Assessment of Tree Resources Outside Forest Based on Remote Sensing Satellite Data

Assessment of Tree Resources Outside Forest Based on Remote Sensing Satellite Data Dr. J.K. Rawat 1, Saibal Dasgupta 2 & Rajesh Kumar 3 Introduction Forests have gained an important place in international political scenario mainly because of the realization of its role in combating green house gases, carbon storage sink, biodiversity conservation, global warming etc. However, modernization, advancements and developmental activities undertaken by mankind has taken a toll from forests and damaged it severely. In a country like India, beset with problems of population explosion, poverty and divisive social trends, conflicts for resource use will arise. The root cause of environmental problems is poverty and to overcome poverty, two things are essential. First, development must continue which means judicious and equitable exploitation of natural resources. Secondly, there must be a check in human and cattle population in order to prevent a collapse of life support system. Both require pragmatic approaches in thinking and calls for sustainable consumption so that exploitation by the present generation does not jeopardize the future of generations yet unborn. Wisdom demands, that resource should be handed over to the next generation, in an enhanced and improved manner, taking advantage of modern scientific technology and resorting to sound resource management principles. One of the management practices would be extensive tree growth outside forest areas for providing fuel, fodder and timber to the local people, which will also help in maintaining the ecological balance. The world has billions of trees that are not included in the FRA 2000 definitions of forests and other wooded land. Trees outside forests - i.e. trees available on agricultural land, along road, railways, canals, ponds, orchards, parks, gardens and homestead plays many role like forests. They make a critical contribution to sustainable agriculture, food security and rural household economies. They supply many products and services similar to forests. They protect crops and the soil against water and wind erosion, thus combating drought and desertification and protecting water resources. Trees outside forests have been defined differently by different countries and international agencies. In India, TOF is defined as all those trees, which has attained 10 cm or more diameter breast height, available on lands, which is not notified as forests. However, FAO defines TOF as trees available on lands which is not defined as forests or other wooded land. 1 Director, Forest Survey of India, Dehradun 2 Joint Director, Forest Survey of India, Dehradun 3 Deputy Director, Forest Survey of India, Dehradun

Since there exists a large amount of wood resources outside the conventional forests, accurate information about tree resources is a pre-requisite for their proper management. Vast volumes of data, collected by means of forest survey and inventory, are required for scientific management of forests. These data are also used for policy and planning purposes at the national, regional, state or local levels. To assess TOF resources, various initiatives have been undertaken world over, following different methodology. In India, FSI has started this work in 1991 following field inventory methods (SFR 2001). For this inventory, firstly, the study area, which is to be considered for TOF inventory is decided. This may be a state or group of districts. Since this area is fairly large there is every possibility of heterogeneity of the study variable i.e. growing stock. TOF being planted along with agricultural crops, is likely to be influenced by the Agro-ecological variables. Therefore, study area is stratified according to agroecological zones (AEZ). Districts, in India, are the basic planning and administrative units and therefore, is considered for further stratification of AEZs. Villages are treated as sampling units. This sampling technique is stratified random sampling. The number of sample villages to be surveyed in the study area is decided by undertaking a pilot study. The number of sample villages are assigned among different AEZs proportionate to the TOF area of the same. Further in each AEZ assigned number of villages are distributed to different districts proportionate to the rural TOF area of the district. The sample villages in each district are selected by using random number table. These villages are enumerated for the purpose of TOF inventory. Complete enumeration of all the trees of 10 cm and above dbh in the randomly selected villages in each district is carried out. Data is collected and is processed following appropriate formula. (Rawat, J.K. et.al. 2003) In Costa Rica, Hidalgo, D.M. and Kleinn, C. (Hidalgo, D.M. et.al. 2002) has advocated two-stage sampling by pre-selecting sites and followed by sample plots on the ground as secondary units. In their opinion pre-stratification based on the segmentation and fusion of Landsat image with IRS image would be ideal. This methodology was tested on silvopastoral system in Costa Rica. Holmgren (Holmgren, P. et.al 1994) in Kenya and Belouard in France (Beloured, T. 2002) have used systematic two-stage sampling. In the first stage they selected systematically aerial photographs covering the study area and in the second stage they chose number of sample points at the ground to enumerate trees and other physical and technical use of the area. Glen (Glen, 2002) commented that the main lessons learned from the two projects, namely, Sudan Reforestation & Anti-desertification and Resource Assessment & Development projects between 1987 and 1993, is that satellite imagery without good ground verification can produce misleading results, and that there is a need for ground plots to supply details on volume, stem/ha, species, site conditions and land use.

Map India 2004 Trees on Farmlands Trees along Roads Objectives The objectives of TOF inventory are: To assess the extent of plantations raised under various forestry schemes. To estimate the total number of trees in TOF To estimate the volume of standing trees outside the forest area. To estimate carbon sequestered in TOF To evaluate the role of TOF in the context of timber production To evaluate the role of TOF in the context of fuelwood, fodder and NTFP. To estimate the contribution of TOF in tree cover Methodology Using Remote Sensing Data The remote sensing data can provide stratification of the TOF resources, which can be utilized to increase the precision level and may turn out time effective. Some time the objectives of TOF resource assessment may require spatial distribution of resources on maps along with several other features. This objective can be appropriately tackled by the use of aerial photographs and satellite imageries in the assessment procedure. High-resolution satellite imageries can provide information even up to identification of a single tree but these are cost prohibitive. Therefore, some other cost effective method is to be developed. The LISS III data, which is multi spectral, and has a resolution of 23.5 m 23.5 m can provide information on vegetation cover. There are techniques available through which tree vegetated land can be segregated from agriculture land if the tree vegetated patch is about one ha and more. However, LISS data cannot be used for smaller patches or scattered trees. The IRS PAN data, which is monochrome, having resolution of 5.8 m 5.8 m can identify a tree vegetated land even less than 0.1 ha. Therefore, if both LISS III and PAN imageries are used, the stratification of TOF resources

Map India 2004 can be appropriately carried out on the basis of geometrical formation of trees i.e. block plantation (i.e. group of trees), linear plantation and scattered trees. (Kleinn, C. 2000) IRS PAN IMAGE FUSED IMAGE OF IRS PAN & LISS III IMAGE IRS LISS III Image TREES OUTSIDE FOREST MAP Taking advantage of multi spectral property of IRS LISS III and high resolution of corresponding IRS PAN, a methodology of TOF assessment has been developed. Firstly, the geo-referenced boundary of study area was supplied to NRSA to procure desired IRS IC/D PAN and LISS III data for the period between Oct.-Dec. 2002. After acquiring the images, the PAN image was geometrically rectified with the help of Survey of India toposheets on 1:50,000 Scale. The LISS III image was co registered with the rectified PAN images. PAN and LISS III images were fused using appropriate algorithm. The boundary of forest area was digitized and forest area was masked. The remaining LISS III image were classified into settlement, water bodies, burnt areas, tree cover and agriculture area using appropriate classifier viz. Maximum likelihood. This classification enables the interpreter to distinguish between tree cover and other dark areas on PAN images as also on fused image. To remove water bodies and dark surface features from PAN images, it

was masked using classified image of LISS III. The remaining area on PAN data (pseudo image) represents only trees and agriculture and using the threshold gray value of PAN image corresponding to tree cover, image was classified. This classified image was visually analyzed with respect to fused images for editing and refinement for inclusion and omissions. Since a cluster of trees having 0.1 ha area or more is defined as Block plantation, pixels were clumped and cluster of pixels having area less than 0.1 ha were eliminated. Incorporating these corrections final classified image was prepared having three classes in TOF areas, namely, Block, Linear and Scattered. Accuracy of Classification The accuracy of classification was assessed by taking 53 points in block, 65 in linear and 65 in scattered stratum. It is recommended that 50 or more points should be located for ground verification in each class. The accuracy of this classification was high as evident from the following confusion matrix of Kapurthala district of Punjab state. Confusion Matrix Block Linear Scattered Row Total User s Accuracy (%) Block 41 0 0 41 100 Linear 0 63 0 63 100 Scattered 12 2 65 79 82 Column Total 53 65 65 183 Producer s Accuracy (%) 77 97 100 Overall Accuracy = 92 % Producer s accuracy for Block is low because big grasses around water bodies were misclassified as block of trees, which will be adjusted while estimating TOF. Sampling Method Having done this stratification, with the help of appropriate sampling design optimum number of plots can be randomly selected in every stratum. Since the variability in each stratum is expected to be different demanding different sample and plot sizes, pilot studies were conducted to ascertain these so that the variability of the stratum can be properly addressed. In this pilot study, 0.1 ha, 0.2 ha and 0.3 ha plots were considered for Block Stratum. Similarly, strip of size 10 m 75 m, 10 m 100 m, 10 m 125 m, 10 m 150 m, 10 m 175 m & 10 m 200 m were considered for Linear Stratum. For scattered stratum plot of size 0.5 ha, 1.0 ha, 1.5 ha, 2.0 ha, 2.5 ha and 3.0 ha were considered for non hilly districts and 0.25 ha, 0.50 ha, 0.75 ha and 1.00 ha were considered for the hilly districts. Twenty concentric plots in each stratum were randomly selected and data was recorded. After analysis it was concluded that optimum plot size for Block, Linear and Scattered stratum are 0.1 ha, 10 125 m strip and 3.0 ha respectively for non hilly districts and 0.1 ha, 10 125 m strip and 0.5 ha for hilly district. It was also concluded through pilot study that the sample sizes for Block, Linear and Scattered stratum are 35, 50 and 50 respectively for non-hilly districts and 35, 50 and 95 for hilly district.

Desired number of sample points were randomly generated in each stratum, separately and the data on pre decided variables were collected on designed formats, following Manual for Assessment for Trees Outside Forests (FSI, 2003). Data processing will be carried out following appropriate formulae corresponding to sampling design. Conclusion As pointed out earlier, conventional and non conventional methodology can be chosen for assessment of TOF resources. Conventional methodology uses stratification variables like, agro-ecological zone, administrative boundaries to stratify the TOF resources and then random sampling is followed in each stratum to select the sampling units to draw estimates of parameter characterizing TOF resources. The methodology using digital image processing and geographical information system, as explained above can be effectively employed using multi spectral and high-resolution satellite imageries to stratify the TOF resources in such a way that the classification system of TOF resource remains valid. In each stratum optimum number of randomly chosen sample points are laid out for ground survey which will provide estimates of TOF resources. Since, this methodology enables resource-based stratification, it is expected to provide better estimates of TOF resources than the one generated through field survey only. References Belouard, T. 2002. Trees outside forests:france. FAO Conservation Guide, 35 Forest Survey of India, Dehradun, 2003: Manual on Assessment of Trees Outside Forests. Forest Survey of India, Dehradun 2003: State of Forest Report 2001. Glen, W.M. 2002, Trees Outside Forests: Sudan. FAO Conservation Guide, 35. Hidalgo, D.M. and Kleinn, C. 2002; Trees Outside Forests: Costa Rica. FAO Conservation Guide, 35. Holmgren, P., masakha,e.j. and Sjoholm,H.1994. Not all African land is degraded:a recent survey of trees on farms in Kenya reveals rapidly increasing forest resources.ambio 23(7): 390-395 Kleinn, C. 2000. On large area inventory and assessment of trees outside forests. Unasylva, 200(51), 3 1-. Legilisho-Kiyiapi, J. 2002. Trees outside forests:kenya. FAO Conservation Guide, 35 of the International Training Workshop on Assessment of Trees outside Forests conducted by FSI in April,2002 in collaboration with FAO of the United Nations. Rawat,J.K.,Dasgupta,S.,Kumar,Rajesh.,Kumar,Anoop.,Chauhan,K.V.S.2003: Training Manual on Inventory of Trees Outside Forests published by FAO under the EC-FAO Partnership Programme.

ABSTRACT Assessment of Tree Resources Outside Forest Based on Remote Sensing Satellite Data Forests have gained an important place in international political scenario mainly because of the realization of its role in combating green house gases, carbon storage sink, biodiversity conservation, global warming etc. One of the management practices would be extensive tree growth outside forest areas for providing fuel, fodder and timber to the local people, which will also help in maintaining the ecological balance. Trees outside forests - i.e. trees available on agricultural land, along road, railways, canals, ponds, orchards, parks, gardens and homestead plays many role like forests. They make a critical contribution to sustainable agriculture, food security and rural household economies. Since there exists a large amount of wood resources outside the conventional forests, accurate information about forest resources is a pre-requisite for their proper management. To assess TOF resources, various initiatives have been undertaken world over, following different methodology. Taking advantage of multi spectral property of IRS LISS III and high resolution of corresponding IRS PAN, a methodology of TOF assessment has been developed. LISS III image has been classified into settlement, water bodies, burnt areas, tree cover and agriculture area using appropriate classifier viz. Maximum likelihood. To remove water bodies and dark surface features from PAN images, it was masked using classified image of LISS III. Classified image was visually analyzed with respect to fused images for editing and refinement for inclusion and omissions. Incorporating corrections, final classified image was prepared having three classes in TOF areas, namely, Block, Linear and Scattered. The methodology using digital image processing and geographical information system can be effectively employed using multi spectral and high-resolution satellite imageries to stratify the TOF resources in such a way that the classification system of TOF resource remains valid. In each stratum optimum number of randomly chosen sample points are laid out for ground survey, which will provide estimates of TOF resources. Since, this methodology enables resource-based stratification, it is expected to provide better estimates of TOF resources than the one generated through field survey only.

Title of the paper: Name of Author(s): Assessment of Tree Resources Outside Forest Based on Remote Sensing Satellite Data 1. Dr, J.K. Rawat, IFS, Director, Forest Survey of India, Dehradun 2. Sh. Saibal Dasgupta, IFS, Director, Forest Survey of India, Dehradun 3. Sh. Rajesh Kumar, ISS, Director, Forest Survey of India, Dehradun Author(s) Affiliations: Mailing Address: Email Address: Forest Survey of India, Kaulagarh Road, P.O.:IPE, Dehradun 248 195 1. Dr. J.K. Rawat fsidir@vsn.com 2. Sh. Saibal Dasgupta saibaldasgupta@hotmail.com 3. Sh. Rajesh Kumar rajsus1@rediffmail.com Telephone number(s) 1. Dr. J.K. Rawat 0135 2756139 2. Sh. Saibal Dasgupta 0135 2754507 3. Sh. Rajesh Kumar 0135 2755042 Fax number(s) 1. Dr. J.K. Rawat 0135 2759104 2. Sh. Saibal Dasgupta 0135 2754507 3. Sh. Rajesh Kumar 0135 2754507 Author(s) Photograph and a brief Bio Data 1. Dr. J.K. Rawat, IFS Indian Forest Service (Haryana) / 1972 M.Tech. in Mechanical Engineering from IIT, Delhi Ph.D in Forest Economics from University of Toronto, Canada 2. Sh. Saibal Dasgupta, IFS Indian Forest Service (M.P) / 1984 M.Sc. (Botany)

3. Sh. Rajesh Kumar, ISS Indian Statistical Service / 1986 M.Sc. (Statistics)