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Application of Satellite Based Remote Sensing for Monitoring and Mapping of India’s Forest and Tree Cover
Sampling Method
Besides generation of TOF maps, the information on block, linear and scattered patches
can be used to estimate the number of trees and the corresponding volume (species wise) using
appropriate sampling design by laying out optimum number of plots 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 were 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 was 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). Thereafter, data processing is carried out
following appropriate formulae corresponding to sampling design. The following table indicates
the results obtained with regard to stems/ha, total number of stems, volume/ha and total volume
of trees outside forests in rural areas of Gurdaspur district of Punjab, India. Likewise, similar
results obtained from different districts spread across the country are aggregated to generate
national level figures (Table 2).
Table 2: District level estimates (Gurdaspur, Punjab, India)
| Geographical Area | 3,551 sq.km. |
| Urban Area | 76.42 sq.km. |
| Forest Area | 368 sq.km. |
| Water bodies | 94.58 sq.km. |
| CNFA (Rural) | 3,013 sq.km. |
| Stems / ha | 18.5 |
| Total Stems | 5,563,798 (5.56 M) |
| Volume / ha | 3.5 cu.m |
| Total Volume | 1,054,577 cu.m(1.05 M cu.m) |
Accuracy of Classification
Any classification is not complete unless and until its accuracy is assessed. For
the present study the accuracy of classification was assessed by taking 53 points in block,
65 in linear and 65 in scattered stratum for a particular district. 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.
Table 3: 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 % |
Conclusion
The main objective of Forest survey of India in mapping and monitoring forest
and tree cover of the country on a two-year cycle is to know the dynamic changes of
forest resources in terms of quantity and quality over a period of time so that appropriate
planning and management interventions can be developed for their conservation and
sustainable utilization. Remote Sensing based forest cover mapping and monitoring
adopted by FSI has proved to be cost and time effective over traditional forest resource
monitoring. 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 addition, spatial distribution of
TOF resources on maps along with other features will provide information for planning
and implementation and utilization of these resources in a sustainable manner. Since, this
methodology enables resource-based stratification, it is expected to provide better
estimates of TOF resources than the one generated through field survey alone.
Bibliography
- 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
- Proceedings 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.
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