U.S. Geological Survey U.S. Department of Interior GFSAD 30 Cropland Products of Nominal 250 m Using MODIS Data and Cropland Mapping Algorithms: Results and update Pardhasaradhi Teluguntla, Prasad Thenkabail, and Jun Xiong Jun 24-26, 2014 Fourth Workshop on Global Food Security Analysis 30 m (GFSAD30) Sioux Falls, SD, USA

U.S. Geological Survey U.S. Department of Interior GFSAD30 Products for Australia Outline

U.S. Geological Survey U.S. Department of Interior GFSAD30 Cropland Products of nominal 250 m Outline 1. Goals and Objectives 2. Issues 3.Data: BDI for Australia 4.Methods and Preliminary results a)Classification, identification, and label classes for the 4 products b)Automated Algorithms\script to automatically compute the 4 products 5.Discussion

U.S. Geological Survey U.S. Department of Interior GFSAD30 Products for Australia Goals and Objectives

U.S. Geological Survey U.S. Department of Interior ❖ Cropland Extent ➢ Croplands vs. non-croplands ❖ Cropping Intensity ➢ Single, double, triple, continuous cropping ❖ Watering Method ➢ Irrigated vs. rainfed ❖ Crop Type ➢ Major 8 crops and others ❖ Cropland change over space and time ➢ Major 8 crops and others GFSAD30 Cropland Products of Nominal 250 m Goal: to Produce 5 Products of GFSAD30 Project We will focus on these 3 products for now in this presentation These products are not part of this presentation

U.S. Geological Survey U.S. Department of Interior GFSAD30 Products for Australia Current State-of-Art

U.S. Geological Survey U.S. Department of Interior ❖ State-of-Art ➢ There are 3 global cropland products + several land cover land use products in which croplands is a class ❖ Limitations ➢ coarse resolution (1 km or higher) ➢ Lack of detailed work on croplands (e.g., where are the irrigated areas, what is the frequency of cropping, what crops are grown and where?, what changes are occuring) GCE V1.0 GFSAD30 Cropland Products of Nominal 250 m Current State-of-Art on Global Croplands: 12 Classes derived from 4 existing products The 12 classes constitute 12 AOIs. Class 1 = AOI 1, Class 2 = AOI 2….. Class 12 = AOI 12

U.S. Geological Survey U.S. Department of Interior Cropland Mapping Algorithms (CMAs) Approach to Producing GFSAD30 Products

U.S. Geological Survey U.S. Department of Interior We will approach development of GFSAD30 products in two distinct steps: ❖ Synergestic approaches to cropland classification leading to ➢ Class identification and labeling; ➢ Creation of knowledge base ❖ Automated cropland classification algorithms ➢ Development and implementation of Automated algorithm to reproduce cropland products year after year Cropland Mapping Algorithms (CMAs) Approach to Automated Cropland Classification Algorithms We will discuss this today We will not discuss this today

U.S. Geological Survey U.S. Department of Interior Cropland Mapping Algorithms (CMAs) Datasets and Megafile Datacubes

U.S. Geological Survey U.S. Department of Interior ❖ MODIS Data Composition: 36 layers per year ➢ monthly NDVI maximum value composite (MVC): 12 layers per year; ➢ monthly B1 Minima: 12 layers per year; ➢ monthly B2 Maxima: 12 layers per year; ❖ Secondary data ➢ Elevation, slope, Precipitation, surface temperature, PET etc Cropland Mapping Algorithms (CMAs) for Australia Datasets in the Megafile for entire Australia: nominal Year 2000

U.S. Geological Survey U.S. Department of Interior Note: 1.We will take each GCE V1.0 class and investigate how much of that is croplands versus non 250 m MODIS resolution; 2.In addition, we will establish cropping intensity and irrigated vs. rainfed 3.This leads to GCE 250 m

U.S. Geological Survey U.S. Department of Interior MODIS 250 m Mega file data cube (36 Layers ): 12 B1 minima, 12 B2 maxima, 12 NDVI (one band per month) AOI / segments GCE V1.0 Final Output GCE V2.0 (illustrated here for AOI 1 which has 5 classes) Calssify Cropland Mapping Algorithms (CMAs) for Australia Process for producing GCE 250 m resolution using MODIS data for nominal year 2000

U.S. Geological Survey U.S. Department of Interior Cropland Mapping Algorithms (CMAs) Synergestic Cropland Classification and Identification (SCCI)

U.S. Geological Survey U.S. Department of Interior Synergestic Cropland Classification and Class Identification (SCCI) involves: A.Classification ❖ unsupervised ISOCLASS clustering of MFDC; B.Class Grouping and identification ❖ Spectral matching technique: SCS R-square grouping; ❖ bispectral tassel cap plots for better understanding of classes; ❖ NDVI phenological plots to determine cropping intensity and class identification C. Class labeling ❖ Very high resolution imagery from ESRI archive ❖ Ground data Cropland Mapping Algorithms (CMAs) for Africa Synergestic Cropland Classification and Class Identification (SCCI)

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 Unsupervised ISOCLASS classification using MODIS 250m MFDC: 25 classes

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 Tassel cap/ Bi-Spectral plots: 25 classes Example1 : Class 1 (cropland) showing how band 1 (red) and band 2 (NIR) reflectivity varies from month to month

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 Tassel cap/ Bi-Spectral plots: 25 classes Example1 : Class 1 (non-cropland) showing how band 1 (red) and band 2 (NIR) reflectivity varies from month to month

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 NDVI time series:25 classes; Class 1 identification process detailed Non cropland Sub- meter to 5 meter imagery

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 NDVI time series:25 classes; Class 12 identification process detailed Croplands; identified from VHRI (sub-meter to 5 meter imagery)

U.S. Geological Survey U.S. Department of Interior Synergestic Cropland Classification and Identification (SCCI) re-grouping of classes of AOI 1 for Australia

U.S. Geological Survey U.S. Department of Interior Spectral Matching Technique: SCS R-square to Group Classes SCS- R 2 Matrices for AOI1; Australia C1C2C3C4C5C6C7C8C9C10C11C12C13C14C15C16C17C18C19C20C21C22C23C24C25 C C C C C C C C C C C C C C C C C C C C C C C C C

U.S. Geological Survey U.S. Department of Interior 6 Highly correlated classes Australia: AOI-1 re-grouping 25 classes to unique croplands versus non-cropland classes using SCS- R 2

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 re-grouping 25 classes to unique croplands versus non-cropland classes using SCS- R 2 12 highly correlated classes

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 re-grouping 25 classes to unique croplands versus non-cropland classes using SCS- R 2

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 re-grouping 25 classes to unique croplands versus non-cropland classes using SCS- R 2

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 re-grouping 25 classes to unique croplands versus non-cropland classes using SCS- R 2 Finally, 25 classes are grouped into 4 unique classes

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 Tassel cap Bi-Spectral plot characteristics of the Final 5 re-grouped classes How to separate / distinguish Classes Example : Class 1 month 12 Class 2 month 8 Class 3 month 10 Class 4 month 9 Class 5 month 10 Note: We can distinctly distingwish the 5 classes in these particular months

U.S. Geological Survey U.S. Department of Interior o Stage 1 Croplands vs. Non-croplands o Stage 2 single, double, triple, or continuous cropping o Stage 3 irrigation versus rainfed o Stage 4 8 major crops or others GFSAD30: Australia Class Naming Convention

U.S. Geological Survey U.S. Department of Interior AOI_1 _class#Histogram%GroupLevel1Level2Level3 Class %G5 Non-CroplandBarren Class %G1CroplandSingleirrigated ? Class %G1CroplandSingleirrigated ? Class %G1CroplandSingleirrigated ? Class %G1CroplandSingleirrigated ? Class %G1CroplandSingleirrigated ? Class %G1CroplandSingleirrigated ? Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G3Natural vegetation Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G2CroplandSingleirrigated Class %G3Natural vegetation Class %G4CroplandDouble(?)Irriagted Class %G4CroplandDouble(?)Irriagted Class %G3Natural vegetation Class %G4CroplandDouble(?)Irriagted Class %G4CroplandDouble(?)Irriagted %Total9.89Mha %Total-Cropland8.75Mha % Total- Non_Cropland1.14Mha Class Naming Convention Croplands, single, irrigated Non-croplands We will use the following data to label this: 1.VHRI (sub meter to 5 meter; 2.Ground data 3.GCE V1.0 We will use the following data to label this: 1.NDVI phenology; 2.Bispectral plots 3.SCS R-square 4.dendogram 5.VHRI (sub-meter to 5 meter) 6.Ground data We will use the following data to label this: 1.NDVI phenology plots; Note: 1.14 million hectares of AOI 1 (out of 9,89 M ha) is non croplands in GCE V2.0. This was classified as croplands in GCE V1.0

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 Class Identification and Labeling Process: Distribution of Random areas for Each Class for selecting VHRI For each of the 5 classes 20 random areas are chosen for selecting VHRI (sub meter to 5 m)….. A total of 100 VHRI are selected for the 5 classes of AOI 1

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 20 very high resolution images (VHRI; sub-meter to 5 meter) for class 1 of AOI 1

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 20 very high resolution images (VHRI; sub-meter to 5 meter) for class 2 of AOI 1

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 20 very high resolution images (VHRI; sub-meter to 5 meter) for class 3 of AOI 1

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 20 very high resolution images (VHRI; sub-meter to 5 meter) for class 4 of AOI 1

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 20 very high resolution images (VHRI; sub-meter to 5 meter) for class 5 of AOI 1

U.S. Geological Survey U.S. Department of Interior Class codeLabel description A1C001AOI1 Class 1 (23.5%) Croplands, single crop, irrigated(?) A1C002AOI1 Class 2 (51.5%) Croplands, single crop, irrigated A1C003AOI1 Class3 (9.5%) Non croplands, Natural vegetation A1C004AOI1 Class 4 (13.4%) Croplands, double crop(?), irrigated A1C005AOI1 Class 5(2%) Non croplands, barren lands GFSAD30: Australia Class Naming Convention for AOI 1, 5 final classes of Australia

U.S. Geological Survey U.S. Department of Interior GFSAD30: Australia Class Naming Convention for AOI 1, 5 final classes of Australia MODIS 250 m GCE V2.0 for AOI 1 Note: Omissions: 11.5% (class 4 and 5) of the AOI 1 area of GCE nominal 1 km is now non- croplands in GCE 250 m

U.S. Geological Survey U.S. Department of Interior GFSAD30: Australia Class Naming Convention for AOI 1, 5 final classes of Australia and their Characteristics

U.S. Geological Survey U.S. Department of Interior AOI-1 of resolution Cropland area = 9.89 M ha (full pixel area ) AOI-1 of 250m resolution Cropland area = 8.75 M ha 11.5% of area is omission GFSAD30: Australia Final classes of AOI 1 in GCE V1.0 (1 km) vs. GCE V2.0 (250 m)

U.S. Geological Survey U.S. Department of Interior ❖ Automated cropland classification algorithms ➢ Development and implementation of Automated algorithm to reproduce cropland products year after year ………the above work is in progress Cropland Mapping Algorithms (CMAs) Approach to Automated Cropland Classification Algorithms

U.S. Geological Survey U.S. Department of Interior

AOI-1 of resolution Cropland area = 9.89 M ha (full pixel area ) AOI-1 of 250m resolution Cropland area = 8.75 M ha 11.5% of area is omission Australia: AOI-1 : GCE V1.0 vs GCEV 2.0

U.S. Geological Survey U.S. Department of Interior Australia: AOI-1 Tassel cap/ Bi-Spectral plots: integrated picture of 25 classes