AREA ESTIMATION UNDER AGRO FORESTRY USING REMOTE SENSING AND GIS

Size: px

Start display at page:

Download "AREA ESTIMATION UNDER AGRO FORESTRY USING REMOTE SENSING AND GIS"

Arnold Rich
6 years ago
Views:

1 AREA ESTIMATION UNDER AGRO FORESTRY USING REMOTE SENSING AND GIS Tauqueer Ahmad Indian Agriultural Statistis Researh Institute, New Delhi INTRODUCTION One of the most important problems faed by the developing ountries is produing food in adequate quantities and of good quality for the fast growing population. It is also neessary to ensure that the quality of land resoures that are diretly or indiretly utilized in produing food is maintained and improved. Therefore, it is important to inrease produtivity and at the same time to onserve and enhane the quality of eo-system. Agroforestry is a land management and farming system that are not only apable of produing food from marginal agriultural land but also apable of maintaining and improving the quality of environment. The integration of farming with forestry praties on the farm for the benefit of agriulture is known as agroforesty. Agroforestry plays a vital role in ahieving integrated rural and urban development. The National Agriulture Poliy (2000) emphasized the role of agroforestry for effiient nutrient yling, nitrogen fixation, organi matter addition and for improving drainage and underlining the need for diversifiation by promoting integrated and holisti development of rainfed areas on watershed basis through involvement of ommunity to augment biomass prodution through agroforestry and farm forestry. The Task Fore on Greening India for Livelihood Seurity and Sustainable Development of Planning Commission (2001) has also reommended that for sustainable agriulture, agroforestry may be introdued over an area of 14 million ha out of 46 million ha irrigated areas that are degrading due to soil erosion, water logging and salinization. For integrated and holisti development of rainfed areas, agroforestry is to be pratied over an area of 14 million ha out of 96 million ha. This all will, besides ensuring eologial and eonomi development provides livelihood support to about 350 million people. The pratie of agroforestry an help in ahieving these targets. Therefore, in the quest of optimizing produtivity, the multi tier system ame into existene. Gap of demand and supply of forest produe in India is widening and forests are unable to fulfill the demand. Agroforestry an play an important role in filling this gap and onservation of natural resoures. The origin of agroforestry praties is believed to have been during Vedi era (Anient period, 1000 BC) but the agroforsetry as siene is introdued only reently. The systemati researh in agroforestry geared up after the establishment of the International Counil for Researh in Agroforestry (ICRAF) in 1977, whih was renamed in 1991 as International Centre for Researh in Agroforestry. During , ICRAF adopted a new brand name World Agroforestry Centre, to reflet more fully their (ICRAF s) global reah and also their more balaned researh and development agenda; however their legal name International Centre for Researh in Agroforestry will remain unhanged. In India, organized researh in agroforestry was initiated in 1983 by the establishment of All India Coordinated Researh Projet (AICRP) on Agroforestry by Indian Counil of 285

2 Agriultural Researh (ICAR) at 20 entres and later establishment of the National Researh Centre for Agroforestry at Jhansi in At present 39 entres of agroforestry are working in the ounty. But there is no reliable data available for area under agroforestry in the ountry and there is no sientifi methodology available for generation of area statistis under agroforestry. Therefore, it was proposed to develop a methodology for estimation of area under agroforestry using Remote sensing and GIS tehniques under the study entitled National Initiative on Climate Resilient Agriulture-Agroforestry Component (NICRA-AF). Remote sensing and GIS has emerged as powerful tool for planning and deision support in the area of agriultural researh and mangement. Estimation of rop area statistis is one of the important field in whih this tehnology has been used very suessfully. Estimation of forest over is being done by Forest Survey of India (FSI) using Remote Sensing tehniques but for estimation of tree over, sampling methodology is being used by FSI. However, remote sensing satellite data is being used for stratifiation in estimation of tree over. Therefore, it was proposed to estimate area under agroforestry using high resolution satellite imageries (LISS IV) under this study. 2. STUDY AREA For development of the proposed methodology Ludhiana distrit of Punjab State was seleted on pilot basis. Ludhiana distrit is one of the twenty distrits of Punjab. The distrit has an area of 3685 sq. km. with a population of 30,30,352 (2001 Census). Its head quarter is at Ludhiana, whih is loated in entral part of the distrit and is at far away distane from the State Capital, Chandigarh. There are twelve (12) bloks & seven (7 tehsils) and the total number of villages is 915. The density of population per square kilometer is 804 (Soure: NIC, Ludhiana, Census-2001). Ludhiana distrit has an area of 3,685 sq. km. and it is the fourth largest distrit of Punjab. It is the most entrally loated distrit whih falls in the Malwa region of Punjab State. It lies between ' ' N Latitude and ' ' E Longitude. It is bounded on the north by Sutlej river whih separates Ludhiana from Jalandhar distrit. It shares ommon boundaries with Rupnagar & Fatehgarh distrits in the East and Moga & Firozpur distrits in the West, Barnala distrit and Sangrur distrits in the South respetively. Ludhiana distrit is well developed in transport and ommuniation failities. The National Highway No.1 originating from Amritsar to National Capital New Delhi passes through the distrit. 3. DATA USED IN THE STUDY The data used in the study area is given below: (i) Satellite data 286

3 The satellite data used for the study for Ludhiana distrit of Punjab State is mentioned in Table 1. Table 1. Satellite data used for Ludhiana distrit S. Date of Pass Path & Row Sensor Satellite Soure No Ot /029 LISS-IV IRS-P6 NRSC Ot /030 LISS-IV IRS-P6 NRCS Ot /025 LISS-IV IRS-P6 NRSC Ot /026 LISS IV IRS-P6 NRSC Ot /027 LISS-IV IRS-P6 NRSC Ot /025 LISS-IV IRS-P6 NRSC Ot /026 LISS-IV IRS-P6 NRSC Ot /027 LISS-IV IRS-P6 NRSC Mar /041 LISS-IV IRS-P6 NRSC Ot /041 LISS-IV IRS-P6 NRSC Ot /042 LISS-IV IRS-P6 NRSC Ot /043 LISS-IV IRS-P6 NRSC The above mentioned satellite data purhased from National Remote Sensing Centre(NRSC), Hyderabad was in Geo-TIFF format. The data being LISS IV, resolution is 5.8 metre and is in three bands (Green: 0.52 to 0.59µm, Red: 0.62 to 0.68µm, NIR: 0.77 to 0.86µm). (ii) Collateral data Besides satellite data other data used for the study are: 1) List of villages obtained from Survey of India (SOI), Dehradun 2) Village loation map 3) Distrit map of Ludhiana distrit with village boundaries from SOI 4) Topographi maps of Ludhiana distrit from SOI 4. PROPOSED METHODOLOGY There is no reliable data available for area under agroforestry in the ountry and no sientifi methodology is available for generation of area under agroforestry statistis. Therefore, it was proposed to develop a methodology for estimation of area under agroforestry using Remote sensing and GIS tehniques under this study. For development of methodology Ludhiana 287

4 distrit of Punjab State was seleted on pilot basis. The details of the proposed methodology are as follows: (i) Layer Staking Layer staking is the proess of ompositing of different bands of raw image (single band) one after other. After layer staking of different bands, false olor omposite (FCC) image was reated and it was saved in image (*.img) format. (ii) Image Geometri Corretion Image geometri orretion is very important to orret the geometrially distorted images due to the perspetive of the sensor optis, the motion of the sanning system, the motion and (in) stability of the platform, the platform altitude, attitude, and veloity, the terrain relief, and the urvature and rotation of the earth. In geometri orretion, Ground Control Points (GCP) play a signifiant role for retifying images. A speifi pixel on an image or loation on a map whose geographi oordinates are known, GCPs are used to orret geometri distortion in an image by mathing image oordinates with map oordinates. After that, image and map oordinates are used to ompute the transformation matrix for retifying image. In other way, the transformation of o-ordinate system from toposheet to images using Ground Control Points is known as image geometri orretion. The geometri orretion of images was done using SOI toposheets. (iii) Edge Mathing Edge mathing is a proedure to adjust the position of features extending aross map sheet boundaries. This funtion ensures that all features that ross adjaent map sheets have the same edge loations. In edge mathing tehnique, mathing of similar feature lass from two geo-referened satellite images is done. Here, edge mathing was done using Auto-syn tool of ERDAS Imagine software. (iv) Mosaiing of Images The geo-referened images were joined together and a larger image was formed. (v) Subset of Area of Interest (AOI) It is a proess of delineation of area of interest (region of interest) from the large image. Therefore, AOI was delineated for the distrit under study. (vi) Ground truthing 288

5 Extensive ground truthing for image analysis was done in 20 villages of 7 bloks of Ludhiana distrit in the month of November 2011 and in 22 villages of 8 bloks of Vaishali distrit in Deember The data olleted through GPS during ground truthing was treated as training site data and was applied to whole image. (vii) Digital Image Classifiation For estimation of area under agroforestry, hoie of the satellite data pertaining to some speifi period in whih agroforestry trees an be easily identified is very important. Field observation with satellite imagery shows that in the study area, there may be two ritial periods for hoosing the satellite data for delineation of agroforestry areas. During the kharif season, vegetation growth is good and beause of anopy density it gives a typial signature. This signature helps in delineating agroforestry area from nearby ropland. It gives very typial blakish red signature beause of healthy vegetation. As per the rop alendar, the Otober month is ideal for aquiring satellite image and during this period loud free images are available for winter rop area estimation. The September image for delineation of agroforesty is advisable as during this period mature agroforestry field is in a unique pattern giving a unique spetral signature, whih ould be easily delineated from rest of the neighboring land over lasses espeially from other rops grown during that period. Classifiation Method Maximum likelihood lassifier was found to be aurate enough for lassifying the study area for extration of agroforestry fields. In this supervised lassifiation tehnique, training sites were generated based on detailed ground survey. The equation for the maximum likelihood/bayesian lassifier is as follows: D ln T 1 a 0.5ln Cov 0.5 X M Cov X M where D = weighted distane (likelihood) C = a partiular lass M = the mean vetor of the sample of lass X = measurement vetor of the andidate pixel a = perent probability that any andidate pixel is a member of lass 289

6 Cov = determinant of Cov Cov -1 = inverse of Cov ln = natural logarithm funtion T = Transposition funtion (viii) Land Use/Land Cover Analysis The land use/land over analysis of the distrit was done using both unsupervised and supervised lassifiation methods. Under unsupervised lassifiation, nine land use/land over lasses viz. ropland, agroforestry, srubland and fallow land, reserve forest, plantation, water bodies, sand and dry streams and built-ups were identified for the distrit. Area under these lasses was estimated and it was found that area under reserve forest and plantation are on higher side where area under agriulture is on lower side. Thus, these estimates were not found to be reliable. Therefore, the training sites were reated for the nine major lasses of the distrit using FCC. Ground truth data olleted on agroforestry systems from the distrit through GPS was used as training sites for agroforestry lass identifiation. Spetral signatures were generated for these lasses in three bands viz. green, red and near-infrared. It was observed that spetral signatures of forest and agroforestry were quite different from eah other. In ase of supervised lassifiation, maximum likelihood lassifier was applied. Using maximum likelihood lassifiation, area under agroforestry along with other lasses was obtained. It was observed that sand & dry riverbed was intermingled with settlements. So in order to redue suh error, both settlements and dry riverbed in the distrit were masked with the help of toposheets and FCC. Similarly, agroforestry and forest were intermixed for the distrit. For minimizing the intermixing pixels, masking approah was applied to estimate area under agroforestry. The forest area was masked out from the whole images of the distrit and hene area under forest was obtained. Finally, the estimated area under different lasses was found to be reliable. The estimated area under forestry and agriulture lasses were ompared with the estimates published in FSI report 2009 and published by Ministry of Agriulture, Govt. of India. It was observed that the estimates obtained using the proposed methodology were in agreement with these published figures. 290

7 (ix) Auray Assessment Classifiation auray was tested in terms of Kappa o-effiient. The Kappa o-effiient expresses the proportionate redution in error generated by a lassifiation proess, ompared with the error of a ompletely random lassifiation (Congalton, 1991). For example the value 0.90 implies that the lassifiation proess was avoiding 90 per ent of the error that a ompletely random lassifiation generated by the field observation. Ground truth data on different land uses inluding agroforestry was olleted from the distrit and lassifiation auray was omputed using maximum likelihood method of supervised lassifiation. Error matrix was generated using maximum likelihood method. The overall auray of lassifiation and Kappa oeffiient were obtained whih indiates perent agreement between lassified and atual land uses/ land overs. Methodology Flow Diagram RAW DATA LAYER STACK IMAGE GEO-METRIC CORRECTION USING SOI TOPOSHEETS EDGE MATCHING MOSAIC IMAGES SUBSET OF AOI SUPERVISED CLASSIFICATION (USING MAXIMUM LIKELYHOOD METHOD) LAYER MASK (USING SUPERVISED CLASSIFICATION) AND MOSAICING ACCURACY ASSESMENT 291

8 5. RESULTS AND DISCUSSIONS The land use/land over analysis of the distrit was done using both unsupervised and supervised lassifiation methods. Under unsupervised lassifiation, nine land use/land over lasses viz. ropland, agroforestry, srubland and fallow land, reserve forest, plantation, water bodies, sand and dry streams and built-ups were identified for the distrit. Area under these lasses was estimated and it was found that area under reserve forest and plantation are and perent respetively whih is on higher side. Further, area under agriulture is only perent whih is on lower side. The estimates for identified lasses are shown in Table 1 whih are not found to be reliable. Table 1. Land use/land over in Ludhiana distrit using unsupervised method Land use/ land over lasses Area (ha) Perentage Agriulture 67, Agroforestry 41, Srubland 37, Reserve Forest 45, Plantation 55, Current fallow/waste land 29, Water bodies 17, Sand & dry streams 21, Built-ups 33, Unavailability of satellite data 18, Total 3,68, Therefore, the training sites were reated for the nine major lasses of the distrit using FCC. Ground truth data olleted on agroforestry systems from the distrit through GPS was used as training sites for agroforestry lass identifiation. Spetral signatures were generated for these lasses in three bands viz. green, red and near-infrared. It was observed that spetral signatures of forest and agroforestry were quite different from eah other. In ase of supervised lassifiation, maximum likelihood lassifier was applied. Using maximum likelihood lassifiation, area under agroforestry along with other lasses was obtained. The estimated area under agroforestry in Ludhiana distrit is 1.64%. The estimates for different lasses are shown in Table 2 for the distrit. 292

9 Table 2. Land use/land over in Ludhiana distrit using maximum likelihood lassifiation method Land use/ land over lasses Maximum Likelihood Area (ha) Area (Sq.km.) Perentage Cropland 3,08, , Agroforestry 6, Current Fallow 1, Reserve Forest 1, Plantation Srubland Built-ups 29, River, Canal & Water bodies 1, Sand and dry streams Unavailability of satellite data(restrited area) 18, Total 3,68, , It was observed that sand & dry riverbed was intermingled with settlements. So in order to redue suh error, both settlements and dry riverbed in the distrit were masked with the help of toposheets and FCC. Similarly, agroforestry and forest were intermixed. For minimizing the intermixing pixels, masking approah was applied to estimate area under agroforestry. The forest area was masked out from the whole images of the distrit and hene area under forest was obtained. Land use/land over map for the distrit was generated. Classifiation auray was tested in terms of Kappa o-effiient. Ground truth data on different land uses inluding agroforestry was olleted from the distrit and lassifiation auray was omputed for the distrit using maximum likelihood method of supervised lassifiation. Error matrix generated using maximum likelihood method whih is shown in Table 3 showed that error of omission for agroforestry lass is only 3.04 per ent, whih means there is an auray of about per ent for this lass. The overall auray of lassifiation is per ent whih is very signifiant and Kappa oeffiient omes out to be 0.93, indiating 93 perent agreement between lassified and atual land uses/land overs. 293

10 Table 3. Error matrix for Ludhiana distrit using maximum likelihood lassifier AG CF AF SL WB DR RF PL BU Row Total Err. C (%) AG CF AF SL WB DR RF PLNT BU Total Err.O (%) AG-agriulture, CF-urrent fallow, AF-agroforestry, SL-srubland, WB-water bodies, DRdry riverbed/sand, RF-reserve forest, PL-plantation, BU-built-ups, Overall Auray (%) Kappa oeff Finally, the estimated area under different lasses was found to be reliable. The estimated area under forestry and agriulture lasses were ompared with the estimates published in FSI report 2009 and published by Ministry of Agriulture, Govt. of India. It was observed that the estimates obtained using the proposed methodology was in agreement with these published figures. 6. CONCLUSION In view of unavailability of any reliable data on area under agroforestry, methodology for estimation of area under agroforesry based on Remote sensing and GIS was developed. Area under agroforesry was estimated for Ludhiana distrit of Punjab State using the developed methodology. Area under agroforestry for Ludhiana distrit was obtained as 6, hetares. The land use/land over analysis of the distrit was done using both unsupervised and supervised lassifiation methods. The total number of lasses obtained in the distrit are nine. Land use/land over map was generated for the distrit. The estimated area under 294

11 different lasses was found to be reliable. The estimated area under forestry and agriulture lasses were ompared with the estimates published in FSI report 2009 and published by Ministry of Agriulture, Govt. of India. It was observed that the estimates obtained using the proposed methodology was in agreement with these published figures. REFERENCES Ahmad, T., Rai, A., and Singh, R. (2012). Objetive Spatial Analyti Hierarhy Proess for identifiation of potential agroforestry areas using GIS. Mod. Assist. Statist. Appl., 7(1), Dadhwal, V.K. and Parihar, J.S. (1985). Estimation of wheat areage of Karnal distrit (Haryana) using Landstat MSS digital data. Tehnial note, IRS- UP/SAC/CPF/TN/09, Spae Appliation Centre, Ahmedabad. Dadhwal, V.K., Rahul, D.S., Medhavy, T.T., Jarhal, S.D., Khera, A.P., Singh, J., Sharma, T. and Parihar J.S. (1991). Wheat areage estimation for Haryana using satellite digital data. Jour. Ind. So. Rem. Sens., 19, Murthy, C.S., Thiruvengadahari, S., Raju, P.V. and Jonna, S. (1996). Improved ground sampling and rop yield estimation using satellite data. Int. Jour. Rem. Sens., 17(5), Rai, A., Srivastava, A. K., Singh, M., Singh, S. and Sapra, R. K. (1999). A pilot study of agroforestry in Chhahhrauli blok of Yamunanagar distrit(haryana). Projet Report, IASRI, New Delhi. Sahoo, P.M., Rai, A., Singh, R., Handique, B.K. and Rao, C.S. (2005). Integrated approah based on Remote Sensing and GIS for estimation of area under paddy rop in North- Eastern Hilly region. Jour. Ind. So. Agril. Statist. 59 (2), Singh, R., Goyal, R.C., (2000). Use of remote sensing satellite data in rop surveys. Projet Report, IASRI, New Delhi 295

Energy Savings from Increased Preventive Maintenance on Indiana Highways

Energy Savings from Increased Preventive Maintenance on Indiana Highways TRANSPORTATION RESEARCH RECORD 112 27 Energy Savings from Inreased Preventive Maintenane on Indiana Highways EssAM A. SHARAF AND KuMARES C. SINHA In this paper are desribed the development of trade-off