Procedure for Development of Crop Mask for Major Seasonal Crops in Punjab & Sindh Provinces of Pakistan

Transcription

1 [Type text] Procedure for Development of Crop Mask for Major Seasonal Crops in Punjab & Sindh Provinces of Pakistan

2 Preface Agriculture sector contributes around 21% to GDP annually. Major contributing crops to GDP growth are wheat (around 2.5%), cotton (2.0%), Rice (1.8%) and sugarcane (1.5%). Productivity of crops is initially dependent on area sown by the farming community. Availability of digital information on crop acreage is a base line data to identify change in cropping patterns, understand the market dynamics, assess flood/drought damages and improve crop statistics. The present procedure document is intended to be a guideline for the development of Crop Mask for Rabi and Kharif crops in Punjab and Sindh provinces of Pakistan. The document provides the steps for standard working procedure to perform the different tasks such as acquisition and processing of satellite images, Ground Truth Survey (GTS) campaigns, digitization and development of spectral signatures, creating training areas (AOIs, ROIs), classification of satellite images, post classification Accuracy Assessment, extraction of target crop classes, vectorization of crop classes, cross checking of vector data and aggregation of polygons for development of crop mask at provincial level. To achieve uniformity in working procedure of all the team members, this document also explains criteria for crop layer symbology and standard output file formats. 1

3 Table of Contents Page 1. Introduction Objectives Work Flow Basic Guidelines Data Storage Hierarchy Image Processing and Nomenclature Ground Truth Surveys and Image Classification Acquisition of Satellite Data Masking of other Crops Pre-Classification Field Survey Digitization of Segments Creating Training Areas (AOIs, ROIs) from Digitized Segments Classification of Satellite images Quality Control Post Classification Accuracy Assessment Sampling Extraction of Target Crop Classes Vectorization of Target Crop Classes Cross Checking of Vectorized Data Merging of Polygons for Final Crop Mask

4 1. Introduction Information on Agriculture land use are one of the most important parameters required for policy making and decision regarding reliable crops statistics, food security, biodiversity and environmental sustainability. Keeping in view increasing population pressure in Pakistan, there is a need for improved management of the agricultural resources. To make this happen, it is imperative to gather reliable data not only related to agriculture land use but also on the major agricultural crops (wheat, cotton, rice, sugarcane, spring maize, and potato). Latest understanding of the climate change issues and adaptive strategy also needs availability of reliable information on agriculture land use at local, regional and global scales. Crop mask (data layer) is a kind of information required to formulate and implement appropriate risk management strategies with respect to possible food insecurity scenarios. It can also provide base line information for identifying any drastic change in cropping patterns. Spatially explicit information on crops can also facilitate policy makers in planning agricultural infrastructure such as research stations, agro-industry, market locations, farm to market roads etc. The information can be used to analyze market situation with respect to specific crop in any area. This data can additionally be used in crop forecasting models by identifying area with large concentration of crops. Similarly crop masks are used to aggregate results to h i g h e r administrative levels. Specific crop mask can also be used for a better exploitation of the mixed pixels in coarse resolution imagery which are increasingly be used in regional and global crops/ floods modeling systems. SUPARCO has developed agriculture masks from time to time. However in order to standardize, it was decided to develop a new crop mask showing 3

5 spatio-temporal variations using both medium and high resolution satellite data for peak growth seasons. Keeping in view requirements for frequent floods in Pakistan and to standardize and refresh the existing agricultural masks, SUPARCO in collaboration with Food and Agriculture organization of the United Nations (FAO-UN) has initiated the project on development of crop masks for Punjab and Sindh using satellite technology. 2. Objectives The primary objective of the project is to produce crop mask showing distribution of different Rabi and Kharif crops in Punjab and Sindh provinces respectively. The working procedure is documented to: Monitor and improve the procedure followed by all team members involved in development of crop mask Provide a uniform working environment for Quality Control 4

6 3. Work Flow 5

7 4. Basic Guidelines This covers the project related data storage hierarchy scheme, satellite data nomenclature convention, processing levels, crops colour coding and metadata generation formats Data Storage Hierarchy 4.2. Image processing and Nomenclature Satellite data in near real-time approach will be process in Lab. A dedicated staff team will keep record of data acquisition and one AM will be deputed as team leader who will ensure the quality control at initial stage. All images will be spatially enhanced with single approach as per policy. These resolution improved images will be ortho-rectified with 6

8 baseline ortho-rectified spot images. These images will be subsetted based on standard district boundary provided by the CA. These subsets will be given name according to below nomenclature rule to control the data generation throughout the project process; KharifCottonMultanSPOT acquisition1.img K C MN S A1.img K = Kharif season C = Cotton crop MN = Multan District S5 = SPOT 5 constellation 199/281 = Satellite path/row 2013 = Acquisition year 06 = Acquisition month 21 = Acquisition date A1 = 1st Acquisition A2 = 2nd Acquisition img = Raster image KharifRiceMultanSPOT acquisition1.img K R MN S A1.img KharifSugarcaneMultanSPOT acquisition1.img K S MN S A1.img RabiWheatMultanSPOT acquisition1.img R W MN S A1.img RabiPotatoMultanSPOT acquisition1.img R P MN S A1.img 4.3. Ground Truth Surveys and Image Classification Ground truthing surveys will be carried out within two weeks of data acquisition. Survey will require not only the segment information but also additional locations on each surveyed satellite image/subset will be collected to optimize the crops signatures database. Additional stratified ground survey points will be generated for each satellite image. Image 7

9 classification will be based on supervised classification based on collected field crops information Crops Colour Codes /Symbology Colour codes/symbology will be adopted to ensure standardization of crops identification on classified satellite images. These codes will remain valid for the raster based vectorized data. These codes are as follows; 1. Wheat = Light Green 2. Cotton = Dark Green 3. Rice = Light Blue 4. Sugarcane = Yellow 5. Potato = Brown 6. Other crops = Pink Image Classifications Control Flags Each ground survey team will be responsible for the classification of the satellite data. They will run supervised classifiers on both first and second acquisition data to produce classified data. They will strictly follow the nomenclature as well as crop colour codes. The classification accuracy will be determined based on surveyed segments as well as each subset based collected crops signatures. Each image subset will be accompanied by associated Metadata. This Metadata will at least include following things; Image Name: R P OK S A1.img District: Okara Classified by: Ibrar ul Hassan Akhtar Classification method: Supervised Classification date: 10-Nov-2013 Survey Date: 20 October 2013 Classifier name: GMLT (Gaussian Maximum Likelihood Technique) Number of Ground Segment used: 2 Additional crops signatures collected: yes Segments based Accuracy of Target crop = 75% Random Points Accuracy Target crop= 70% 8

10 5. Acquisition of Satellite Data Minimum cloud cover SPOT (2.5, 5 & 10 m) data will be acquired for already defined agricultural zones according to Ist and 2nd acquisition plans for Rabi and Kharif crops. 6. Masking of Other Crops To get minimal mixing in final classification result, after Ist acquisition of satellite data, areas of Lush green i.e Sugarcane and other short term crops (i.e Oil seeds) will be omitted in such a way that it will be masked as off. The mask image will be saved as Mask-Scene No-Date Masked Image In case of Kharif crops to minimize mixing of crops, satellite data acquired during Ist acquisition will be used for extraction of Sugarcane class, which will then be masked in satellite images to be acquired during 2nd acquisition. 9

11 7. Pre-Classification Field Survey Pre-classification field surveys will be carried out for both Rabi and Kharif crops based on already defined segments. To have maximum number of spectral signatures and cover all feature types, information about areas other than defined segments will also be recorded randomly. For example sometimes the segments lying in a scene does not cover some crop type but the area covered by that image contains that type. By doing this, maximum number of training areas will collected for that particular type due to which a supervised classification will be carried out with better accuracy. 8. Digitization of Segments After completing pre-classification field surveys of the entire study area, all the segments and other features information will be digitized thoroughly as the quality of classification depends on this. 10

12 Rabi Segments Kharif Segments 11

13 9. Creating Training Areas (AOIs, ROIs) from Digitized Segments The digitized segments will then be exported to training areas as follows: 9.1. As there will be a number of satellite images to be classified, therefore to make training areas for all of them separately, first of all one of these images will be displayed in the ArcMap All the segments lying on it will be selected. These selected segments will then be exported with a new file name like a. 12

14 9.3. The file a will be displayed and the original segment file will be unselected. 13

15 9.4. On the ArcMap main menu Selection/Select by Attributes, in the layers selecting the file a then double clicking Crop then clicking = and then double clicking the crop which is desired to export first i.e Wheat, Then clicking Apply and then OK All the Wheat type polygons within each segment of this image will be selected. 14

16 9.6. All the selected segments on this image will be exported with a name like Wheat and then displaying. 15

17 In this way shapefiles of all the crop types within digitized segments lying on this image will be exported with their respective names i.e Fodder. etc. 16

18 9.7. Exporting Shapefiles of all crop types to training areas (ROIs, AOIs) ROIs/AOIs obtained from digitized segment In case of Kharif crops the same steps will be applied for creating training areas (ROIs, AOIs) for Rice, Cotton, Sugarcane etc. 10. Classification of Satellite Images Based on the research results carried out on crop classification in the past, spectral and spatial information delivered by different resolution optical sensors like HRV (SPOT) are generally sufficient to recognize different agricultural crops. To carry out crop classification with reasonable accuracy, 17

19 multi-temporal datasets from this satellite sensor with an appropriate timing of acquisitions over the growth cycle will be used. In case of Rabi crops after 2nd acquisition of satellite data, already developed mask for Sugarcane and other short term crops will be excluded from these images. In case of Kharif crops Sugarcane area extracted during Ist acquisition will be excluded from satellite images of 2nd acquisition. The training areas (ROIs) already obtained will be utilized for supervised classification of images acquired during 2nd acquisition. To obtain good classification result, some other training areas will be collected through visual comparison with those collected based on field survey. Maximum Likelihood Supervised classification will be applied on the images using image processing software (i.e ERDAS Imagine, ENVI, etc). Collecting ROIs for other features (Water bodies, bare land, etc) 18

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21 11. Quality Control The purpose of quality control is to understand the scope of project and its relation to the overall management of the project. Keeping in view the importance of the quality control during the classification phase as well as vectorization phase and work load; either a team with more than one members or more than one team should be formulated for the quality assessment of Crop Mask project Post Classification Accuracy Assessment The accuracy of spatial data is the closeness of results of observations, computations, or estimates to the true values or the values accepted as being true" (USGS, 1990). To express classification accuracy, error matrix or confusion matrix will be prepared. In this matrix, classification results will be compared with ground truth information collected through sampling. This matrix will show the cross tabulation of the classified land cover and the actual land cover revealed by sample site results. Different measures and statistics will be derived from the confusion matrix. Ground Truthing Classification Result 20

22 Ground Truth SOP:Crop Mask Development Preparing Confusion (Error) Matrix Classification Results Wheat Fodder Orchard Unclassified Producer s accuracy (Precision) Wheat a b c d 1- (a*100)/(a+b+c+d) Fodder e f g h 2- (f*100)/(e+f+g+h) Orchard i j k l 3- (k*100)/(i+j+k+l) Reliability/User s accuracy (a*100)/(a+e+i) (f*100)/(b+f+j) (k*100)/(c+g+k) The diagonal elements in the matrix represent the number of correctly classified pixels of each class, i.e. the number of ground truth pixels with a certain class name that actually obtained the same class name during classification. The off-diagonal elements represent misclassified pixels or the classification errors, i.e. the number of ground truth pixels that ended up in another class during classification. Off-diagonal row elements represent ground truth pixels of a certain class which were excluded from that class during classification. Such errors are also known as errors of omission or exclusion. Based on the above confusion matrix besides Kappa coefficient, average and overall accuracy will also be determined as: Average Accuracy= (1+2+3)/3 Overall Accuracy= (a+f+k)/(a+b+----+l) 21

23 11.2. Sampling Assessing the accuracy of thematic maps derived from remote sensing data is both time and money consuming. Sampling scheme has been selected in such a way to require a minimum number of ground truth samples depending on the objectives of the project. As Simple Random Sampling is area weighted i.e it yields too many samples in larger areas and too few samples in smaller areas. Keeping in view already defined strata for crop estimates, to ensure testing of each class adequately, stratified random sampling will be used for ground validation of image classification results for both Rabi and Kharif seasons. Sampling sites and number of points will be decided based on crops density and their heterogeneity Before going to field for collection of stratified random samples, the original image and its classification result will be displayed in the ArcMap. A point shapefile having the name showing also the number and date of scene will be created. 22

24 After going to field, Editing will be started in such a way that for each point (X & Y or Long. & Lat.), the crop type will be entered in the attribute table at that time After collecting all the sample points data, the X,Y coordinates will be saved in Excel sheet along with the attribute for each point (i.e wheat, fodder, orchard, etc). Then save this file with only X,Y coordinates in Text (Tab delimited) type i.e Book1. 23

25 After returning from Post classification Ground truth survey, using ERDAS imagine, Accuracy Assessment will be performed as follows: - Open ERDAS Imagine - On the main menu Classifier/Accuracy Assessment the following window will appear 24

26 In this Accuracy Assessment window Class represents the classification class while the reference represents the ground truth information for the respective X,Y coordinate collected through GPS during ground survey. - In the Accuracy Assessment window go to File/Open and then navigate to the directory where the supervised classified image has been saved and then open it - - In the Accuracy Assessment Window go to view/select viewer and then click on the viewer on which the image is displayed i.e Viewer#1:classification-1.img 25

27 - Now go to view/change color, the change colors window will look like: Be sure that Points with no reference should be while and Points with reference should be yellow and then clicking ok - On the Accuracy Assessment window go to Edit/Import User-defined Points, select the X,Y coordinates file (Book 1) and click ok. An Import Options window will open like: 26

28 On this window for Field type select Delimited by Separator and leave the rest as these are and click ok, Accuracy Assessment window will look like: - In this window go to view/show All, all the X,Y coordinates points will now appear on the classified image (i.e Classification-1) 27

29 - Now using the already saved Excel file, the actual class values for all the X,Y coordinates as collected through field survey will be entered in the reference column as below: - Now to show the class values as given during image classification, go to Edit/Show class values, the class column will be filled in with the values indicated during classification. 28

30 By comparison of Class and reference columns it can be judged that how much correctly the classes have been assigned to these points during supervised classification - Now go to Report/Options, It should be made sure that Error matrix, Accuracy Totals and Kappa Statistics are all tuned on - Now to show the statistics of accuracy assessment go to Report/Accuracy Report, the following table will appear: 29

31 For crop masking project the allowable accuracy has been set as 80-85%. If the overall accuracy of resulted classification image does not match the required range, the classification will be revised. 12. Extraction of Target Crop Classes After completing all the accuracy assessment steps, only the target crop class such as Wheat in case of Rabi and sugarcane, Rice and Cotton in case of Kharif will be extracted one by one from the classified images. Extracted Wheat class Saving Files: The crop extracted file (i.e Wheat) will be saved as: 30

32 Classification-Season (i.e Rabi)-Crop name-province-zone-sceneno- Date 13. Vectorization of Target Crop Classes All the target crop classes extracted in raster format will be converted to vector format using any of software (i.e. ENVI, ERDAS Imagine, ArcGIS) Saving Files: The final vectorized data for all the crop classes for all the scenes will be saved as: Vectorized-Season -Crop class-province-zone-sceneno-date 14. Cross Checking of Vectorized Data To get more refinement in final crop mask, using minimum working scale of 1:25000, the vector files generated for all the crop classes will then be cross checked with the following: - Classified images (to check the shift and area) - Original unclassified images - LCCS 31

33 In addition, in case of Rabi crops, the vector files will also be overlayed on satellite images of Oct-Nov and May-June to exclude Sugarcane area mixed with Wheat class. 15. Merging of Polygons for Final Crop Mask After thorough cross checking of all crop classes polygons, polygons of each file will be merged and then all files of a crop class within a province will be merged to get final province base crop mask showing spatial distribution of that crop class such as Wheat. Final province wise crop mask for Kharif crops such as Sugarcane, Rice and Cotton will also be developed in the same way. 32