PLANT RICHNESS MODELLING IN SOUTH GUJARAT USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM 2 2...

Page No.

839

FRONT COVER

Photo : Dr. Lalit Narayan

Wood culture in ancient India as reflected from identification of confiscated wooden artifacts dating between th14-19 century A.D.

Sangeeta Gupta and K.S. Rana .........................................................................................................

A preliminary inventory and management plan for medicinal plant conservation areas at Amgaon and Kakati

in Belgaum district of Karnataka

Harsha V. Hegde, Vinayak Upadhya, Sandeep R. Pai, Gurumurthi R. Hegde,

Girish Hosur and Divakar Mesta .......................................................................................................

RESEARCH NOTES

Ternate leaves: An abnormal phyllotaxy in teak (Tectona grandis L.F)Hanumantha, M., Rajesh P. Gunaga, Roopa Patil, Suma S. Biradar and Nagaraj...................................

Anoda cristata (L.) Schltdl. (Malvaceae): A new record for MaharashtraRahangdale, S. S. and S. R. Rahangdale.............................................................................................

Utilization of halophytes in arid Kachchh districtJagruti P. Shah .................................................................................................................................

FROM THE INDIAN FORESTER - ONE HUNDRED YEARS AGO

S

OBITUARYMaheshwar Dixit - D.G.S. .................................................................................................................

Calotropis procera, a new digitalis substitute ..........................................................................................easoning of timber ................................................................................................................................

843

PLANT RICHNESS MODELLING IN SOUTH GUJARAT USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM

2 2 2G.D. BHATT, S.P.S. KUSHWAHA , S. NANDY, KIRAN BARGALI , D. TADVI , P.S. NAGAR AND M. DANIEL

Forestry and Ecology Department, Indian Institute of Remote Sensing, ISRO, Dehradun- 248001, Uttarakhand, India

1

ABSTRACT

The paper presents a geospatial modelling approach for the assessment of plant richness in south Gujarat using a two-tier approach i.e., satellite image (Resourcesat-1) for vegetation type/land use mapping, landscape analysis, and the plant richness modelling on 1:50,000 scale. The study showed that nine vegetation types viz., teak mixed dry and moist deciduous forest, mangrove forest, mangrove scrub, riverain forest, ravine thorn forest, forest plantation, degraded forest and Prosopis juliflora scrub respectively. The largest area is occupied by teak mixed dry deciduous forest by 14.98 per cent. The overall accuracy was found to be 87.78 per cent. The plant richness map, generated using SPLAM software, showed three levels of plant richness. The vegetation type-wise plant richness assessments were also calculated. It was observed that 55.97 per cent of forests area had under high plant richness, rest of the area falls under low, medium and very high categories. The district-wise plant richness was also calculated.

Key words: Plant richness modeling, Remote sensing, Landscape, Vegetation type/land use.

Mangrove forests at Mayabunder, A&N Island .

The plant richness map, generated using SPLAM software, showed three levels of plant richness.

1

2Department of Botany, M.S.University of Baroda, Vadodara-390 007 (Gujarat)Department of Botany, Kumaun University, Nainital- 263001, Uttarakhand, India

Introduction earth will be severely affected and therefore necessitates characterization for conservation and management (Roy Humans have extensively altered the global et al., 2012). environment, changing global biogeochemical cycles,

transforming 40-50 per cent of the ice-free land surface, The diversity of global species reached an all time and converting grasslands, forests, and wetlands into high in the past geological period. The most advanced agricultural and urban systems. Today, humans directly groups of organisms insects, vertebrates and flowering or indirectly consume about one-third of the terrestrial plants reached their greatest diversity about 30,000 net primary productivity and harvest fish that contribute years ago. However, since then, the species richness has 8 per cent of the ocean’s productivity (Stewart et al., decreased, while human populations have grown 2000). They also use 54 per cent of the available (Leakey and Lewin, 1996). Between 1600 and 2000 (400 freshwater, and the projected use is likely to increase to years), an estimated 4000 species of mammals, 9000 70 per cent by 2050 (Chapin et al., 2000). Biologically species of birds, 6300 species of reptiles, 4200 species of diverse and resilient ecosystems are critical to human amphibians, 19,100 species of fishes, 1,000,000 species well-being, sustainable development and poverty of invertebrates and 250,000 species of flowering plant eradication (Singh and Kushwaha, 2008). With the have become extinct (Primack, 2000).present rate of human consumption and the resultant India, with 2.4 per cent geographical area of the impacts on the environment, biodiversity on the earth world, harbours about 8 per cent of the world total and in the oceans will be seriously compromised. It is biodiversity (MoEF, 2009). The country is very rich in already known that land use change is expected to have biodiversity with approximately 45,000 plant and 75,000 the largest global impact on biodiversity by the year animal species (Lal, 1995). Hence, it is called as a mega-2100, followed by climate change, nitrogen deposition, diversity region. Besides, it is recognized as one of the species introduction, and atmospheric CO accumulation 2 eight Vavilovian centers of origin and diversity of crop (Sala et al., 2000). Land use change is expected to be of plants, having more than 300 wild ancestors and close particular importance in the tropics, while the effects of relatives of cultivated plants, which are still evolving climatic change are likely to be important for temperate under natural conditions (Vavilov, 1951). India is also a and polar regions; a multitude of interacting causes will vast repository of traditional knowledge associated with affect other biomes. In short, the biological diversity on biological resources (Singh and Kushwaha, 2008). India

Indian Forester, 139 (9) : 757-768, 2013http://www.indianforester.co.in

ISSN No. 0019-4816 (Print)ISSN No. 2321-094X (Online)

851

853

855

858

858

859

https://www.researchgate.net/publication/232133581_The_Origin_Variation_Immunity_and_Breeding_of_Cultivated_Plants?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==

Plant richness modelling in south Gujarat using remote sensing and geographic information system758 759The Indian Forester 2013]

ranks among the top ten species-rich nations and shows the total plant resources. The floral wealth of India high endemism. India has four global biodiversity comprises more than 47,000 species including 43 per hotspots viz., Eastern Himalaya, Indo-Burma, Western cent vascular plants. Nearly 147 genera are endemic to Ghats and Sri Lanka, and Sundaland (MoEF, 2009). The India (Nayar, 1996). The vast geographical expanse of the country has been divided into a number of biogeographic country has resulted in enormous ecological diversity, zones based on biodiversity value and environmental which is comparable with continental level diversity realms. At country level, Rodger and Panwar (1988) scales across the world. Twelve biogeographic provinces, recognized ten biogeographic zones divided into twenty- five biomes, and three bioregions are represented in the six biotic provinces. Of these ten biogeographic zones, country (Cox and Moore, 1993). Natural forests and Gujarat has four biogeographic zones, viz., Desert Thar forest plantations together cover 21.02 per cent of the (3A), Desert Kachchh (3B), Semi-arid Gujarat Rajputana geographical area in India. India, one of the 12 Vavilovian (4B) and Coasts- West coast (8A). According to centers of origin and diversification of cultivated plants, biogeographical evidences, biotic elements of Gujarat is known as the Hindustan Center of Origin of Crop Plants have major similarities with both African and palaearctic (Vavilov, 1951). About 320 species belonging to 116 realms. The varied edaphic, climatic and topographic genera and 48 families of wild relatives of crop plants are conditions and years of geological stability have resulted known to have originated in India (Arora and Nayar, in a wide range of ecosystems and habitats such as 1984).forests, grasslands, wetlands, deserts, and coastal and The conventional methods of biodiversity marine ecosystem (Shukla, 2006). Inventories of faunal assessment at landscape level focus on plant richness, diversity in India are being progressively updated and abundance and similarity index (Beals, 1985). Remote analyzed with several new discoveries. So far, nearly sensing and GIS can contribute immensely in the 91,212 of faunal species, 7.43 per cent of the world biodiversity assessment (Fuller et al., 1998; Nagendra faunal species have been recorded in the country (BSI, and Gadgil, 1999; Roy and Tomar, 2000). The vegetation 2007). The unique features of the plant diversity, among types/land use map is prime inputs for biodiversity others, include 60 monotypic families and over 6000 assessment at landscape level (Kushwaha et al., 2000; endemic species. Recent estimates indicate the presence IIRS, 2011; Nandy and Kushwaha, 2010; Kushwaha et al., of over 256 globally threatened plant species in India 2005). A methodology was developed by Roy and Tomar (MoEF, 2010). (2000) for biodiversity characterization at landscape

Biodiversity hotspots, as proposed by Myer (1988) level using remote sensing and GIS. Several studies have are the regions characterized by exceptional plant been done subsequently in India to characterize the endemism and plagued by serious level of habitat loss. biological richness (Behera et al., 2005; Kushwaha and There are 34 biodiversity global biodiversity hotspots, Roy, 2002; Roy et al., 2005; Nandy and Kushwaha, 2010). and India has four of them (Conservation international, This study reports the results of the work on plant 2012). They are (1) The Himalaya, (2) Indo-Burma, (3) The richness modelling at landscape level on 1:50,000 scale in Western Ghats and Sri Lanka, and (4) Sundaland, south Gujarat. The results have been compared with an including the Andaman and Nicobar Islands. According to earlier work at landscape level on 1:50,000 scale to see Vavilov (1951), primitive agriculture originated in the impact of scale i.e. from ecosystem to landscape level different regions of the world as a process of the on plant richness assessment (Roy et al., 2012; IIRS, domestication of local wild varieties of palatable plants. 2011). The Forest Department of Gujarat has been These nine Vavilov centers of origin of food crops, two prepared a guide line for protection and management for falls within the Indian subcontinent- the Hindu-Kush and biodiversity conservation and prioritization in the study the Indo-Malayan region. Vavilov has described this area. The botanical garden in The Dangs district, region as an independent center of origin of crops, Research plots, Sahyadri and Satpura hill ranges in the mainly rice. This makes the biodiversity of this region study area played a major role for plant richness even more important as almost a quarter of the global assessment at landscape level.population depend on the food crops, which have their

Study Areagenetic source in the region. There is an urgent need to

The south Gujarat lies between 21°14’-22°49’ N to conserve this invaluable agro-biodiversity and their wild 72°22’-74°15’E and consists of seven districts viz., genetic materials in situ.Vadodara, Bharuch, Narmada, Surat, The Dangs, Navsari

India, the second most populous country in the 2and Valsad, covering a geographical area of 31, 495 km thworld, is the 11 mega-biodiversity center of the world (Fig.1). It is bounded in the north to northeast by the

and the third in Asia, with a share of about 11 per cent of districts of Gujarat including Anand, Kheda, Godhra and

thorn forest (6B/C ) (Champion and Seth, 1968). The main I

tree species are: teak (Tectona grandis L.f.), sadad (Terminalia crenulata Roth.), shisham (Dalbergia sissoo Roxb.), khair (Acacia catechu L.f. (Willd.), timru (Diospyros melanoxylon Roxb.), mahuda (Madhuca longifolia var. latifolia (Roxb.) A. Chev.), dhavdo (Anogeissus latifolia Roxb. ex DC. Wall ex Bedd.), khakhar (Butea monosperma (Lam.) Taub.), kalam (Mitragyna parvifolia (Roxb.) Korth.), bondarao (Lagerstroemia parviflora Roxb.), billi (Aegle marmelos (L.) Correa. ex Roxb.) and moina (Lannea coromandelica (Houtt.) Merr., etc. The critically endangered species are Sterculia guttata Roxb., Toona ciliata M. Roem. and Wrightia dolichocarpa Bahadur and Bennet. The endangered species are Casearia championii Thwaites, Tamrix aphylla (L.) H. Karst., Melia dubia Cav. and Ficus nervosa B. Heyne ex Roth. The vulnerable species are Firmiana colorata (Roxb.) R. Br., Boswellia serrata Roxb. ex Colebr., Garuga pinnata Roxb., Ceriops tagal (Perr.) C.B. Rob. and Ehretia laevis Roxb (Ambasta, 1986; Nayar and Sastry, 1987; IUCN, 2008). The main faunal species found in south Gujarat forest are panther (Panthera pardus L.), sloth bear (Melursus ursinus Shaw), chinkara (Gazella bennetti Sykes) and grey hornbill (Ocyceros birostris Scopoli) (Anon., 2006). The study area is rich from biodiversity point of view (IIRS, 2011). It has three wildlife sanctuaries

Dahod, in the east by Madhya Pradesh, in the south and southeast by Maharashtra, Dadra-Nagar Haveli and Daman. To its northwest lies the Arabian Sea and Gulf of Khambhat. South Gujarat also known as Deccan Gujarat or Dakshin Gujarat is a region in Indian state of Gujarat. The region is divided into two parts, eastern and western part. The western part is almost coastal and is locally known as Kantha Vistar meaning costal region in Gujarati while the eastern part is known as Dungar Vistar, which is hilly, ranges from 100-1000m with the highest peak at Saputara in The Dangs district of south Gujarat. Surat is the largest city in this region and second largest city in Gujarat and one of the eight largest cities in India (Anon., 2007). The plains of south Gujarat are watered by Purna, Par, Damanganga, Auranga, Kolak, Ambica, Darota, Narmada, Mahi, Vishwamitri and Tapi rivers. The region shows a typical sub-humid to humid climate (Anon., 2006). The annual temperature is about 26°C and the summer and winter temperatures are 41°C and 22°C respectively. The relative humidity is 70-75 per cent. The annual rainfall varies from 1300-2200 mm (Patel, 1997).

The main forest types in the south Gujarat are: Moist teak forest (3B/C (b,c)), Southern moist mixed 1

deciduous forest (3B/C ), Southern secondary moist 2

mixed deciduous forest (3B/ S ), Mangrove forest (4B/TS ) 2 I I

and Mangrove scrub (4B/TS ), Dry teak forest (5A/C (b)), 2 1

Southern dry tropical riverain forest (5/ S ) and Desert I I

Fig. 1 : Location of the study area.

Fig. 2 : False Color Composite.

[September

https://www.researchgate.net/publication/277686294_Bray-Curtis_Ordination_An_Effective_Strategy_for_Analysis_of_Multivariate_Ecological_Data?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==

https://www.researchgate.net/publication/223697799_The_integration_of_field_survey_and_remote_sensing_for_biodiversity_assessment_A_case_study_in_the_tropical_forests_and_wetlands_of_Sango_Bay_Uganda?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==

https://www.researchgate.net/publication/225561511_Geospatial_Modelling_of_Biological_Richness_in_Sunderbans?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==

https://www.researchgate.net/publication/225561511_Geospatial_Modelling_of_Biological_Richness_in_Sunderbans?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==

https://www.researchgate.net/publication/248199908_Biodiversity_characterization_at_landscape_level_using_geospatial_modelling_technique_Bio_Conserv?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==



https://www.researchgate.net/publication/223237456_Rapid_assessment_of_biological_richness_in_a_part_of_Eastern_Himalaya_An_integrated_three-tier_approach?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==

https://www.researchgate.net/publication/270370769_Biogeography_An_Ecological_and_Evolutionary_Approach?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==


ranks among the top ten species-rich nations and shows the total plant resources. The floral wealth of India high endemism. India has four global biodiversity comprises more than 47,000 species including 43 per hotspots viz., Eastern Himalaya, Indo-Burma, Western cent vascular plants. Nearly 147 genera are endemic to Ghats and Sri Lanka, and Sundaland (MoEF, 2009). The India (Nayar, 1996). The vast geographical expanse of the country has been divided into a number of biogeographic country has resulted in enormous ecological diversity, zones based on biodiversity value and environmental which is comparable with continental level diversity realms. At country level, Rodger and Panwar (1988) scales across the world. Twelve biogeographic provinces, recognized ten biogeographic zones divided into twenty- five biomes, and three bioregions are represented in the six biotic provinces. Of these ten biogeographic zones, country (Cox and Moore, 1993). Natural forests and Gujarat has four biogeographic zones, viz., Desert Thar forest plantations together cover 21.02 per cent of the (3A), Desert Kachchh (3B), Semi-arid Gujarat Rajputana geographical area in India. India, one of the 12 Vavilovian (4B) and Coasts- West coast (8A). According to centers of origin and diversification of cultivated plants, biogeographical evidences, biotic elements of Gujarat is known as the Hindustan Center of Origin of Crop Plants have major similarities with both African and palaearctic (Vavilov, 1951). About 320 species belonging to 116 realms. The varied edaphic, climatic and topographic genera and 48 families of wild relatives of crop plants are conditions and years of geological stability have resulted known to have originated in India (Arora and Nayar, in a wide range of ecosystems and habitats such as 1984).forests, grasslands, wetlands, deserts, and coastal and The conventional methods of biodiversity marine ecosystem (Shukla, 2006). Inventories of faunal assessment at landscape level focus on plant richness, diversity in India are being progressively updated and abundance and similarity index (Beals, 1985). Remote analyzed with several new discoveries. So far, nearly sensing and GIS can contribute immensely in the 91,212 of faunal species, 7.43 per cent of the world biodiversity assessment (Fuller et al., 1998; Nagendra faunal species have been recorded in the country (BSI, and Gadgil, 1999; Roy and Tomar, 2000). The vegetation 2007). The unique features of the plant diversity, among types/land use map is prime inputs for biodiversity others, include 60 monotypic families and over 6000 assessment at landscape level (Kushwaha et al., 2000; endemic species. Recent estimates indicate the presence IIRS, 2011; Nandy and Kushwaha, 2010; Kushwaha et al., of over 256 globally threatened plant species in India 2005). A methodology was developed by Roy and Tomar (MoEF, 2010). (2000) for biodiversity characterization at landscape

Biodiversity hotspots, as proposed by Myer (1988) level using remote sensing and GIS. Several studies have are the regions characterized by exceptional plant been done subsequently in India to characterize the endemism and plagued by serious level of habitat loss. biological richness (Behera et al., 2005; Kushwaha and There are 34 biodiversity global biodiversity hotspots, Roy, 2002; Roy et al., 2005; Nandy and Kushwaha, 2010). and India has four of them (Conservation international, This study reports the results of the work on plant 2012). They are (1) The Himalaya, (2) Indo-Burma, (3) The richness modelling at landscape level on 1:50,000 scale in Western Ghats and Sri Lanka, and (4) Sundaland, south Gujarat. The results have been compared with an including the Andaman and Nicobar Islands. According to earlier work at landscape level on 1:50,000 scale to see Vavilov (1951), primitive agriculture originated in the impact of scale i.e. from ecosystem to landscape level different regions of the world as a process of the on plant richness assessment (Roy et al., 2012; IIRS, domestication of local wild varieties of palatable plants. 2011). The Forest Department of Gujarat has been These nine Vavilov centers of origin of food crops, two prepared a guide line for protection and management for falls within the Indian subcontinent- the Hindu-Kush and biodiversity conservation and prioritization in the study the Indo-Malayan region. Vavilov has described this area. The botanical garden in The Dangs district, region as an independent center of origin of crops, Research plots, Sahyadri and Satpura hill ranges in the mainly rice. This makes the biodiversity of this region study area played a major role for plant richness even more important as almost a quarter of the global assessment at landscape level.population depend on the food crops, which have their

Study Areagenetic source in the region. There is an urgent need to

The south Gujarat lies between 21°14’-22°49’ N to conserve this invaluable agro-biodiversity and their wild 72°22’-74°15’E and consists of seven districts viz., genetic materials in situ.Vadodara, Bharuch, Narmada, Surat, The Dangs, Navsari

India, the second most populous country in the 2and Valsad, covering a geographical area of 31, 495 km thworld, is the 11 mega-biodiversity center of the world (Fig.1). It is bounded in the north to northeast by the

and the third in Asia, with a share of about 11 per cent of districts of Gujarat including Anand, Kheda, Godhra and

thorn forest (6B/C ) (Champion and Seth, 1968). The main I

tree species are: teak (Tectona grandis L.f.), sadad (Terminalia crenulata Roth.), shisham (Dalbergia sissoo Roxb.), khair (Acacia catechu L.f. (Willd.), timru (Diospyros melanoxylon Roxb.), mahuda (Madhuca longifolia var. latifolia (Roxb.) A. Chev.), dhavdo (Anogeissus latifolia Roxb. ex DC. Wall ex Bedd.), khakhar (Butea monosperma (Lam.) Taub.), kalam (Mitragyna parvifolia (Roxb.) Korth.), bondarao (Lagerstroemia parviflora Roxb.), billi (Aegle marmelos (L.) Correa. ex Roxb.) and moina (Lannea coromandelica (Houtt.) Merr., etc. The critically endangered species are Sterculia guttata Roxb., Toona ciliata M. Roem. and Wrightia dolichocarpa Bahadur and Bennet. The endangered species are Casearia championii Thwaites, Tamrix aphylla (L.) H. Karst., Melia dubia Cav. and Ficus nervosa B. Heyne ex Roth. The vulnerable species are Firmiana colorata (Roxb.) R. Br., Boswellia serrata Roxb. ex Colebr., Garuga pinnata Roxb., Ceriops tagal (Perr.) C.B. Rob. and Ehretia laevis Roxb (Ambasta, 1986; Nayar and Sastry, 1987; IUCN, 2008). The main faunal species found in south Gujarat forest are panther (Panthera pardus L.), sloth bear (Melursus ursinus Shaw), chinkara (Gazella bennetti Sykes) and grey hornbill (Ocyceros birostris Scopoli) (Anon., 2006). The study area is rich from biodiversity point of view (IIRS, 2011). It has three wildlife sanctuaries

Dahod, in the east by Madhya Pradesh, in the south and southeast by Maharashtra, Dadra-Nagar Haveli and Daman. To its northwest lies the Arabian Sea and Gulf of Khambhat. South Gujarat also known as Deccan Gujarat or Dakshin Gujarat is a region in Indian state of Gujarat. The region is divided into two parts, eastern and western part. The western part is almost coastal and is locally known as Kantha Vistar meaning costal region in Gujarati while the eastern part is known as Dungar Vistar, which is hilly, ranges from 100-1000m with the highest peak at Saputara in The Dangs district of south Gujarat. Surat is the largest city in this region and second largest city in Gujarat and one of the eight largest cities in India (Anon., 2007). The plains of south Gujarat are watered by Purna, Par, Damanganga, Auranga, Kolak, Ambica, Darota, Narmada, Mahi, Vishwamitri and Tapi rivers. The region shows a typical sub-humid to humid climate (Anon., 2006). The annual temperature is about 26°C and the summer and winter temperatures are 41°C and 22°C respectively. The relative humidity is 70-75 per cent. The annual rainfall varies from 1300-2200 mm (Patel, 1997).

The main forest types in the south Gujarat are: Moist teak forest (3B/C (b,c)), Southern moist mixed 1

deciduous forest (3B/C ), Southern secondary moist 2

mixed deciduous forest (3B/ S ), Mangrove forest (4B/TS ) 2 I I

and Mangrove scrub (4B/TS ), Dry teak forest (5A/C (b)), 2 1

Southern dry tropical riverain forest (5/ S ) and Desert I I

Fig. 1 : Location of the study area.

Fig. 2 : False Color Composite.

[September

760 The Indian Forester

(WLS) - Jambughoda WLS in Vadodara district, images were then geometrically corrected using Landsat Shoolpaneshwar WLS in Narmada district, and Purna TM (resolution-30m) as reference map. A global WLS in The Dangs district, and Vansda National Park in positioning system (GPS) was used for collection of Navsari district. different GPS location points for each vegetation

types/land use classes. WGS 84 datum, Lambert Material and MethodsConformal Conic (LCC) projection and nearest neighbour

Indian Remote Sensing Satellite IRS P6 LISS-III resampling method were used for data processing. The

(Resourcesat-1) satellite data acquired in dry (April-May) per centage fraction of the correctly classified points out

and wet seasons (October) 2006 were used for the of total points for each category was taken as measure of

present study (Fig. 1 and Table 1). Figure. 2 shows the classification accuracy (Congalton, 1991). The accuracy

standard False Colour Composite (FCC) was generated was assessed using stratified random sampling design.

from Resourcesat-1 spectral bands B1 (green), B2 (red) The overall accuracy of vegetation types/land use map

and B3 (NIR). The complete methodologies were shown was 87.56 per cent (Table 3).

in Fig. 3. The vegetation types/land use maps were The satellite based vegetation types were prepared through on-screen visual interpretation

compared with equivalent Champion and Seth’s (1968) method (Fig. 4). Table 2 shows area under different classification (Table 4). In-house developed Spatial vegetation types/land use categories in south Gujarat. Landscape Modelling (SPLAM) software was used for Survey of India (SOI) topographic maps on 1:250,000 landscape analysis. SPLAM is semi-expert geospatial scale were used to extract the road, rail and settlement system software developed in Windows environment. for communication network generation and biotic SPLAM was developed and customized in the ArcInfo disturbance. Darkest pixel subtraction technique was environment using Arc Macro Language (AML) and used for haze removal from the satellite imagery libraries of grid module. The SPLAM ver. 1.0, a new (Lillesand and Kiefer, 2004). The Resourcesat-1 satellite

Table 1: Satellite imagery used in the present study.

S. No. Satellite Sensor Path-row Date of pass 1. Resourcesat-1 LISS-III 93-56 19 Oct. and 04 May 2006 2. -do- -do- 93-57 19 Oct. and 10 Apr. 2006

3 -do- -do- 94-56 24 Oct. and 09 May 2006 4. -do- -do- 94-57 24 Oct. and 09 May 2006 5. -do- -do- 94-58 24 Oct. and 15 Apr. 2006 6. -do- -do- 95-57 10 Oct. and 20 Apr. 2006

Table 2: Area under different vegetation types/land use categories.

S.No. Vegetation type/land use Area (km ) Area (%) 1. Teak mixed moist deciduous forest 243.01 0.77 2. Teak mixed dry deciduous forest 4718.54 14.98

3. Mangrove forest 32.90 0.10 4. Mangrove scrub 75.40 0.24 5. Riverain forest 0.16 0.0004 6. Ravine thorn forest 144.29 0.46 7. Forest plantation 19.53 0.06 8. Degraded forest 0.36 0.0011 9. Scrub 912.38 2.90 10. Prosopis juliflora scrub 257.85 0.82 11. Grassland 19.10 0.06 12. Orchard 4.56 0.01 13. Agriculture 20452.35 64.94 14. Barren land 77.03 0.24 15. Mine 72.49 0.23 16. Mudflat 109.30 0.35 17. Salt-affected land 605.43 1.92 18. Salt pan 169.50 0.54 19. Waterbody 3021.55 9.59 20. Wetland 33.09 0.11 21. Settlement 526.19 1.67 Total 31,495.00 100.00 Table 3: Accuracy assessment of vegetation types/land use map.

Vegetation

type/land use

TMMD TMDD M MS RV RTH FP DF SC P GR OR AG BL M MF SL SP WB WL S Total

TMMD

8

1

-

-

-

-

-

-

-

1

-

-

- - - - - - - - - 10

TMDD

1

19

-

-

-

-

-

-

-

-

-

-

- - - - - - - - - 20

M

-

-

6

1

-

-

-

-

-

-

-

-

- - - - - - - - - 7MS

-

-

1

5

-

-

-

-

-

-

-

-

- - - - - - - - - 6

RV

-

-

-

-

3

-

-

-

-

-

-

-

- - - - - - - - - 3

RTH

-

-

-

-

-

6

-

-

-

-

-

-

- - - - - - - - - 6FP

-

-

-

-

-

-

7

-

-

-

-

-

- - - - - - - - - 7

DF

-

-

-

-

-

-

-

4

1

-

-

-

- - - - - - - - - 5

SC

-

-

-

-

-

-

-

-

10

-

-

-

1 - - - - - - - - 11P

-

-

-

-

-

-

-

-

-

8

-

-

- 1 - - - - - - - 9

GR

-

-

-

-

-

-

-

-

-

-

4

-

- - - - - - - - - 4OR

-

-

-

-

-

-

-

1

-

-

1

5

- - - - - - - - - 7

AG

-

-

-

-

-

-

-

-

-

-

1

-

3 - - - - - - - - 4

BL - - - - - - - - - - - - - 4 - 1 - - - - - 5M - - - - - - - - - - - - - - 2 - - - - - - 2

MF - - - - - - - - - - - - - - - 4 - - - - - 4

SL - - - - - - - - - - - - - - - - 4 1 - - - 5SP - - - - - - - - - - - - - - - - 1 3 - - - 4

WB - - - - - - - - - - - - - - - - - - 3 1 - 4

WL - - - - - - - - - - - - - - - - - - 1 4 - 5S - - - - - - - - - - - - - - - - - - - - 3 3

Total 9 20 7 6 3 6 7 5 11 9 6 5 4 5 2 5 5 4 4 5 3 131

Overall accuracy = 87.78 per cent, K = 0.85 TMMD- Teak mixed moist deciduous forest, TMDD- Teak mixed dry deciduous forest, M- Mangrove forest, MS- hat

Mangrove scrub, RV- Riverain forest, RTH- Ravine thorn forest, FP- Forest plantation, DF- Degraded forest, SC- Scrub, P- Prosopis juliflora scrub, GR- Grassland, OR- Orchard, AG- Agriculture, BL- Barren land, M- Mine, MF- Mud flat, SL- Salt-affected land, SP- Salt pan, WB- Waterbody, WL- Wetland, S- Settlement.

Fig. 3: Methodology.

[September Plant richness modelling in south Gujarat using remote sensing and geographic information system 7612013]

https://www.researchgate.net/publication/222467662_A_Review_of_Assessing_The_Accuracy_of_Classifications_of_Remotely_Sensed_Data?el=1_x_8&enrichId=rgreq-5f4ae073-53dd-494f-b183-ac37c3ea2a42&enrichSource=Y292ZXJQYWdlOzI3MDU3NzkyNztBUzoxODQxMzE1NjA0MTkzMjhAMTQyMDkxMTc5MzE0Nw==


(WLS) - Jambughoda WLS in Vadodara district, images were then geometrically corrected using Landsat Shoolpaneshwar WLS in Narmada district, and Purna TM (resolution-30m) as reference map. A global WLS in The Dangs district, and Vansda National Park in positioning system (GPS) was used for collection of Navsari district. different GPS location points for each vegetation

types/land use classes. WGS 84 datum, Lambert Material and MethodsConformal Conic (LCC) projection and nearest neighbour

Indian Remote Sensing Satellite IRS P6 LISS-III resampling method were used for data processing. The

(Resourcesat-1) satellite data acquired in dry (April-May) per centage fraction of the correctly classified points out

and wet seasons (October) 2006 were used for the of total points for each category was taken as measure of

present study (Fig. 1 and Table 1). Figure. 2 shows the classification accuracy (Congalton, 1991). The accuracy

standard False Colour Composite (FCC) was generated was assessed using stratified random sampling design.

from Resourcesat-1 spectral bands B1 (green), B2 (red) The overall accuracy of vegetation types/land use map

and B3 (NIR). The complete methodologies were shown was 87.56 per cent (Table 3).

in Fig. 3. The vegetation types/land use maps were The satellite based vegetation types were prepared through on-screen visual interpretation

compared with equivalent Champion and Seth’s (1968) method (Fig. 4). Table 2 shows area under different classification (Table 4). In-house developed Spatial vegetation types/land use categories in south Gujarat. Landscape Modelling (SPLAM) software was used for Survey of India (SOI) topographic maps on 1:250,000 landscape analysis. SPLAM is semi-expert geospatial scale were used to extract the road, rail and settlement system software developed in Windows environment. for communication network generation and biotic SPLAM was developed and customized in the ArcInfo disturbance. Darkest pixel subtraction technique was environment using Arc Macro Language (AML) and used for haze removal from the satellite imagery libraries of grid module. The SPLAM ver. 1.0, a new (Lillesand and Kiefer, 2004). The Resourcesat-1 satellite

Table 1: Satellite imagery used in the present study.

S. No. Satellite Sensor Path-row Date of pass 1. Resourcesat-1 LISS-III 93-56 19 Oct. and 04 May 2006 2. -do- -do- 93-57 19 Oct. and 10 Apr. 2006

3 -do- -do- 94-56 24 Oct. and 09 May 2006 4. -do- -do- 94-57 24 Oct. and 09 May 2006 5. -do- -do- 94-58 24 Oct. and 15 Apr. 2006 6. -do- -do- 95-57 10 Oct. and 20 Apr. 2006

Table 2: Area under different vegetation types/land use categories.

S.No. Vegetation type/land use Area (km ) Area (%) 1. Teak mixed moist deciduous forest 243.01 0.77 2. Teak mixed dry deciduous forest 4718.54 14.98

3. Mangrove forest 32.90 0.10 4. Mangrove scrub 75.40 0.24 5. Riverain forest 0.16 0.0004 6. Ravine thorn forest 144.29 0.46 7. Forest plantation 19.53 0.06 8. Degraded forest 0.36 0.0011 9. Scrub 912.38 2.90 10. Prosopis juliflora scrub 257.85 0.82 11. Grassland 19.10 0.06 12. Orchard 4.56 0.01 13. Agriculture 20452.35 64.94 14. Barren land 77.03 0.24 15. Mine 72.49 0.23 16. Mudflat 109.30 0.35 17. Salt-affected land 605.43 1.92 18. Salt pan 169.50 0.54 19. Waterbody 3021.55 9.59 20. Wetland 33.09 0.11 21. Settlement 526.19 1.67 Total 31,495.00 100.00 Table 3: Accuracy assessment of vegetation types/land use map.

Vegetation

type/land use

TMMD TMDD M MS RV RTH FP DF SC P GR OR AG BL M MF SL SP WB WL S Total

TMMD

8

1

-

-

-

-

-

-

-

1

-

-

- - - - - - - - - 10

TMDD

1

19

-

-

-

-

-

-

-

-

-

-

- - - - - - - - - 20

M

-

-

6

1

-

-

-

-

-

-

-

-

- - - - - - - - - 7MS

-

-

1

5

-

-

-

-

-

-

-

-

- - - - - - - - - 6

RV

-

-

-

-

3

-

-

-

-

-

-

-

- - - - - - - - - 3

RTH

-

-

-

-

-

6

-

-

-

-

-

-

- - - - - - - - - 6FP

-

-

-

-

-

-

7

-

-

-

-

-

- - - - - - - - - 7

DF

-

-

-

-

-

-

-

4

1

-

-

-

- - - - - - - - - 5

SC

-

-

-

-

-

-

-

-

10

-

-

-

1 - - - - - - - - 11P

-

-

-

-

-

-

-

-

-

8

-

-

- 1 - - - - - - - 9

GR

-

-

-

-

-

-

-

-

-

-

4

-

- - - - - - - - - 4OR

-

-

-

-

-

-

-

1

-

-

1

5

- - - - - - - - - 7

AG

-

-

-

-

-

-

-

-

-

-

1

-

3 - - - - - - - - 4

BL - - - - - - - - - - - - - 4 - 1 - - - - - 5M - - - - - - - - - - - - - - 2 - - - - - - 2

MF - - - - - - - - - - - - - - - 4 - - - - - 4

SL - - - - - - - - - - - - - - - - 4 1 - - - 5SP - - - - - - - - - - - - - - - - 1 3 - - - 4

WB - - - - - - - - - - - - - - - - - - 3 1 - 4

WL - - - - - - - - - - - - - - - - - - 1 4 - 5S - - - - - - - - - - - - - - - - - - - - 3 3

Total 9 20 7 6 3 6 7 5 11 9 6 5 4 5 2 5 5 4 4 5 3 131

Overall accuracy = 87.78 per cent, K = 0.85 TMMD- Teak mixed moist deciduous forest, TMDD- Teak mixed dry deciduous forest, M- Mangrove forest, MS- hat

Mangrove scrub, RV- Riverain forest, RTH- Ravine thorn forest, FP- Forest plantation, DF- Degraded forest, SC- Scrub, P- Prosopis juliflora scrub, GR- Grassland, OR- Orchard, AG- Agriculture, BL- Barren land, M- Mine, MF- Mud flat, SL- Salt-affected land, SP- Salt pan, WB- Waterbody, WL- Wetland, S- Settlement.

Fig. 3: Methodology.



toolbar, ArcObjects was developed in ArcMap for output layer with patch numbers was thus derived. A landscape analysis (Jeganathan and Narula, 2006). The look-up-table (LUT) was generated to keep the vegetation types/land use map was used as input to normalized data of the patches per cell in the range of 0 SPLAM. Plant richness indicative of the plant richness to 10. The Shannon-Weaver diversity index of plant was calculated as a function of species richness, diversity (Shannon and Weaver, 1949) was calculated for biodiversity value, ecosystem uniqueness, terrain species richness assessment (Table 5). The ecosystem complexity and biotic disturbance. uniqueness was derived from the detailed vegetation

analysis. The Total Importance Value (TIV) was calculated A user grid cell of 500m x 500m was convolved considering ten important primary economic uses of with the spatial data layer for deriving the number of plant species such as fodder, medicinal, edible, timber, forest patches within the grid cell. This was repeated by charcoal, dye, fuel, oil, tannins and others uses such as moving the grid cell through entire spatial layer. An

Fig. 5: Plant richness map.Fig. 4: Vegetation type/land use map.

shade and hedges (Belal and Springuel, 1996): uniqueness, disturbance index, TIV and terrain complexity were used to derive plant richness modelling and landscape assessment for the study area. The equations were developed for the plant richness modelling:

where, TIV (%) is total importance value, U is importance value for each particular use (e.g., timber, fuelwood, food etc.). The highest TIV vales were found in teak mixed dry and moist deciduous forest followed by where TC = Terrain complexity, EU = Ecosystem mangrove forest, riverain forest and ravine thorn forest uniqueness, SR = Species richness, BV = Biodiversity respectively. The species richness, ecosystem value, DI = Disturbance index, Wt = Weights

× 100 (1) (U + U + U …………Un)1 2 3

(Number of uses maximum uses) TIV (%) =


Plant richness modelling in south Gujarat using remote sensing and geographic information system 7652013]

Table 4: Vegetation types in south Gujarat compared to Champion and Seth (1968).

Forest code

Forest types (Champion and Seth, 1968)

Satellite-based vegetation types

Dominant species

5A/C1 Dry teak bearing forest

Teak mixed dry deciduous forest

Tectona grandis L.f., Butea monosperma (Lam.) Taub ., Wrightia tinctoria R.Br., Terminalia elliptica Willd., Diospyros melanoxylonRoxb.

3B/C1 Moist teak bearing forest

Teak mixed moist deciduous forest

Tectona grandis L.f., Terminalia bellirica (Gaertn.) Roxb., Ficus racemosa L., Miliusa tomentosa (Roxb.) Fenet and Gang., Wrightia tinctoria R.Br., Mallotus philippensis (Lam.) Müll.Arg. , Mangiferaindica

L.

4B/TS2

Mangrove forest

Mangrove forest

Avicennia marina

(Forssk.) Vierh. Avicennia officinalis

L., Avicennia marina subsp.

marina, Rhizophora mucron ata

Lam., Ceriops tagal

(Perr.) C.B.Rob., Bruguiera cylindrica (L.) Blume, B. gymnorhiza (L.) Lam., Sonneratia apetala Buch.-Ham.

4B/TS1

Mangrove scrub

Mangrove scrub

Avicennia marina

(Forssk.) Vierh. Avicennia officinalis

L. Ceriops tagal

(Perr.) C.B. Ro b.

5/1S1

Dry tropical riverain forest

Riverain forest

Pongamia pinnata

(L.) Pierre, Syzygium salicifolium

(Wight) J.Graham, Wrightia tinctoria

R. Br., Alangium salviifolium

(L.f.)

Wangerin, Mitragyna parvifolia

(Roxb.) Korth. 6B/C2

Ravine thorn forest

Ravine thorn forest

Acacia senegal

(L.) Willd., Acacia tortilis

(Forssk.) Hayne, Streblus asper

Lour., Azadirachta indica

A.Juss., Holoptelea integrifolia

Planch. 5/DS4

Dry grassland

Grassland

Aristida adscensionis

L., Apluda mutica

L., Cymbopogon martini

(Roxb.) W.Watson

Fig. 6: District-wise plant richness.

In this view of the extensive studies conducted in forest ecosystems in different parts of the world, the plot sizes of 0.1 ha sample plot was considered. The sample plot of 0.1 ha was randomly distributed across each stratum. The sample plot is reached on ground based GPS locations. For sampling of shrub species one plot of 5 m x 5 m in center were laid. For herbaceous plants, four plots of 1 m x 1m in opposite corners were laid. At each sample plots, the circumference at breast height (cbh) of all tree species was recorded and marked with chalk to avoid

duplication. The individuals with cbh ³ 30cm were considered as tree, with > 17 to < 30cm cbh as saplings and <17cm as seedlings. In the case of shrub, the cbh need to be measured about 30cm above ground. Total number of seedling of various species was counted and average girth of each species was recorded. For shrubs, contributed by teak mixed dry deciduous forest 14.98 per total number of tillers for each species was counted and cent. Riverain forest occupied very less area 0.0004 per for each species an average circumference at ground cent. Non-forest classes occupied the rest of the area. height level was estimated. The plot size was derived Most of the areas were covered by agriculture 64.94 per from species-area curve method (Mishra, 1968; Kershaw, cent and waterbody 9.59 per cent respectively. Salt-

2 21975). The required number of sample plots for each affected land 605.43 km and settlement 526.19 km vegetation types was calculated by plotting the were found to be major land cover/land use area in the cumulative number of species against number of plots in south Gujarat. a pilot ground truthing. The sample plots of 31.62m ×

The accuracy assessment of the vegetation 31.62m, 5m x 5m and 1m x 1m size were used to collect

types/land use map was assessed. Table 3 gives details of data on trees, shrubs and herbs respectively. The species the accuracy estimates of the vegetation types/land use were recorded from each sample plot and categorized for map prepared using on-screen visual interpretation endemism, vulnerability, rarity, economic importance method. The overall accuracy was found to be 87.78 per and ecological uniqueness based on BSI (2007) and IUCN cent with k coefficient 0.85. The satellite-based (2008) database. Different vegetation types categories hat

vegetation types were compared to the Champion and were assigned weights depending upon above species attributes. The above-mentioned approach was adopted Seth classification (1968). The nine forest classes were keeping in view the capability of remote sensing made including Tropical moist deciduous forest (3B/C ), 1

technology to classify the vegetation types, utility of Tropical dry deciduous forest (5A/C ), Tropical thorn 1

landscape variables in plant diversity characterization forest (6B) and Littoral and Swamp forest (4A/L ) as 1

and the importance of field-based assessment of described by Champion and Seth’s classification (1968). biodiversity. The dominant species in each vegetation type is shown in Results and Discussion Table 4.

The classification and mapping of the vegetation The location of each field site was determined types/land uses were done on 1:50,000 scale using on- using GARMIN 12 GPS receiver. The stratified random screen visual interpretation method. This method sample plot with 0.001 per cent intensity was carried out facilitated identification and mapping of teak mixed dry for analyzing the vegetation of all vegetation types. A and moist deciduous forest, mangrove forest, mangrove 31.62m × 31.62m plot size was worked out to be optimal scrub, riverain forest, forest plantation, ravine thorn

by species-area-curve method for tree sampling. A total forest, degraded forest, Prosopis juliflora scrub (forest

of 157 tree sample plots, 157 shrub plots of 5m × 5m size classes) and scrub, grassland, orchard, agriculture,

and 628 herb plots of 1m × 1m size were laid in different barren land, mine, mud-flat, salt-affected land, salt-pan,

vegetation types. The shrub and herb plots were laid waterbody, wetland and settlement (non-forest classes) inside the tree plots. At each sample plot the cbh of all (Fig. 4). Table 2 shows the area covered by different tree species was recorded. The individuals with cbh ³ 30 vegetation types/land use classes in the study area. The cm were considered as tree and the saplings were forest classes together account for 17.44 per cent of total considered as shrub and seedlings as herb. For shrubs, geographical area. Out of which maximum area was

the total numbers of tillers of each species were counted forest, P. julflora scrub, mangrove scrub, degraded forest and forest plantation respectively.and the circumference at ground level was recorded. For

herbs, only numbers of individuals and tillers were The high plant richness was found 9.76 per cent recorded. The ecosystem uniqueness, species richness, followed by medium 6.43 per cent, low 0.86 per cent and biodiversity value and terrain complexity were worked very high 0.38 per cent in the study area. The vegetation out from sample plot data. Species richness for each type-wise plant richness assessments were also

calculated (Table 6). It was observed that 55.97 per cent vegetation type was calculated using Shannon-Weaver of forests area had under high plant richness, rest of the diversity Index (Shannon and Weaver, 1949).area falls under low, medium and very high categories. If The lowest diversity was observed in P. juliflora proper conservation and management were not taken, scrub. It was observed that teak mixed dry and moist the plant richness area under this category may increase, deciduous forest have highest ecosystem uniqueness as which may lead to lose the richness areas. High plant well as highest biodiversity value and species richness richness areas were observed along the Sahyadri and comparatively to others vegetation types. Species Satpura hill ranges. The study also revealed that 62.95 richness map was generated by extrapolating these per cent of teak mixed dry deciduous forest and 42.67 per values in vegetation types/land use map. High degree of cent teak mixed moist deciduous forest had high level of richness observed in teak mixed dry and moist deciduous plant richness. Medium level of plant richness was forest followed by mangrove forest, mangrove scrub, observed in teak mixed moist deciduous forest (50.31 %). riverain forest, ravine thorn forest and forest plantation Low levels of plant richness were observed in forest showed medium level of richness and degraded forest, plantation 72.16 per cent followed by degraded forest scrub and P. juliflora scrub had lowest species richness. (100 %) and P. juliflora scrub (100 %). The very high plant Biological value in terms of economic and ecological uses richness was observed in teak mixed dry (7.01 %) and of the species was adjusted as one of the most important moist (2.18 %) deciduous forest. Low to medium plant parameters in determining the plant richness. The TIV richness were observed in mangrove forest, mangrove calculated for each vegetation type, based on different scrub, r iverain forest , ravine thorn forest , uses, were used to generate the biodiversity value map. degraded forest and P. juliflora scrub respectively. The biodiversity value map shows different levels ranging Figure 5 shows overall plant richness status in the study from low to high. It was observed that teak mixed dry and area.moist deciduous forest had the highest biodiversity value

followed by mangrove forest, riverain forest, ravine thorn The low plant richness was observed in the forest

764 The Indian Forester [September

areas of Bharuch district followed by Vadodara and Navsari districts. It was observed that 76.75 per cent of forest areas of Bharuch district had low plant richness, whereas in Vadodara and Navsari districts had low plant richness covered 57.13 per cent and 50.68 per cent of the forest areas respectively. High plant richness was observed in 51.21 per cent forest areas of The Dangs districts followed by 44.82 per cent and 43.01 per cent forest area of Surat and Narmada districts respectively. Figure. 6 shows district-wise plant richness status in the study area.

Conclusions

The approach presented here, has proved that the study is useful for the detection of species-richness and species-poor areas at a fine grain over large areas. It allows for a comparison of landscape plant richness with respect to ecosystem, and provides a potentially valuable further study. The approach of using a geospatial basis for deriving national nature conservation approach for plant richness modelling for south Gujarat strategies. Our analysis leads us to propose that more may be applied to any landscape as long as the required emphasis should be placed on the implementation of hot basic data for plant and remote sensing data are belts in conservation and management planning. The available. It showed that the numbers of factors that present study also demonstrate the complexity of linear influence plant richness at landscape scale are also likely arrangements of increased plant richness at the to increase. In order to deal with this increasing factor landscape level, which in turn are the result of the complexity, it is proposed that plant richness should be different spatial effects of ecologically rich areas such as modeled using sets of appropriate remote sensing mountains and high land use diversity along hill ranges. techniques that reflect the underlying spatial However, agricultural and urban land that can undergo characteristics of the region concerned.rapid temporal and spatial land use changes still needs


Acknowledgements

This study was supported by joint funding from the Departments of Space and Biotechnology, Government of India. The authors are thankful to Dr. M.L. Sharma, Ex-Principal Chief Conservator of Forest, Govt. of Gujarat, Forest Department, Gandhinagar for collaborating in the project. We also thank Chief Conservator of Forest (Working Plans), Vadodara for coordinating the field investigations. Thanks are also to Dr. M. Daniel, Dr. P.S. Nagar and Mr. Dipak Tadvi, Department of Botany, M.S. University of Baroda, Vadodara, Gujarat for ground truthing, data collection, and species identification.

Table 6: Vegetation type-wise plant richness.

Vegetation types Low Medium High Very high Total (km2)

Teak mixed moist deciduous forest 0.00 122.26 103.70 17.05 243.01 Teak mixed dry deciduous forest 0.00 1645.38 2970.33 102.83 4718.54

Mangrove forest 0.00 32.90 0.00 0.00 32.90 Mangrove scrub 0.00 75.40 0.00 0.00 75.40 Riverain forest 0.00 0.16 0.00 0.00 0.16 Ravine thorn forest 0.00 144.29 0.00 0.00 144.29 Forest plantation 14.09 5.44 0.00 0.00 19.53 Degraded forest 0.36 0.00 0.00 0.00 0.36 Prosopis juliflora scrub 257.85 0.00 0.00 0.00 257.85 Total 272.30 2025.83 3074.03 119.88 5492.05

Table 5: Shannon-Weaver diversity index in different vegetation types.

Variable Shannon-Weaver index

TMDD

Tree

3.09

Shrub

1.31

Herb

3.35TMMD

3.41

1.38 2.61M 1.38 1.04 1.78MS 0.41 1.04 1.78RV 2.37 1.79 1.61

RTH

DF

PFPSC

2.01 1.38 1.552.34 2.11 1.90.32 1.08 2.812.12 1.28 1.712.21 3.16 3.43

TMDD- Teak mixed dry deciduous forest, TMMD- Teak mixed moist deciduous forest, M- Mangrove forest, MS- Mangrove scrub, RV- Riverain forest, RTH- Ravine thorn forest, DF- Degraded forest, P- Prosopis juliflora scrub, FP- Forest plantation, SC- Scrub.

lqnwj laosnu rFkk HkkSxksfyd lwpuk i¼fr ls nf{k.kh xqtjkr esa ikni lef¼ ekMfyaxth-Mh- HkV~V] ,l-ih-,l- dq'kokgk] ,l- uanh] fdju cjxkyh] Mh- VkM~oh] ih-,l- ukxj rFkk ,e- MSfu;y

lkjka'kizys[k esa] nf{k.kh xqtjkr esa ikni lef¼ dk vkdyu djus ds fy, Hkw&LFkkfud i¼fr ds ckjs esa crk;k x;k gS] ftlds fy, f}Lrjh;

i¼fr dk mi;ksx fd;k x;k] ;Fkk% ouLifr fdLeksa @Hkw mi;kstuksa dk ekufp=khdj.k] Hkw&n'; fo'ys"k.k ds fy, lsVsykbZV best (fjlkslsZV &I) rFkk lef¼&ekMfyax ds fy, 1%50]000 LdsyA v/;;u esa ukS ouLifr fdLeksa ds ckjs esa crk;k x;k gS vFkkZr~ Vhd fefJr 'kq"d vkSj ue i.kZikrh ou] dNkjh ou] dNkjh >kfM+;k¡] un rVh; ou] unrVh; daVhys ou] ou jksif.k;ka] fuEuhdr ou rFkk izkWfLil T;wyhÝyksjk ds ouA lcls foLrr {ks=k Vhd fefJr 'kq"d i.kZikrh ouksa dk gS tks 14-98 izfr'kr {ks=k esa iQSys gq;s gSA lexz 'kq¼rk 87-78 izfr'kr ikbZ xbZA ,l-ih-,y-,-,e- lkÝVos;j ds ikni lef¼ ekufp=k rS;kj fd;k x;k] ftlls ikni lef¼ dk rhu Lrjksa ij irk pykA ouLifr fdLeksa ds vuqlkj ikni lef¼ vkdyu dh x.kuk dh xbZA ;g ik;k x;k fd 55-97 izfr'kr ou {ks=k esa mPp ikni ckgqY; gSA tcfd 'ks"k Hkkx esa U;wu] eè;e vkSj cgqr mPp Jsf.k;ka gSA ftyksa ds vuqlkj ikni lef¼ dh x.kuk Hkh dh xbZA

References

Ambasta, S.P. (1986). The Useful Plants of India, Publication and Information Division, CSIR, New Delhi.

Anon., (2006). Working Plan for Rajpipla East and Rajpipla West Divisions, Vol. II., Research and Working Plan Division, State Forest Department, Rajpipla, Narmada district, Gujarat.

Anon., (2007). Gujarat: A Panorama of the Heritage of Gujarat, Gujarat Vishvakosh Trust, Usmanpura, Ahmedabad, Gujarat, India.

Arora, R.K. and Nayar, E.R. (1984). Wild relatives of crop plants in India, NBPGR Science Monograph, pp. 7-90.

Beals, E.W. (1985). Bray-Curtis ordination: an effective strategy for analysis of multivariate ecological data, Advances in Ecology, 14: 1-55.

Behera, M.D., Kushwaha, S.P.S. and Roy, P.S. (2005). Rapid assessment of biological richness in a part of eastern Himalaya: an integrated tree-tier approach, J. Forest Eco. Management, 207(3): 363-384.

Belal, A.E., and Springuel, I. (1996). Economic value of plant diversity in arid environments, Nature and Resources, 22(1): 33-39.

BSI, (2007). Bulletin of the Botanical Survey of India, 49(1-4): 246, Botanical Survey of India, Kolkata, India.

Champion, H.G., and Seth, S.K. (1968). A Revised Survey of the Forest Types of India, Government of India Publication, New Delhi, India, pp. 404.

Chapin-III, Stuart, F., Zavaleta, E.F., Eviner, V.T., Naylor, R.L., Vitousek, P.M., Reynolds, H.L., Hooper, D.U., Lavorel, S., Sala, O.E., Hobbie, S.E., Mack, M.C., and Diaaz, S. (2000). Consequences of changing biodiversity, Nature, 405: 234-242.

Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data, Remote Sensing of Environment, 37(1): 35-46.

Conservation International, (2012). http://www.biodiversityhotspots.org, (Accessed on 07 February, 2012).thCox, C.B. and Moore, P.D. (1993). Biogeography: An ecological and evolutionary approach, 5 Eds. Blackwell Scientific Publications, London,

U.K.

Fuller, R.M., Groom, G.B., Mulish, S., Pullet, P., Pomeroy, D., Katende, A., Bailey, R., and Ogutu-Ohwayo, R. (1998). The integration of field survey and remote sensing for biodiversity assessment: a case study in the tropical forests and wetlands of Sango Bay, Uganda, Biological Conservation, 86: 379-391.

IIRS, 2011. Biodiversity Characterization at Landscape Level in North-West India Using Satellite Remote Sensing and Geographic Information System, Forestry and Ecology Department, Indian Institute of Remote Sensing, Dehradun, pp. 296.

IUCN, (2008). An Overview of the IUCN Red List, Published on-line at http://www.iucnredlist.org/info/programme (Accessed 30 June, 2008).

Jeganathan, C. and Narula, P. (2006). User Guide for Spatial Landscape Modelling Developed in Arc-Map using Arc Objectives and Visual Basic 6.0, Indian Institute of Remote Sensing, Dehradun.

Kershaw, K.A. (1975). Quantitative and Dynamic Plant Ecology, Elsevier Press, New York.

Kushwaha, S.P.S. and Roy, P.S. (2002). Geospatial technology for wildlife habitat evaluation, Tropical Ecology, 43(1): 137-150.

Kushwaha, S.P.S., Behera, M.D., and Roy, P.S. (2000). Biodiversity characterization at landscape level using remote sensing and GIS. Proceeding Symposium on Biodiversity in India, Darjeeling, pp. 320-325.

Kushwaha, S.P.S., Padmanaban, P., Kumar, D., and Roy, P.S. (2005). Geospatial modeling of biological richness in Barsey Rhododendron Sanctuary in Sikkim Himalaya, Geocarto International, 20(2): 63-68.

Lal, J.B. (1995). Forest diversity vs. biodiversity conservation, MFP, News, 5(3): 7.

Leakey R., and Lewin, R. (1996). The Sixth Extinction: Biodiversity and its Survival, London, Widenfeld and Nicolson.thLillesand, T.M. and Kiefer, R.W. (2004). Remote Sensing and Image Interpretation, 4 Edition John Wiley and Sons, New York, pp. 756.

Mishra, R. (1968). Ecology Work Book, Oxford and IBH, Calcutta, pp. 244.

MoEF, (2009). India Fourth National Report to the Convention on Biological Diversity, Ministry of Environment and Forests, Government of India Paryavaran Bhawan, CGO Complex, New Delhi, India.

MoEF, (2010). India and the convention on biological diversity, COP-10, Nagoya, Japan.

Myer, N. (1988). Threatened biotas: Hotspots in tropical forests, The Environmentalist, 8: 1-20.

[September

Nagendra, H., and Gadgil, M. (1999). Satellite imagery as a tool for monitoring species diversity: an assessment, Journal of Applied Ecology, 36: 388-397.

Nandy, S., and Kushwaha, S.P.S. (2010). Geospatial modelling of biological richness in Sunderbans, Special issue of Journal of Indian Society of Remote Sensing, 38: 431-440.

Nayar, M.P. (1996). Hot Spots of Endemic Plants of India, Nepal and Bhutan, Tropical Botanic Garden and Research Institute, Trivandrum.

Nayar, M.P., and Sastry, A.R.K. (Eds.) (1987). Red Data Book of Indian Plants, Vol. 1, Botanical Survey of India, Calcutta, pp. 367.

Patel, P.P. (1997). Ecoregions of Gujarat, Gujarat Ecology Commission, Vadodara, pp. 34-36thPrimack, J.R. (2000). Sources and detection of dark matter in the universe, 4 International Symposium on Universe, (Eds.) D. Cline, Springer,

Berlin, pp. 3.

Rodgers, W.A., and Panwar, H.S. (1988). Planning wildlife protected area network in India, Vol. 2, pp. 339, Project FO: IND/82/003. FAO, Dehradun, India.

Roy, P.S., and Tomar, S. (2000). Biodiversity characterization at landscape level using geospatial modellng, Biological Conservation, 95: 95-109.

Roy, P.S., Kushwaha, S.P.S., Murthy, M.S.R., Roy, A., Kushwaha, D., Reddy, C.S., Behera, M.D., Mathur, V.B., Padalia, H., Saran, S., Singh, S., Jha, C.S., and Porwal, M.C. (2012). Biodiversity Characterization at Landscape Level: National Assessment, Indian Institute of Remote Sensing, Dehradun, India, pp. 140.

Roy, P.S., Padalia, H., Chauhan, N., Porwal, M.C., Gupta, S., Biswas, S., and Jagdale, R. (2005). Validation of geospatial model for biodiversity characterization at landscape level - a study in Andaman and Nicobar Islands, India, Ecological Modeling, 185(2-4): 349-369.

Sala, O.E., Chapin-III, Stuart, F., Armesto, J.J., Eric, Berlow, Bloompled, J., Dirzo, R., Huber-Sanwald, E., Huenneke, Laura, F., Jackson, R.B., Kinzig, A., Leemans, R., Lodge, D.M., Mooney, H.A., Marto Oesterheld, N., Poff, LeRoy, Sykes, M.T., Walker, B.H., Walker, Marilyn and Wall, D.H., 2000. Global biodiversity scenarios for the year 2100, Science, 287: 1770-1774.

Shannon, C.E. and Weaver, W. (1949). The Mathematical Theory of Communication, University of Illinois Press, Urbana.

Shukla, R.P. (2006). Biogeography and Vegetation Types of India, A Ph.D. Thesis submitted to Department of Botany, Deen Dayal Upadhyay University, Gorakhpur, Uttar Pradesh, India.

Singh, J.S. and Kushwaha, S.P.S. (2008). Forest biodiversity and its conservation in India, International Forestry Review, 10(2): 292-304.

Stewart-Brown, S., Evans, J., Patterson, S., Peterson, S., Doll, H., Balding, J., and Regis, D. (2000). The health of students in institutes of higher education: An important and neglected public health problem? J. of Public Health Medicine, 22, 492-499.

Vavilov, N.I. (1951). The origin, variation, immunity and breeding of cultivated plants, Chionica Botanica, 13: 139-248.

CONSERVATION OF LITSEA DECCANENSIS: AN NTFP UNDER LOCAL EXTIRPATION

1C.K. MISHRA AND G. RAMAKRISHNA NAIDU

Office of Principal Chief Conservator of Forests, Social Forestry, 29-16-39, Gopal Reddy Road,Vijayawada – 520010, Andhra Pradesh.

ABSTRACT

Population of Litsea deccanensis is dwindling due to unscientific harvest and over exploitation. In the present paper matrix population model has been applied to study the behavior of NTFP, Litsea deccanensis, which is on decline in the Chittoor district of Andhra Pradesh due to over exploitation from bark extraction. Its conservation measures have been suggested.

Key words: NTFP, Litsea deccanensis, Extirpation, Transition matrix, Eigenvalue, Eigenvector.

Introduction of these beats covering population of the target species. The plants were marked and their height and girth were Many plant species from forests including some measured at an interval of three months. The flowering, important Non Timber Forest Products (NTFPs) have seeding was recorded at the Silvicultural Research been locally extirpated due to over exploitation and Station, Rajahmundry. The results were drawn into a life unscient i f ic harvest ing. Understanding how table and from that the projection matrix was derived.management activities impact plant population

dynamics is necessary to conserve at risk species, control Description of the Speciesinvasive species and sustainably harvest non timber Family : Lauraceaeforest products. Matrix population models are Genus : Litseac o m m o n l y u s e d t o g e n e ra te m a n a g e m e n t Species : deccanensisrecommendations (Orou et al., 2011). In our study area Local name : Narmamidi, Peddaone such species named Litsea deccanensis has been bandali (Telugu)subjected to over exploitation due to destructive English name : Indian Laurel.practices of bark extraction. We examined its natural Habitat : Rare in the hills of moist population and with the help of collateral data, places. attempted to project the behaviour of this plant in its Flowering and Fruiting : October to April.natural habitat by application of matrix model, and Utilitysuggest conservation measures.

The seeds contain aromatic oil which has been Study Area used to make candles and soap. The fruits have a sweet

The study region consists of the forests of Chittoor creamy edible pulp. Fodder: The young leaves are eaten East and Tirupati Wildlife Management Divisions of the by livestock. The pounded seeds are also applied Seshachalam Hills in the Eastern Ghats of Andhra medicinally against boils. The bark also acts as a

0 0Pradesh. This study area lies between 13 – 2' to 13 – 56' demulcent and mild astringent in diarrhea and 0 0 dysentery. Increasing demand of the barks of Litsea of Northern latitude and 78 – 47' to 80 – 3' of Eastern

deccanensis and Lisea glutinosa in agarbati preparation longitude. The forests constitute Southern Tropical Dry has resulted into destructive harvesting and decimation Deciduous Forests, Dry Mixed Deciduous Forests, Thorn of the natural population. Forests, Mixed Deciduous Forests and Moist Deciduous

Forests. The natural population of target species was Availability of Litsea deccanensisobserved in three forest beats namely Ganugapenta,

This species is observed to be in a precarious Chamla and Nagapatla covering total area of 5321

condition in the forests of the study area due to hectares with very low density.

overexploitation and destructive harvesting. Natural Methodology regeneration, mostly of coppice origin and from root

suckers is observed in the three beats named earlier. But Ten sample plots of 0.1 ha were established in each

The matrix population projection model shows the population of Litsea deccanensis at a precarious level and is not going to attain exploitable levels in natural forests.

1Environmental Sciences, S.V. University, Tirupati, Andhra Pradesh.

Indian Forester, 139 (9) : 769-772, 2013http://www.indianforester.co.in

ISSN No. 0019-4816 (Print)ISSN No. 2321-094X (Online)

768 The Indian Forester [September

PLANT RICHNESS MODELLING IN SOUTH GUJARAT USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM 2 2...

Documents

Transcript of PLANT RICHNESS MODELLING IN SOUTH GUJARAT USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM 2 2...