Trees outside the Forest (ToF) mapping in Adilabad District
using Cartosat Data:
1. Interpretation of the imagery:
Classification scheme (Level III) adopted by Geomatics Center is
a. Natural growing on private/community/government lands – 3 types (teak, mixed teak, mixed/miscellaneous) and 4 canopy density classes (very dense, moderately dense, open, scrub)
b. Natural growing along streams, nallahs, tank foreshores – mixed/ miscellaneous and 4 canopy density classes (very dense, moderately dense, open, scrub)
c. Block Plantations (Manmade) – Teak, Eucalyptus, Mango, Cashew, Sapota, Miscellaneous, Bamboo etc
d. Habitations - Urban and Rural
e. Linear Plantations
i. Along roads
ii. Along canal banks
iii. Along railway tracks
iv. Along field bunds (natural and manmade)
f. Scattered Trees
It is opined that the Scheme is elaborative and sufficient.
However, the item 1(e)(iv) may be included in scattered trees i.e., 1(f), to simplify the work.
2. Inventory/Enumeration: Decisions on Sample Frame – Sample Size, Number of Samples, Size of the Sampling Unit, Sampling Intensity.
Suggestions made unanimously on the following issues
To have more number of samples with smaller size than few bigger samples, since more samples cover more area and better accuracy is achieved. It also meets the criteria of optimal sample size with same resources and time.
FSI method may be adopted as nearly as possible, for easy comparison and acceptance from other organizations.
To collect more parameters, but as simple as possible, if time and resources permits.
a. For Natural Class: the tree cover is similar to that of forest areas. Hence, the regular inventory methodology used for Forest areas can be adopted for this category. The methods viz., Probability proportionate to Area and Pre-inventory were discussed.
i. The number of sample points will be calculated using probability proportionate stratified random sampling (Probability proportionate to Area) method.
1.. The sampling intensity adopted for general inventory was 0.01 % (6700 points of 0.1 Ha over 64000 Sq Km). 0.03% sampling intensity can be adopted, if time and resources permits, otherwise intensity may be reduced.
ii. Alternately pre-inventory may be conducted to arrive at number of samples.
1. Pre-inventory @ 15 points for each class is sufficient.
2.Based on the variance in the population and allowable error 20%, the number of sample points will be estimated using t-distribution.
iii. 0.1 ha sample plot can be adopted for pre-inventory and final inventory.
iv. The same forms (Tree and Plot approach forms) and procedure can be followed with few modifications.
v. Collection of soil samples may not be necessary, since it consumes huge amount of time, in collection of 5 soil samples in each plot and mixing them into one sample, and testing the same in the laboratory, as adopted in regular inventory.
It is decided that pre-inventory method 2(a)(ii) to be considered, since this information can be reused in final inventory.
It is also opined that
Collection of information on legal status of the land, if time and resources permits, will help declaration of ‘deemed forests’. However, since the current inventory is confined to 0.01 % intensity or so and collection of this data may not be practicable.
Information on all life forms (shrubs & Regeneration, herbs and climbers) may not be necessary.
b. For Block Plantations: The crop in a block is generally uniform in nature in terms of age and species in 90% of the cases.
i. The number of sample points will be calculated using probability proportionate stratified random sampling method.
ii. Sampling intensity 0.1% is sufficient. The data is used to estimate the growing stock, feeling cycle, develop harvest schedules to project future timber supplies and for other operational planning activities.
iii. The same forms (Tree and Plot approach forms) and procedure can be followed with few modifications. The species, dbh, height, approximate age, average spacing may be recorded.
iv. It was discussed that the CIDA project data can be used to estimate the growing stock in SF Plantations. However, this data may not be beneficial in the current inventory, since this data is about 20 years old.
c. For Habitations Rural: Villages are to be stratified based on the geographical area or population (adopted by FSI).
i. It is decided that area stratification will be better which is readily available from the imagery.
ii. The classes are - Area between 10 to 5 km2, 5 to 2 km2, 2 to 1 km2, 1 km2 to 50 ha, 50 to 25 ha and area less than 25 ha.
iii. 6-10 samples (villages) in each class will be selected using Stratified Random Sampling Technique. Total enumeration will be done in each selected sample and extrapolated to entire class.
d. For Habitations Urban: Stratification was done using population by FSI. Area based stratification will be adopted similar to Rural areas, since areas are readily available from the imagery.
i. The stratification will be done basing on the geographical area. The classes adopted are - Area more than 50 km2, Area between 50 to 35 km2, 35 to 20 km2, 20 to 10 km2. Sample frame (Habitation units) will be generated.
ii. Habitation is to be divided in to number of segments using systematic grid or based on the manmade/natural features. Segments will be selected randomly for enumeration at desired sampling intensity. Total enumeration has to be done in the selected segments and extrapolated to entire area.
e. For linear plantations: the FSI method of using 125 m length * 10 m width sample plot is ideal, with 1% sampling intensity. The same forms (Tree and Plot approach forms) and procedure can be followed with few modifications.
f. For Scattered Trees: the individual trees also contribute substantially towards ToF. Counting the number of trees on screen is cumbersome and may be prone to errors.
i. 3 Ha sized 60 sample plots suggested by FSI.
ii. Select 60 samples randomly. The sample size to be adopted is 3 Ha.