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Lin Yan & David Roy
Geospatial Sciences Center of Excellence
South Dakota State University
February 13-15th 2018
Advances in Emerging Technologies and Methods in Earth
Observation for Agricultural Monitoring Workshop
Beltsville, Maryland
Field size estimation, past and future opportunities
Why Study Field Sizes ?The size of agricultural fields is:
• a fundamental description of rural
landscapes
• of biophysical, ecological and
economic importance
• indicative of the degree of agricultural
capital investment, mechanization,
and labor intensity
• through time is indicative of
agricultural changes
Farmers ripping up fence line and clearing tree lot to increase field size
(5th September 2015, one mile north of Brookings, SD)
Photograph by David Roy
Importantly Landsat has both suitable resolution & length of record
to capture field size changesin many parts of the world
Landsat 30m observations since 1982
What would this histogram
look like for:
• all CONUS ?
• every decade ?
Conterminous United States (CONUS) Motivation
Histogram from 109,000 agricultural
fields (> 1 acre) digitized from Landsat
data acquired in 1977 – 1980 over
parts of the Mid-West and Canada
M. C. Ferguson and C. D. Badhwar, 1986. Field size distributions
for selected agricultural crops in the United States and Canada,
Remote Sensing of Environment, 19 (1), 25-45.
Field Extraction Methodology
Computational methodology designed to be
• fully automated
• no training data
• no human interactions
• needed for CONUS 30m Landsat application
Example WELD Weekly Products
Week 27: July 8 - 14 2008
10 Years of Alaska and CONUS
Landsat 7 ETM+ 30m products
http://weld.cr.usgs.gov
Use Landsat time series
gridded calibrated 30m Landsat reflectance
weekly, monthly, seasonal and annual products
Example WELD Weekly Products
Week 28: July 8 - 14 2008
10 Years of Alaska and CONUS
Landsat 7 ETM+ 30m products
http://weld.cr.usgs.gov
Use Landsat time series
Example WELD Weekly Products
Week 29: July 15 - 21 2008
10 Years of Alaska and CONUS
Landsat 7 ETM+ 30m products
http://weld.cr.usgs.gov
Use Landsat time series
USDA National Agricultural Statistics ServiceCropland Data Layer (CDL)
based on supervised classification of many satellite data, lots of training data, and interactive refinements
Pixel-based products unable to extract separated and coherent fields
Big-picture algorithm processing flow
52 weeks of WELD 2010
Landsat 5 & 7 weekly images
USDA NASS 2010
Cropland Data Layer (CDL)
edge intensity map
Binary crop mask
Extracted fields
Computer vision approach
Detailed processing flow – computer vision approach
Yan, L. and Roy, D.P. (2016). Conterminous United States crop field size quantification from
multi-temporal Landsat data, Remote Sensing of Environment, 172, 67-86
Field Extraction Results
Texas
5000 x 5000 30m pixels
WELD Tile h13v12 Northern High Plains, Texas
Automatically extracted field objects
Texas
5000 x 5000 30m pixels
1200 x 900 30m pixels
1200 x 900 30m pixels
California
5000 x 5000 30m pixels
WELD Tile h05v13Imperial Valley, CA
Automatically extracted field objects
California
5000 x 5000 30m pixels
2200 x 1650 30m pixels
1400 x 1050 30m pixels
1400 x 1050 30m pixels
Field size
= (30 × 30) 𝑚2σ𝑛𝑢𝑚𝑏𝑒𝑟 𝑝𝑖𝑥𝑒𝑙𝑠
2010 CONUS crop field size map
km2
4,182,777 crop fields
extracted
(mean field size in 7.5 x 7.5 km grid cells)
derived from all 13,666 sunlit Landsat 5 and 7 scenes available in the U.S. Landsat archive for December 2009 to November 2010
Validation
Validation Sites
99,177 km2
Harvested area
• 48 sites distributed in the top 16 U.S. states by harvested area
• each site ~ 7.5 x 7.5 km
• >5,800 reference fields manually selected from Landsat 5 and Google-
Earth images over the 48 sites for comparison with the automatically
extracted fields
pixel-based accuracy metrics
Extracted fields
Site Ref. # Extr. # one to one
matching
ratio
over
-split
under-
split
Ref. mean
size
Extr. mean
size
mean
size diff
CA3 161 149 139 86.3% 1 8 318.7 315.7 -0.9%
Validation example: a California site ~ 7.5 x 7.5 km
Site fields PA fields UA OA
total Ref.
pixels
total Extr.
pixels
total pixels
diff
IA 1 90.4% 98.6% 93.5% 51315 47040 -8.3%
object-based accuracy metrics
USDA NASS CDL, 2010Extracted fields
& reference field polygons
Extracted fields
& reference field polygons
(one-to-one matched,over-split,
under-split,missed)
Landsat 5 image, acquired
7/13/2010
Validation results over 48 validation sites• 81.4% of the >5,800 reference fields correctly extracted• mean of <2% mean-field-size difference with <5% standard deviation
Extracted fields with reference field polygons
(one-to-one matched, over-split, under-split, missed)
Landsat 5 RGB image, acquired 8/23/2010
Google-
Earth image,
acquired
9/28/2010
Site Ref. # Extr. #
one to
one
matching
ratio
over
-split
under-
split
Ref.
mean
size
Extr.
mean
size
mean
size diff
MI 1 134 134 96 71.6% 14 13 464.9 447.5 -3.7%
Validation example: a Missouri site ~ 7.5 x 7.5 km
object-based accuracy metrics
CONUS 2010 crop field size histogram
4,182,777 fields extracted
Field size (km2)
Num
ber
of
field
s x
10
5
Fie
ld a
rea p
erc
en
tage (
%)
Field size (km2)
1 × 1/2 mile2
1/2× 1/2 mile2
1/2× 1/4 mile2
1/4 × 1/4 mile2
field area percentage =
Σ (field area in histogram bin)
Σ (CONUS field area)
CONUS 2010 crop field size histogram
4,182,777 fields extracted
California 2010 crop field size histogram
116,888 fields extracted
Area (km2)
Num
ber
of
fie
lds
Google-Earth image. ~5.5 x 5 km
subset in California near Corcoran
1 × 1/2 mile2
1/2× 1/2 mile2
1/2× 1/4 mile2
1/4 × 1/4 mile2
Fie
ld a
rea p
erc
en
tage (
%)
Field size (km2)
Iowa 2010 crop field size histogram
308,917 fields extracted
Num
ber
of
fie
lds
Area (km2)
Google-Earth image. ~5.5 x 5 km
subset in Iowa near Eagle Grove.
1 × 1/2 mile2
1/2× 1/2 mile2
1/2× 1/4 mile2
Fie
ld a
rea p
erc
en
tage (
%)
Field size (km2)
1/4 × 1/4 mile2
2010 CONUS CDL majority crop map(with ≥ 10% CDL crop pixels in 7.5 x 7.5 km grid cells)
soybeanscorn
alfalfa
wheat (winter, spring and durum wheat)cottonother crops
2010 CONUS crop field size map
km2
4,182,777 crop fields
extracted
(mean field size in 7.5 x 7.5 km grid cells)
derived from all 13,666 sunlit Landsat 5 and 7 scenes available in the U.S. Landsat archive for December 2009 to November 2010
CONUS 2010 field size histograms for the major crops
Fie
ld a
rea p
erc
en
tage (
%)
Field size (km2)
cornsoybeanalfalfawheatcotton
Largest Extracted Field
Google-Earth image
(acquired on 8/18/2010)
2010 CDL
(CDL classified as
cotton in the annual
2008 to 2014 product)
3,200 acres
(5 square miles!)
Gaines, Texas
Future Work
(but needs funding …)
Fritz et al. (2015). Mapping global cropland and field size, Global Change Biology
• field size categories from geo-wiki information • qualitative field size information only and unknown quality
Global Field Extraction
HDF format products at: http://globalweld.cr.usgs.gov/collections
GeoTiff format products at: http://globalweld.cr.usgs.gov
Native resolution visualizations at: http://go.nasa.gov/2kLcKto
GLOBAL WELD 3 years (2009-2011) of monthly & annual Landsat 5 & 7
composites atmospherically corrected Nadir BRDF-Adjusted Reflectance
(NBAR)
Landsat 8
400 × 400
30 m pixels
August 23 2016
California
Sentinel 2A
1200 × 1200
10 m pixels
August 23 2016
California
Li Z., Zhang H.K., Roy D.P., Yan L., Huang H., Li J., 2017, Landsat 15-m Panchromatic-Assisted
Downscaling (LPAD) of the 30-m reflective wavelength bands to Sentinel-2 20-m resolution,
Remote Sensing, 9(7), 755.
Landsat 8 LPAD
600 × 600
20 m pixels
August 23 2016
California
Landsat 8
400 × 400
30 m pixels
August 23 2016
California
Some fields too small to be discernable with Landsat or Sentinel-2 data
India – Punjab, 15 x 15km scene
Landsat 7 ETM+ 30m (10/28/2002) Quickbird-2 2.5m (10/07/2003)
Some fields too small to be discernable with Landsat or Sentinel-2 data
China – Jiangsu province, 15 x 15km scene
Landsat 5 TM 30m (03/23/2005) Quickbird-2 2.5m (04/07/2005)
Summary• Landsat time series provide sufficient information to detect crop fields in an
automated way using a computer vision based approach across the U.S.
• First-ever U.S. wall-to-wall satellite-based field extraction demonstrated (using WELD processed Landsat 5 and 7).
• Validation results over 48 validation sites
– 81.4% of the >5,800 reference fields correctly extracted
– mean of <2% mean-field-size difference with <5% standard deviation
• New moderate resolution satellite data will provide improved global agricultural monitoring where field sizes are small
– Landsat-8 30m data have better quantization and signal/noise characteristics than previous Landsats
– Sentinel-2 has Landsat-like bands at 10m & 20m
• We have been contacted by the National Geospatial-Intelligence Agency
(NGA) to investigate the approach on NGA commercial high-resolution
data under a Cooperative Research and Development Agreement
(CRADA).
AcknowledgementsResearch funded by NASA NNH09ZDA001N-LCLUC “Changing Field Sizes of the Conterminous United States, a Decennial Landsat Assessment”.
The USGS EROS are thanked for provision of the Landsat data and the USDA NASS are thanked for provision of the Crop Data Layer product.
References• Yan, L. and Roy, D.P. (2016). Conterminous United States crop field size
quantification from multi-temporal Landsat data, Remote Sensing of Environment, 172, 67-86.
• Yan, L. and Roy, D.P. (2014). Automated crop field extraction from multi-temporal Web Enabled Landsat Data, Remote Sensing of Environment, 144, 42-64.
• White, E. and Roy, D.P. (2015). A contemporary decennial examination of changing agricultural field sizes using Landsat time series data, Geo: Geography and Environment. DOI: 10.1002/geo2.4.