6. FOREST COVER AND GROWING STOCK

Transcription

1 FOREST COVER AND GROWING STOCK 6.1 OVERVIEW This chapter presenting the forest cover and growing stock findings and assessment of forest cover of the study area, by interpretation of satellite data. The imageries of Indian Remote Sensing (IRS) satellite, LISS-III & LISS IV sensors have been used. NDVI transformation also performed for accurate assessment of forest cover findings. The purpose of this inventory is to assess and monitor to the extent, for sustainable development of nations forests in a timely and accurate manner by considering all the relevant parameters. Basically, forest stock maps are not geo-referenced so that, not possible to analyze the data on GIS platform even after converting the data into digital mode under computer-based GIS. These can overcome with the use of advanced Geospatial technologies. Remote sensing with its multi-spectral, multi-temporal and synoptic view has the potential to provide both spatial and temporal information on forests. This is possible because of the characteristic differences in structure, phoenological state, internal biochemical and biophysical properties of different vegetation types which result in reflection properties unique to each vegetation type. The spatial and spectral resolution of satellite data is of great advantage in precise spatial mapping of forest type distribution. Recent studies conducted by Forest Department of Government of Andhra

2 226 Pradesh and National Remote Sensing Centre (NRSC), Government of India have shown that the interpretation of high-resolution satellite data used to prepare forest stock mappings to achieve more informative stock maps than conventional ones. With the advent of high-resolution satellite imagery it is easy to obtain better information on stand density. 6.2 FOREST TYPE Based on Ecosystem, Champion and Seth proposed the classification of forest types in India during On the basis of availability of additional information and they revised the preliminary classification in their volume "A Revised Survey of the Forest Types of India" in According to that volume of forest types and classification in India, the following forest type, subtypes occurring in Giddalur Division of Guntur Circle The various types and subtypes occurring are: Southern Dry Mixed Deciduous forest Secondary Dry Deciduous forest Boswellia Forest Hardwickia Forest Southern Tropical Thorn Forest Southern Thorn Scrub Southern Euphorbia Scrub TYPE 5A/C3 TYPE 5/DS1 TYPE 5B/E2 TYPE 5B/E4 TYPE 6A/C1 TYPE 6A/C1/DS1 TYPE 6A/C1/DS2 Tropical Dry Evergreen Forest TYPE 7/C1 Tropical Dry Evergreen Scrub TYPE 7/C1/DS1

3 Species The list of important species generally found in the Giddaluru division as given below in table no (Source: Working plan of Giddaluru forest division) Table 6.1 Important species generally found in the Giddaluru Scientific division Name Local Name Anogeissus latifolia Chirumanu Terminalia tomentosa Nallamaddi Pterocarpus marsupium Yegisa Hardwickia binata Yepi Chloroxylon sweitenia Bhiltudu Lannea coromandalica Gumpena Cochlospermum reiigiosum Kondagogu (or) Adaviburuga Boswellia serrata Dalbergia paniculate Cleistanthus collinus Zizyphus xylopyrus Lagerstroemia parvrflora Tectona grandis Buchanania ianzan Diospyros melanoxylon Givotia rottjeriformis Syzigium cumini Morinda tinctoria Pterocarpus santalinus Gardenia latifolia Terminalia chebula Albizzia amara Emblica officinaiis Madhuca indica Anduga Pachari Kodisa (or) Vodisa Gothi chettu Chennangi Teku Sarapappu - kernal Seed/ Aku Teilapoliki Neredu Togaru Yerrachandanam Pedda Bikki Karaka Chikireni Usirica Ippa

4 FOREST QUALITY CLASS As per the Quality class adopted by Forest Department, Andhra Pradesh, total no. of classes are 6 based on the height class in meters Quality Class Height Class in meters I. ABOVE 25 Mts. II Mts. III Mts. IV Mts. V. 6-9 Mts. VI. Below 6 Mts. Out of 2,15,807 Ha, of forest area in the Study area, there are no forests which fall in quality class 1 & II. 33% area is in quality class III, 4% in quality class IV, 19% in quality class V, 40% quality class VI and 4% either blanks or sheet rock. This data indicates that forests whose height is below 6 Mt is 40% and that 33% is with 12 to 20 Mt height. The details of type of forest as per champion & seth classification, and quality class as per growing stock enumerated and given below in table no.6.2 (Source: Working plan of Giddaluru forest division)

5 229 Table 6.2 The details of type of forest as per champion & seth classification S.No Type of Forest Area under each Quality in ha I II III IV V VI Sheet rock & Blanks Total area in ha 1 5A/C /DS B/E B/E A/C A/C1/DS A/C1/DS /C o /C1/DS Sheet rock & Blanks Total: Percentage 33% 4% 19% 40% 4% 6.4 FOREST VEGETATION MAPPING The satellite technology has utilized for the estimation of forest resources and preparation of vegetation maps and for close monitoring of the status of forests. The forest areas of Giddalur Division has classified as three categories, based on the IRS, LISS III and LISS IV data. 1. Dense Forest ( 0.4 to 0.7 % density) 2. Open Forest (0.1 to 0.4 % density)

6 Scrub Forest (<0.1 % density) 6.5 FOREST DENSITY WISE DISTRIBUTION 54% of forest area is in less than 0.4 density, 15% is in 0.4 to 0.6 % density and 31% is in 0.6 to 0.8 % density. The outcome of this data is Nallamala high forest is 31% of the division, and a little more than half of the division (54%) is in lesser density class (0.4 and less). The details are shown in following table no 6.3 Table 6.3 S.No Type of Forest Approximate Area under Density Class in Ha 0 to 0.4% 0.4 to 0.6% 0.6 to 0.8% Total area in ha 1 5A/C /DS B/E B/E A/C A/C1/DS A/C1/DS /C /C1/DS Sheet rock & blanks Total: Percentage: 54% 15% 31%

7 231 According to above, year and range wise findings of Giddalur Division as shown below in table no. 6.4 and 6.5 Table 6.4 Type of Year wise in Hectares Forest During 1996 During 1998 During 2000 During 2003 During 2005 During 2007 Dense Forest 40, , , , , , Open Forest 52, , , , , , Scrub Forest 82, , ,17, , , , Blanks 41, , , , , , TOTAL Range wise forest density Table 6.5 Density Classes in Ha Range Giddalur Gundlakamma Turimella Kanigiri Ongole Total Ha. Dense area Open area Scrub area Others/Blanks Grand Total FOREST STOCK MAPPING IN PROTECTED FOREST AREAS OF GIDDALUR DIVISION Forest stock maps are defined as the maps which contain detailed spatial information on extent of recorded forest lands including the administrative

8 232 jurisdiction at various levels, infrastructure and communication facilities, water resources and the status of forest vegetation. The status of forest vegetation include categorization of forest into density classes, species distribution, assessment of growing stock, growth data for various species and age distribution. The forest stock maps depict forest type, density, encroachments, cultivation patches, human habitats, regeneration status and provide an idea about available resources. The following tables & pie charts showing the species wise area available in Giddalur forest division Mixed species Table 6.6 Range Area(Hac) Percentage Ongole Turimella Kanigiri Gundlakamma Giddalur Total Pie chart 6.1 Gundlakamma 4% Giddalur 12% Ongole 40% Kanigiri 31% Turimella 13%

9 Pure species Table 6.7 Range Area(Hac) Percentage Ongole Turimella Kanigiri Gundlakamma Giddalur `Total Pie chart 6.2 Giddalur 37% Ongole 45% Gundlakamma 10% Turimella 0% Kanigiri 8%

10 Species wise area in Giddalur Division Table 6.8 Pie chart 6.3 S.No Species Area(Hac) Percentage 1 Bamboo Casuarina Eucalyptus Dirasanam Jatropha Kanuga Maddi Miscellanious Sisoo Teak Extracted area 75 2 TOTAL

11 Range wise species Table 6.9 Giddalur Range S.No Species Area(Hac) Percentage 1 Eucalyptus Kanuga TOTAL Pie chart 6.4 Kanuga 42% Eucalyptus 58% Gundlakamma Range Table 6.10 S.No Species Area(Hac) Percentage 1 Eucalyptus Kanuga TOTAL Pie chart 6.5 Eucalyptus 34% Kanuga 66%

12 236 Kanigiri Range Table 6.11 S.No Species Area(Hac) Percentage 1 Bamboo Eucalyptus Dirasanam Kanuga Miscellanious Sisoo Teak TOTAL Pie chart 6.6

13 237 Turimella Range Table 6.12 S.No Species Area(Hac) Percentage 1 Bamboo Kanuga TOTAL Pie chart 6.7 Bamboo 31% Kanuga 69% Pie chart 6.8 Table 6.13 Ongole Range S.No Species Area(Hac) Percentage 1 Bamboo Casuarina Eucalyptus Jatropha Kanuga Maddi TOTAL

14 SPECIES COMPOSITION The general distribution of the major species, the number of stumps per Ha and the percentage of occurrence in the forests of the division and range wise has given below in the tables 6.6 and 6.7. Table 6.14 Species Composition Local Name Botanical Name No. of stumps Percentage per ha. of composition Chirumanu Anogeissus lalifolia Nallamaddi Terminalia tomentosa Narayepi Hardwickia binata Billudu Chloroxylona swietenia P.Yegisa Pterocarpus marsupium Gumpena Lannea coramandelica ,55 Jana Grewia rotundifolia Gotika Zizyphus xylopyrus Chennangi Lagerstrcemia parviflora Anduga Boswellia serrata Pacha re Dalburgia paniculata Chikireni Albizzia amara Kodisa Cleistanthys collinus Usiri Embelica officinalis Karaka Terminalia chebula Chinduga Albizia odoiatissima Tuniki Diospyros melanoxyion Poliki Givotia rottleriformis Red sander Pterocarpus santalinus R.Genupu Adina cordifolia

15 239 Somi Soymida febrifuge Ippa Madhuca ii:dica Neredu Syziqium cunvni Sandra Acacia chundra Velaga Umonia eleohantum B.Genupu Mttragyna paiviflora Teak Tectona randis KondaGogu Cochlospermum religiosum Bikki Gardenia latifolia Sara Buchanania latiforia Kunkudu Sapindus emarginatus Nemilinara Holoptelia integrefolia Peddabikki Gardenia latifolia Mango Mangifera indica Chandanam Santalum album Rosewood Pterocarpus santalinus G.Teak Gmelina arborea Chinta Tamarindus indica Prosphis Prosofis julifora ,03 Misc TOTAL

16 240 Table 6.15 Range wise Species composition Distribution of species Range wise in Giddalur Division (No. of Stems per Ha.) S.No Species Giddalur Gundla kamma Kanigiri Turimella Division 1 Teak Maiiamaddi P.Yegisa Karaka B.Genupu R.Genupu Rosewood Veiaga Naifamaddi Narayepi Anduga Red sander G.Teak Viango Neredu Billudu Chinduga Usiri Poliki Gumpena Chinta Chikireni

17 Ippa Tuniki Misc KondaGogu Nemilinara Chennangi Jana Kunkudu Bikki CL Chandanam Chirumanu Goiika Kodisa Pachare Peddabikki Prosphis Sandra Sara Somi Grand Total Apart from the 41 species mentioned, the other miscellaneous species account for about 19.17% of the crop.

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19 DISTRIBUTION OF CANOPY Canopy class-wise average growing stocks are M 3 /Ha in Dense Forest, M 3 /Ha in Open Forest and 8.32 M 3 /Ha in Scrub Forest. The total number of stems in the division is million of these, million fall in dense 5.95 million in Open and 0.83 million in Scrub Forest. Average number of stems per Ha in division is 249. They are in Dense Forest /Ha, Open Forest 142/ Ha and in Scrub Forest 86 /Ha. Prominent five species are contributing maximum for the Growing Stock in the division as given in the table.6.16 Table 6.16 S.No Local Name Scientific Name Volume in Million M 3 1 Nallamaddi Terminalia tomentosa Tiruman Anogeissus latifolia Yegisa Pterocarpus marsupium Nara Yepi Hardwickia binata Pachari Dalbergia paniculata CHANGE DETECTION Proper utilization and management of natural resources depends on the development of efficient resource information techniques. Remote sensing is one of the key tools which has capability to provide real time information and makes it possible to have meaningful repetitive surveys which can show how the changes have taken place so that the problem

20 244 can be dealt with after studying their means and causes. Forests are the dynamic features on the land surface and keep changing according to time and space. The change may be either positive i.e. regeneration etc. or negative such as fire, shifting cultivation etc. Accurate forest cover information is required at the local, regional and national administrative levels for various management purposes. It is also necessary to monitor the changes by time effective methodology to understand the trend, forest resource supply and demand, and it requires long term data sets. Thus, change detection is the process of identifying difference in the state of an object or phenomenon by observing this at different periods. The Remote sensing techniques have been effectively used to achieve this through the visual and change comparison.

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27 ACCURACY OF FOREST COVER ASSESSMENT Accuracy is assessed using remote sensing technology to give a reliability level to the results obtained after interpretation of the satellite data. In Present research work, Forest cover assessment has been carried out by interpretation of satellite data Accuracy assessment of forest cover In remote sensing technology, radiometric and geometric errors may occur due to many reasons like, altitude variations, sensor velocity, platform, panoramic distortions, earth curvature and random variations in the functioning of the sensor or by the intervening atmosphere between the terrain and remote sensing system. While interpretation, these errors also influence the remote sensing data for accuracy and assessment. In assessment of forest cover, accuracy assessment illustrates as, how accurately the satellite imageries have been interpreted to match the exact position on the earth/ground. This was done by ground truth database with careful designed field verification using high resolution satellite images For this research work, database collected during month of December 2007 at Giddalur forest division. A total of 430 points were randomly selected for accuracy assessment. Since the geographic coordinates of the sample points were known, corresponding coordinates are selected on the classified forest

28 252 map. The ground truth data was recorded giving land use details and it compared with the classified image of LISS-III and LISS-IV to prepare the error matrix The error matrix is an array of numbers arranged in rows and columns wherein, number of rows and columns are equal it represents open forest and dense forest, Scrub forest etc. The percentage of accurately classified sampling units i.e., diagonal elements out of total sampling units in the error matrix provides a measure of 'overall accuracy. Accuracy of each class was measured by calculating the accurately classified sampling units percentage and compared with total sampling units in row and column. Table 6.17 Error matrix Classification Ground truth (based on field data) User s classes Dense Open Scrub Non Total Accuracy Forest Forest Forest Forest (%) Dense Forest Open Forest Scrub Forest Non Forest Total Producer s Accuracy (%) Overall Accuracy (%) Overall kappa statistics 0.81

29 Accuracy of Forest Cover Findings The error matrix was prepared and presented in table no 6.17 the diagonal element at column 1 and row 1 i.e. 150 indicates the correct classification of Dense Forest on 150 out of 173 sample points. The off-diagonal points were showing misclassification of the respective classes. The error matrix method reveals that, out of 430 sampling points where observations were made, classification of 362 sampling points of the elements along the main diagonal of the matrix was found accurate. The total of classification accuracy was % which is termed as 'high' by the generally accepted norms. Table 6.18 Simplified error matrix Classification classes Ground truth (based on field data) User s Accuracy (%) Forest Non Total forest Forest Non forest Total Producer s Accuracy (%) Overall Accuracy (%) Overall kappa statistics In the table no.6.18 explained the simplified error matrix method, It was developed by grouping all the land use into two classes forest and non

30 254 forest categories. This has been done by combining the dense forest and open forest into single class like, forest, non-forest and scrub into one class. This method reveals that, interpretation of 388 sample points were found correct by giving a total accuracy of 90.23% Besides the total accuracy, individual classes was also determined the producers and user accuracy. Producer s accuracy measures the how accurately specific land-use class has been classified. It was derived by separating the number of correct sampling points in one class divided by total number of points derived from reference data. It includes the omission error; it means the percentage of observed features on the ground which is not classified in the map. If the omission error is more, producer's accuracy is low. User s accuracy measures the reliability of map and represents what is really on the ground. It was obtained by dividing the accurately classified units of class by total number of units. One class in the map has two classes on the surface. Like, 'right and wrong class, which means, same land class on the map same on the ground. Wrong class, which shows different land class the map and on the ground then, later classes were referred as omission errors. If more the omission error, user's accuracy is low. From table no.6.17 it was found that, producer's accuracy for dense forest, open forest, Scrub forest, Non-forest classes are 90.36, 85.71, 60 & respectively. Similarly, for user s accuracy is 86.70, 83.16, 75 & 87.87

31 255 respectively. From table no of simplified error matrix was found that, highly accuracy levels. Producer's accuracy for non-forest and forest classes is found as and respectively while user's accuracy of these classes is and respectively. To get more authenticate the results of accuracy, the Kappa analysis is also used which is a multivariate technique, provides a statistic known as KHAT. This coefficient gives a measure of total agreement of matrix. In contrast to the total accuracy i.e., ratio of sum of diagonal values to total number of sampling units of matrix, the KHAT also takes into account the non diagonal elements. This statistic commonly ranges between 0 & 1. If any classification has the Kappa coefficient more than 0.6 was considered as statistically sound. KHAT was calculated from error matrix method which is given in above table 6.17 is 0.81 which is indicating that, an observed classification is 81% NDVI TRANSFORMATION Normalized Differential Vegetation index (NDVI) is created to separate vegetative and non vegetative part. NDVI is a ratio of red band of visible spectrum and infrared band. It is highly correlated with vegetation parameters such as green leaf biomass and green leaf area and hence is of considerable value for vegetation discrimination. The remote sensing data extensively used for large area vegetation monitoring. Typically the spectral bands used for this purpose are visible and VIR bands. Various

32 256 mathematical combinations of these bands have been used for the computation of NDVI, which is an indicator of the presence and condition of green vegetation. These mathematical quantities are referred to as Vegetation Indices. The Normalized Differential Vegetation Indices are computed from the following equation NDVI = (NIR-RED)/ (NIR + RED) This equation is applied and the values of NDVI are computed for the entire Image of the Giddalur forest division, using ERDAS Imagine and Arc GIS Software. Land surfaces are characterized by a high degree of spatial heterogeneity of surface cover. These types have associated references in emissivity, thermal properties and reflectance characteristics (Mathews and Rossow, 1987). Satellite studies show that the soil moisture and vegetation parameter like vegetation status, leaf area density and photo synthetic activity are not independent. Naturally, vegetated surfaces can fill moisture from within the soil and may show signs of stress rather than adjacent arable land. Therefore there is a close relationship between meteorologic drought indicators and satellite based indices of vegetation activity (Walsh, 1987). Photo synthetically active vegetation typically has a reflectance of < 20% in the narrow band visible ( µm) but a much higher reflectance upto 60% in the near IR ( mm). It can be referred to from Fig. 6.8 as the generalized spectral reflectance curves of five different targets in the µm (visible and near IR regions). These positions of the

33 257 electromagnetic spectrum almost correspond with IRS 1C LlSS III channels of two and three. An NDVI (Tarpley et. al 1984) can be derived which Figure 6.8 Generalised spectral reflectance curves of five different targets in the μm (visible, near-ir) region. (μm) Capitalized on the spectral sensitivities of different land cover types. Thus the NDVI can be quantified by using, NDVI = (Band3 - Band2)/ (Band3 + Band2) of IRS 1C LlSS III DATA This NDVI is bounded ratio that ranges between -1 to +1. Clouds, water and snow have negative NDVI since they are more reflective in visible than near IR wave lengths. Soil and rock have a broadly similar reflectance giving NDVI close to '0', Only active vegetation has a positive NDVI being typically between about 0.1 and 0.6 values at the higher end of the range indicating

34 258 increased photosynthetic activity and a greater density of the canopy (Tarpley et. al 1984). This is explained by applying or transforming the digital numbers/spectral reflectance values/pixel values of NDVI using the formula given above for Giddalur forest division by adopting the following algorithm: (i) Computation of NDVI values for the entire study area by conversion of spectral reflectance values into NDVI values. (ii) Conversion of these NDVI values to a scaled channel values by using density-slicing method that measures apparent reflectance to sensor values. (iii) Display of image with NDVI and creation of a legend keeping the threshold values and the ranges that are shown in the below figures. Figures from 6.9 to 6.14 are the NDVI images for the Giddalur forest division obtained on a clear day of different years. Most of the variance in the data occurred in the agricultural land, forest and along the river grasslands. A proportionally small variance exists in the other features like built-up and wastelands. The greenness range is divided into discrete classes by slicing the range of NDVI values into ranges by fixing the thresholds. A cursory examination of pseudo colour image of NDVI and classified output of Land use/land cover (Fig ) reveals that along the water bodies, built-up lands yield negative values, their reflectance being more visible than near IR wavelengths. All the agricultural lands and forest areas located in Giddalur

35 259 forest division yield moderate values of NDVI. The dense forest, open forest, scrub forest and grass lands yield high index values because of the increased photosynthetic activity and greater density of canopy. The dense vegetation cover shows the high absorption and low reflectance in red band, hence the value of NDVI close to +1. The degraded vegetation cover shows low absorption and high reflectance due to soil back ground and the NDVI values close to -1. So the negative NDVI value indicates the forest degradation and a positive NDVI value indicates the dense forest cover.

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