Cropland mapping using big earth data and crowd-source samples. Cropland Mapping Usi… · Samples...
Transcript of Cropland mapping using big earth data and crowd-source samples. Cropland Mapping Usi… · Samples...
Cropland mapping using big earth data and crowd-source
samples
Miao Zhang
Aerospace Information Research Institute,
Chinese Academy of Sciences
November 2019, Tashkent, UZB
Outline
• Introduction
• Study area and data
• Method
• Result and discussion
• Conclusion and future plans
Source: World Food Programme
What about the future?
Introduction
Food security is a serious issue for Africa and might be more problematic;
Understanding the extent of cropland and the changes are the basis to fight against hunger and make agriculture plans;
There are several global/regional land cover / cropland extent datasets;
But with some uncertainty and great discrepancies exist among these products
Large discrepancies between four different datasets
62
8392 96
93 2 1
No agreement Partialagreement
Highagreement
Full agreement
Accuracy% Com. ERR
Mohsen Nabil, et al., draft manuscript, 2018
Outline
• Introduction
• Study area and data
• Method
• Result and discussion
• Conclusion and future plans
Study area
• Zambezi River Basin• Rain-fed
• Single cropping
• Low productivity
• Vulnerable to Agro-climatic conditions
Data
SATELLITE DATA CROWDSOURCE DATA IN-SITUMEASUREMENTS
Sentinel-2 top of canopy (TOC) reflectance
Data from GEOWIKI, NASA, and local experts
Joint field data collection in 2016-2018
Sentinel-2 Source: NASA
Samples includes GEOWIKI points, joint field surveys,
etc
Data collected using GVG
Outline
• Introduction
• Study area and data
• Method
• Result and discussion
• Conclusion and future plans
Methods
Data composite Training the classifier
Mapping Validation
Flowchart of procedures
Sentinel 2 image
collection
Multi source
samples
Field
measurements
Data composite via GEE
• Composite for rainy season / dry season
• Quality Mosaic based on Vis (qualityMosaic)
• Seasonal median composite (median)
• Composite by different percentile (ee.Reducer.percentile)
Dry season 2015 May to October
Rainy season 2015 October to May 2016
Classifiers and training
• Random forest
• Support vector machine
• First applied for six major classes
• Results were grouped into two classes, cropland & non-cropland
Validation
Kappa Coefficient
Confusion Matrix
User accuracy
Producer accuracy
Overall accuracy
Outline
• Introduction
• Study area and data
• Method
• Result and discussion
• Conclusion and future plans
Data composite
Median Max reflectance Quality Mosaic based on NDVI
Inputs for classification: MaxNDVI, MedianNDVI, SRmax, Srmedian, Viredge, P5, P25, P50, P75, P95
Mapping on Google Earth
EngineWithout downloading raw data
Samples Random forest SVM
T V T V
1 0.82 0.65 0.68 0.64
2 0.83 0.54 0.67 0.65
3 0.83 0.65 0.76 0.74
4 0.84 0.69 0.78 0.76
5 0.82 0.56 0.80 0.69
Average 0.83 0.62 0.74 0.70
Accuracy (5 repeat, 70% randomly as training and rest as validation)
Samples Random forest SVM
T V T V
1 0.77 0.67 0.76 0.76
2 0.80 0.70 0.76 0.86
3 0.82 0.92 0.93 0.93
4 0.78 0.78 0.72 0.82
5 0.76 0.66 0.94 0.84
Average 0.79 0.75 0.82 0.84
2015-2016 2016-2017
SVM out performs that of Random forest, but more time consuming
Cropland maps
Cropland 2015-2016 Cropland 2016-2017
Cultivated cropland area expanded by 27%
Inter-annual cultivated cropland
2015-2016 cropland
Expanded cropland in 2016-2017
Uncultivated in 2016-2017
Limitations
Pixel based methods result in ‘salt and pepper’ effect
Will try to include object-based classification methods
Outline
• Introduction
• Study area and data
• Method
• Result and discussion
• Conclusion and future plans
Conclusion and future plans
Taking advantage of GEE cloud platform, cropland mapping over large area is possible and promising if training data are sufficient
Data composite based on data over a single year still have gaps during rainy season, Two or three years data will be better
Training samples have significant impacts on the classification and accuracy; More samples will be derived from outdated datasets for early years
Expanded to early years using Landsat series to detect the inter-annual changes / hotspots, and driving forces will be analyzed
Thanks for your attention!
The authors acknowledge the financial support from the National Key Research and Development Program (No. 2016YFA0600302), National Natural Science Foundation of China (41561144013 and 41761144064)
Contacts: [email protected]; [email protected];