 flood Non-flood year Multiple dates of MODIS imagery were classified to estimate the flood extent without errors from clouds and cloud shadows Qiqihar Qiqihar Source: USDA GLAM Project, MODIS Terra satellite (250m) bands taken on August 13th.

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7 Estimated Area Flooded (hectares) Field Crops 1.2 million ha Rice Crops 60,000 ha (Only 4% of the 2011 sown area)

8 Flood in Amur oblast, Russia July 16, 2013 Flood August 21, 2013 Before flood Data source: MODIS Terra, bands

9 Preliminary estimate GIS overlay: 2013 flood and historic crop map, Amur oblast

10 Verify vegetation classification results 2011crop statistics vs classified image results 2013 flood water, classified crop area and % of flooded crops 2013

11 Distinguish flood and irrigation waters When floods occurred concurrently with irrigation, an overlay of pre-flood vegetation and water areas did not correctly quantify the flood damaged crop area. Irrigated fields, which were initially classified as water, turned to productive crops later in the season.

12 Torrential rains coincided with irrigation season, early August, 2013, Sind, Pakistan Irrigation 2013 Flood water passed through irrigation systems, partially inundated fields, then spilled to drainage network. Afghanistan Baluchistan Punjab Sindh India

13 Before flood, July 10 After flood, August 27 Water on the fields

14 Landsat time series Multispectral LS images compounded as 30+ band image stack? Image time series allowed to see water and vegetation almost like in motion.

15 Normalized Difference Vegetation Index (NDVI) (NIR - Red) (NIR + Red) 50% % 0.14 ( )/( ) = 0.14 Synthetic signatures of a temporal NDVI image stack simulate crop performance during vegetation cycle and surface water stages in each pixel. ( )/( ) = 0.72

16 NDVI time series (from Landsat) NDVI time series show water and vegetation almost like in motion. Multi-temporal image stack classification located water, healthy and flood damaged crops.

17 Time series classification goals Well performing crops Flooded crops Non-agricultural vegetation Irrigated land Flooded non-cropland Permanent surface water bodies

18 Crop locations and crop cycle Typical NDVI cycles in predominantly rice cultivation regions Typical NDVI cycles in predominantly cotton cultivation regions Rice irrigation and transplanting, July - August Cotton planting, May - June

19 Image classification: spectral bands vs. temporal layers Spectral, 8/27/2013 Single day vegetation and other land cover types based on spectral profiles Seasonal vegetation performance based on NDVI time series Temporal July August September October

20 Interpret class metrics and locate crops Right Indus bank: dominant rice Left Indus bank: dominant cotton

21 NDVI time series classification results: Mapping vegetation performance Similar to rice Similar to cotton Afghanistan Baluchistan Punjab Sindh India Poor / stressed Vegetation performance types during the crop cycle Water and bare land classes have been removed

22 Results: crop performance map and calculating areas by performance categories Class Pixels Percent Sq. meter Hectare 1 810, % 729,693,000 72, , % 558,584,100 55, , % 409,094,100 40, , % 348,489,000 34, , % 314,411,400 31, , % 603,616,500 60, , % 699,317,100 69, ,006, % 906,156,900 90, , % 606,809,700 60, , % 392,542,200 39,254 Other 3,669, % 3,302,368, ,237 Quantify and compare areas of well performing and damaged crops.

23 NDWI Time Series Normalized Difference vegetation Water content Index NDWI (NIR SWIR) (NIR + SWIR)

24 NDWI time series classification detected permanent and flood water July 10 August 27 Sept. 12 Sept. 28

25 Image classification detected: Well performing crops Flooded crops Other (native) vegetation Irrigated land Flooded non-cropland Permanent surface water Disclaimer: Interpretation of the results was based on assumptions about the crop cycle. Ground truth data were not available. The contents contained in this presentation do not necessarily reflect the work and/or opinions of the USDA and its authorities.