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: zhangmiao@radi.ac.cn; wubf@radi.ac.cn;