Proc. Indian Acad. Sci. (Engg. Sci.), Vol. 6, Pt. 3, September 1983, pp. 209-231. Printed in .

Forest survey and management using remote sensing

N V MADHAVAN UNNI and Ecology Division, National Remote Sensing Agency, Balanagar, Hyderabad 500 037, India Abstract. Some of the important work done in India and other parts of the world in the application of remote sensing for survey and management has been reviewed in this paper. The account of work has been given under three main headings viz airborne remote sensing, satellite remote sensing and multistage approach.

Keywords. Forest survey; ; airborne remote sensing; aerial photography; satellite remote sensing; multiband photography; multispectral scanning; thermal infrared.

1. Introduction

The problems involved in maintaining a sustained supply of natural resources for the contemporary needs and futuristic projections of demands of mankind have made the land managers conscious of a compelling need for planned utilization of earth resources. Conservation and planned utilization of earth resources require a detailed understanding of the resources available with respect to their quality, quantity and distribution so as to strike a balance between the exploitation and regeneration processes and ensure the preservation of environmental quality. The conventional survey methods are not very efficient for this purpose. The advent of modern remote sensing techniques for collecting information about earth's surface features using photographic cameras and multispectral scanners from aerial and satellite platforms and man-machine interactive processing and analysis have ushered in an exciting and new era revolutionising the resource survey methods. Their capability to provide near real-time data with wide synoptic coverage and also for temporal verification are significant advantages with immense possibilities.

2. Application of remote sensing to forestry

Aerial remote sensing to study is almost a century old although the term in the modern sense, starting with the use of infrared photography, is only half a century old. Satellite remote sensing is only of recent origin beginning with the use of photographs taken from Apollo-9 satellite in late sixties.

3. Airborne remote sensing

Airborne remote sensing is of two types viz photographic or non-photographic. Use of photographs taken from an elevated platform is quite old. However, the non- photographic scanning methods have come into existence only during the sixties. 209 210 N V Madhavan Unni

3.1 Use of aerial photography

Although there had been earlier experimentation of photography from raised platform like hot air balloon etc, for making forest maps, perhaps the first serious attempt of using photographs taken from aircraft to study forests was by Seeley (1929) who used oblique photographs to measure heights from shadows and related tree heights to timber volume. Aerial photographs are useful for classification of forest land and forest volume inventory.

3.1a Classification ofJorest lands Use of aerial photographs or other imageries is to complement and improve or reduce field work rather than absolutely replacing it. Therefore forest land is classified according to a scheme and using a combination of image interpretation techniques and field work. As different parts of the world have different types of forest cover, the classification schemes also vary from region to region. Therefore the interpretation keys also will be different. Identification of forest types and tree species will depend upon the scale, film used, the season and the image quality. Considerable research has gone into the selection of the best film/filter combination for the forest type identification. Panchromatic photography has been found to be superior in identifying tree species in western United States by Jensen & Colwell (1949). However, Spurr & Brown (1946) and Seeley (1949) found that in the eastern us seperation between hardwoods and conifers was better on infrared minus blue, although species identification was not very good. Chase & Korotov (1947) and Steigerwaldt (1948) found that infrared minus blue in Great Lakes States was better. Stone (1950) found that in Alaska panchromatic was successful while Hegg (1966) preferred infrared minus blue. In India black and white aerial photographs were used by Jones, a Canadian expert who collected data on the forest of Kulu and Seraj Valley. Versteegh (1968) prepared forest cover type map for Bastar (Madhya Pradesh) on 1:25,000 scale using aerial black and white photographs of 1:15,000 scale. Tomar & Maslekar (1974) suggested a classification scheme for forest survey and mapping of forests of India with reference to photo-interpretation (table 1). Tiwari (1978) made a comparative evaluation of accuracy, time and cost to prepare forest and land-use map using photointerpretation techniques and conventional ground survey, in Tehri-Gherwal area, and found that they gave more accurate information with respect to the boundaries of the types, and that they could be mapped in much less time and at lower cost. The Forest Survey of India prepared forest cover type and land-use maps (as reported by Shedha 1983) on 1:50,000 and 1:63,360 scale by interpreting medium to small scale panchromatic aerial photographs for about 4,20,000 km 2 in India, 36,600 km z in Nepal and 29,200 km z in Bhutan during the period 1965-1982. Figure 1 is a part of the forest cover type map prepared for a part of the Godavari basin area by visual stereoscopic interpretation of panchromatic aerial photographs at 1:25,000 scale (Madhavan Unni et al 1983). An integrated perspective of the forest cover types was attempted indicating average density and height and the type of terrain on which they stand. The topography is first defined into three categories viz (a) flat (slopes upto 10 ~) (b) undulating (slopes 10-30 ~o) and (c) hilly (slopes more than 30 ~o). The cover types were identified by giving the dominant tree species, for example Forest survey and management usin 9 remote sensin 9 211

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Figure l. Forestmap prepared by visual interpretation of aerial photographs for a part of Godavari basin (Madhavan Unni et a11983) forest means percentage of teak more than 30 ~o; mixed teak with 10-30 ~o by number of stems and when no species predominate the term miscellaneous was used. Five crop densities were identified such as 5-20 ~o, 20-40 ~o, 40-60~o, 60-80~o and 80 ~o and above. The average tree height also was estimated into two classes (i) less than 10 m and (ii) more than 10 m. Besides, , degraded areas and forest blanks also were identified and delineated. This type of maps is useful to understand the forest type distribution and the timber stand volume available. It also helps in identifying areas suitable for raising plantations. Figure 2 is a land facet/land form map for a part of the Godavari basin prepared by Bedi (1982) by visual interpretation of aerial photographs on 1:25,000 scale. It delineates the different land forms in the area with constituent rocks and thickness of soil cover, erosion/accretion stages, existing vegetation cover and limitations/ capabilities. Whether or not a particular category is favourable for forest growth and in areas where there are forests what are the management practices like checking headwards extension of stream and erosion, land scaping, raising plantations and so on are to be taken to check the degradation process, were also recommended. Considerable studies on using aerial photographs of different film filter combi- nations for forest species identification and type mapping have been carried out in Europe (Belov & Berezin 1958; Pohorly 1958; Hildebrandt 1963; Lackner 1966; Nyyss6nen et al 1968; Rabinau 1969). Although there are differences of opinion, the general consensus is that the species identification improves as the photo scales become larger, colour and colour infrared are better than black and white for species identification when medium and large scale photographs are used and stand type identification is better on medium scale (1:151000) photographs than on both large and 214 N V Madhavan Unni

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A- denudational features E- streams ~fragments of adissected flat top surface main stream/river round top ridge with variable slope tributary streams scarp / steep slope I rock wall terrace scarp residual hill F- forest / vegetation relation triangular facet densely forested flat top forested degraded slope B-depositionaL features sparsely forested valley fill ~fan densely forested valley fill Bazada forest pocket C-slopes. G-mlscelLeous features ~a slope direction cleared and cultivated area --t-- gentle wllage or settlement -~, medium to steep ----- converging f~ dam diverging 'as' obsequent D-erosion classes ~rr sheet erosion l} hill erosion lit deeply incised gully

Figure 2. Landform/land facet map prepared by visual interpretation of aerial photo- graphs for a part of Godavari basin (Bedi 1983)

small scale photographs although in regions where fewer species occur stand type could be identified on small scale (1:30,000) photographs. All of them agree that when photography is done in summer, black and white or colour infrared is preferred for stand type identification irrespective of the scale chosen. True colour aerial photo- graphy was found to be better than black and white photography by Becking (1959), Heller et al (1964) in United States and by Cochrane (1968, 1970) in sclerophyllous forests of Australia for species identification. As the vegetation reflects highly in near-infrared, infrared false colour film is useful for discriminating vegetation from other objects. True vegetation develops red tones on these photographs and this property was first used in world war II for camouflage detection i.e. for discriminating green painted things within the green vegetation. Lauer (1968) and Anson (1970) found that infrared false colour photographs enhance the capability of forest stand type identification. Madhavan Unni et al (1983) report that some tree species could be identified in deciduous forests of Andhra Pradesh on infrared false colour photographs by combining tonal characteristics and phenology for deriving conclusions.

3.1b Forest volume inventory Stand volume can be computed if tree height, crown diameter, crown closure, crown area and the number of are known. All these can be measured from stereoscopic photocoverage. The tree height will be closely related to the volume. The diameter at breast height (DBn) is also found to be related to crown diameter (Feree 1953; Bonner 1964; Wolff 1966; Eule 1959; Klier 1970; Toma~egovi~ 1963; Samojilovic 1940; Berezin & Trunov 1957, 1963; Hildebrandt & Kenneweg 1969). The components which can be measured from aerial photographs are correlated to the volume measured on the ground by regression analysis and aerial volume tables prepared for all tree species or combination of tree species and forest types. 216 N V Madhavan Unni

Assessment of tree height by measuring the tree shadow length on the photographs was explored earlier (Seeley 1929; Rogers 1947, 1949). However, this was replaced by more accurate methods such as using a parallax wedge or parallax bar (Spurr 1945; Rogers 1946; M6essner 1961, 1962; Wert & Myhre 1967; Lund 1971). This is done by measuring at two points and looking through a stereoscope viz one at the base of the tree and the other at the top of the tree and finding out the parallax of the tree. There are equations which relate the tree height to the height from which the photographs are taken, the absolute parallax and the differential parallax. The accuracy of the parallax measurements by either parallax wedge or different types of parallax bars with a floating point has been compared by Johnson (1958) and M6essner (1961) who found no significant difference between them. The exactness of stereophotometry for tree height measurements has been tested in terms of types of photographs (Akca et al 1971) and in terms of scale (Pope 1957; Johnson 1958; Versteegh 1968; Tomar 1970; and Joshi 1972). The standard errors in tree height measurements in the best and poorest cases were found to be between + 0"3 to 2-6m by Nyyss6nen (1955), + 1-5 and + 3m by Spurr (1960) and _+ 0-4 to _+ 3-2m by Schultz (1970). The crown diameter is measured by micrometer wedges of different types (Losee 1956; and M6essner 1960) and the accuracy will depend upon the scale. For example M6essner (1950) could group them in 1-5 m classes on 1:20,000 scale. Worley & Meyer (1955) could do within 1 meter of the actual diameter or 1 : 12,000 scale and Losee (1953) within 30cm on 1:12,000 scale. Methods of measuring crown closure, a measure of crown density have been described by Robinson (1947), M6essner (1949), Losee (1953, 1956) Aldrich (1967) Bonner (1968) and Seth & Tomar (1970). Crown area also can be a component to the stand volume and has been useful when large scale photographs are used (Sayn-Wittgenstein & Aldred 1967). Another variable is tree count, the accuracy of which depends on the photographic scale, resolution, stand density and the homogeneity of stand (Thorley et al 1975). There are several aerial volume tables prepared by regression analysis using values for tree height, crown density and diameter etc, by photometric measurements and corresponding ground estimation of volumes for different tree species (Feree 1953; Bonner 1964; Lyons 1966) and stand types (M6essner et al 1951; Gingrich & Meyer 1955; Morris 1957; Allison & Breadon 1958; Avery 1958, 1959; M6essner 1960, 1963; Pope 1961, 1962; Duffy & Meyer 1962; Hanks & Thomson 1964; Chapman 1965; Bonner 1966; Joshi 1973).

3.2 Other airborne remote sensin9 techniques Multispectral data have been used in recent times to study forest features. A similar spectral response by two features in one narrow band of spectrum will not allow differentiation between them. However, the difference in response to another narrow wave band can separate them into two different categories. The difference may not be pronounced when the responses to a wide band covering the above two bands are studied. Hence, the more the number of spectral bands used, the more will be the possibility of differentiation of features. All the subtle differences in response to different bands, however, may not be apparent to the naked eye. Forest survey and management using remote sensin 9 217

The multispectral data could be in the form of multispectral photographs or in the form of multispectral digital data. Multiband photography using different film/filter combinations is not a new idea to forest land studies (Ryker 1933; Jensen & Colwell 1949; Spurr 1949; Schulte 1951; Haack 1962; Carnegie & Lauer 1966). Nevertheless, multiband camera, multilens camera and multispectral scanners are new equipment which can provide high quality imageries. The advantage ofmultispectral photographs is that colour images can be reconstituted by passing the appropriate coloured lights through individual transparencies in different spectral bands and combining them. These reconstituted colour images were better than traditional black and white photographs or colour photos (Colwell et a11969; Lauer et a11971; Shedha 1980, 1982). Thorley et al (1975) observed that computer processing enables man to extend his capability and perform tasks otherwise not possible. For example, automatic image classification techniques may not only make the final decision on forest classification but can also (i) perform first approximation interpretation, calling attention to areas needing further analysis by the human (ii) combine and integrate remote sensing data gathered in a portion of the spectrum beyond the visible and (iii) extract additional information from imagery by amplifying small differences in radiance which are below the human threshold. In addition, the digital analysis techniques through appropriate computer systems reduce the time required for interpretation. The automatic computer analysis techniques have been tried both by using digitised data obtained by scanning of panchromatic photoprints and multispectral data acquired by a scanner. Langley & Sharpnack (1968) could get 97 out of 100 data cells of various combinations of conifer and hardwood species correctly classified by automatic photo scanning of panchromatic prints. They also classified 46 ~o of the observed types when the technique was tried on an experimental forest although the expected correct classification by purely random assignment was only 25 ~o- Smedes et al (1969) differentiated forested area in Yellow Stone National Park with an overall accuracy of 80 ~o, eight vegetation/terrain classes on a computer map generated by automatic classification using the LARSYSSA programme of Purdue University. Multispectral line scan imagery was also used by Lent (1969) for attempting automatic classification using LARSYSSAprogramme based on maximum likelihood ratio calculations and he was able to discriminate nine categories viz open, mixed species brush fields, dense snow brush, dense manzanita, mixed conifer stands with some hardwoods, cleared brushfields, roadways/bare areas, lake shore line, water surfaces and snow patches. Except for roadways and lake shore lines the rest gave satisfactory separation. Olson & Rhode (1970) separated with 70 ~ accuracy conifers, red oak, white oak, black locust, black walnut and sugar mapple by automatic classification techniques using Michigan spectral processing and recognition computer. Madhavan Unni et al (1982) analysed the multispectral data in channels 4(0.54-0-58 p), 6(0.62-0.70/~), 8(0-704).74/~) and 9(0.77-0.86/~) of a Bendix 11 channel multispectral scanner through an interactive computer system viz Bendix multispectral data analysis system (MDAS) employing a supervised classification method using a maximum likelihood algorithm to produce a colour coded output giving 12 categories viz (i) teak forest/older teak (ii) teak forest/young teak plantations (iii) mixed Annogeissus (30-80~o) forests (iv) miscellaneous forests well stocked 218 N V Madhavan Unni

(v) miscellaneous forests medium stocked (vi) miscellaneous forest poor stocked (vii) degraded forest/scrub (viii) forest depot (ix) areas affected by ground fire (x) standing crop (xi) fallow/blank (xii) water bodies. In some of the tropical areas remote sensing in higher frequency regions of electromagnetic spectrum is restricted due to perpetual cloud cover. In such cases cloud penetrating microwave remote sensing is the only solution. There had been reports of the use of side-looking airborne radars (SLAR)for forestry studies (Viksne et al 1970; Azevedo 1971). The K band is most favourable for forestry studies as it will not penetrate the vegetation cover.

4. Satellite remote sensing

The multispectral data obtained from satellites like LANDSATare used to study broad forest features either by visual interpretation of data in the form of imageries or by computer analysis of the multispectral data in 4 bands viz band 4(0-5-0.6/z), 5(0.6-0"7#), 6(0.7-0.8/~) and 7(0.8-1'1/1). The advantages of LANDSAT data are (i) synoptic coverage (ii) repetitive coverage and (iii) multispectral nature. The limitations are low spatial resolution and lack of stereo coverage. Between the two analysis methods viz visual and computer, the former alone may not bring out all the details as it cannot fully utilize the multispectral nature of the data. The latter can make full use of the subtle differences in the multispectral nature and therefore more detailed spectral information could be extracted. However, as this alone cannot take into account other photo elements like texture, shape, size, pattern, location etc for making the final decision, a combination of visual and computer techniques would be ideal. Study of vegetation on the earth surface using data collected by satellite has been a subject of interest ever since the data from even weather satellites like Nimbus started pouring in. However, satellite data began to be seriously considered for study of vegetation only when the Apollo~Gemini photographs became available, even though Rabchevsky (1970) reported that the Nimbus III day-time HRIR (0"7--1"3/~) data with a resolution of 5 nautical miles have proved to be useful to monitor moisture/vegetation conditions in Mississipi and Niger valleys. Infrared colour pictures taken by Apollo astronauts were useful in providing information on the distribution of relative timber volumes and for selecting first-stage samples in multistage sampling design by Aldrich (1971). He found that since the photographs were taken at a time when the deciduous trees (hard ) were without leaves it gave distinctive colour characteristics to deciduous forests, evergreen and a mixture of these two. Since LANDSAT data became available there had been several reports on its interpretation adopting either visual or automatic computer technique.

4.1 Visual interpretation Of LANDSAT imagery Anderson (1973) reports that in a study conducted in Alaska, band 7 imagery alone could give the final distinction enabling discrimination of the various vegetation types like Spruce forests, broad-leaved trees, scattered Spruce, vegetation where Spruce and broad-leaved share dominance, shrub-dominated vegetation and vegetation dominated by heterogenous dwarf shrub species etc. Forest survey and management usin9 remote sensin9 219

There had been some attempt in India to delineate the forests by visual interpretation of black and white band 5 imageries. The Forest Survey of India (1982) adopted this technique to cover most of the forest areas of India and although the technique was inadequate the delineation attempted in general was into categories like closed and open forests, those affected by /biotic factors and non-forests. In a false colour composite picture ofa LANDSATscene made by superimposition of band 4, 5 and 7 imageries through which lights of appropriate colour were passed, the forest and other vegetation appear in various red tones. The extent and layout of wild land vegetation over very large area can be seen in a single synoptic view in this picture. Careful delineation of differences in the red tone can differentiate many broad cover types. Anderson (1973) differentiated forests into needle-leaved trees, scrub vegetation, herbaceous tundra vegetation, muskeg and bog vegetation from reconstituted simulated infrared colour images. Hall (1973) studied the colour composite (simulated infrared) and reported that it is extremely useful to detect the degree of mortality by insect infestation on pine trees and also for mapping timbered and untimbered areas, timber stand density, principal stream courses and lakes, mountain meadows and grazing land, massive rock outcrops and dunes, riparian vegetation and possibly glaciers. Heller et al (1975), Heller (1976), Howard (1976) and Jaakola (1976) also found that visual interpretation of colour composites of LANDSAT imageries yields valuable information. Unlike the temperate forests which are characterised by vast tracts of homogeneous timber stands, tropical forests present a flora which varies frequently from place to place in species composition. This heterogenous nature of Indian forests poses problems in the application of satellite data to extract detailed information like species identification, density determination, timber stand volume assessment etc (Madhavan Unni 1978). LANDSATcollects data over the same area once every 16-18 days. Thus a picture over the same area can be produced every 18 days (now 16 days). Consequently the process also records changes occurring in the area covered during different seasons or over the years. The difference in colour tones in forested area between the two imageries of two different seasons of the same year shows the phenological changes that have occurred. For example, those areas where January imagery has red tone but in March imagery no or reduced red tone, are deciduous forests which shed their leaves. Such comparisons not only help in understanding the phenological behaviour of forests over large areas but also help in differentiation and delineation of different broad types of forests such as evergreen from deciduous etc. In other words changes that have occurred could be monitored (Madhavan Unni & Roy 1979). There had been attempts to standardise a procedure for making use of the repetitive LANDSATimageries for visual interpretation and delineation of forests in terms of broad cover types and in terms of crown closure. Roy (1982) studied the LANOSArimagery products covering different vegetation cover types of various phytogeographical regions of India and has come forward with a three-step 'decision tree' viz interpretation of band 5 black and white imageries of winter time as the first step, then of the summer time and finally the interpretation of false colour composite of summer time for the same area for making a more accurate identification of the cover types. Madhavan Unni (1983) reports that experimental studies have revealed that the 220 N V Madhavan Unni preprocessing of the raw LANDSATdigital data using image enhancement techniques like stretching, band ratioing, etc., before generation of both black and white and colour composite imagery products brings out the contrast between the vegetation types better and this technique holds great promise in making the visual interpretation easier and more accurate.

4.2 Digital analysis of multispectral LANDSATdata Multispectral LANDSAT data in digital form can be analysed by computers and categorised into forest cover types and produced in the form of colour coded maps. This requires only very limited field work. The representative sample features are visited on the ground and their exact geographical locations are marked. This is known as ground-truth. These areas are identified on the raw digital data display and spectral signatures for individual categories developed by the computer. Using these spectral signatures the computer categorizes each pixel. The accuracy of classification is further checked on the field and the necessary modifications in the categorisation are made. The supervised approach was employed by Kan & DiUman (1975), Bryant et al (1978) and Mead & Meyer (1977). It was found that the classification accuracies are not very high. The accuracy was higher when multidate overlays were used for supervised classification using the principal component analysis (Williams 1976; Williams & Hayer 1976; Lapietra & Megeir 1976;Kalensky & Wightman 1978; Nelson & Hoffer 1979). Studies have been carried out to determine which type of classification procedure viz supervised or unsupervised or a combination of these two is the best to categorise the forest features (Hoffer & Staff 1975; Edwards 1977; Harding & Scott 1978 and Fleming & Hoffer 1977). It was found that a combination of unsupervised and supervised which is termed as multicluster blocks approach was the best. Schubert (1978), however, found that automatic classification using ratios and previously-established ratio signatures is the best method. Early studies in India using the LANDSATdigital data forests were not dealt separately but only as a category in the land cover/land use maps generated covering the state of Orissa (Sharma 1976) and Upper Barak River Watershed (Gautam 1977). The supervised approach was adopted for the above mentioned studies as well as other studies done in India by digital analysis of LA~OSATdata which are referred to in this paper. The first attempt to categorise forest cover types by computer analysis of LANDSATdigital data was in 1978 for Nagaland. In this study (Madhavan Unni 1977, 1978) a colour coded categorised map delineating the broad forest cover types such as temperate evergreen, tropical evergreen, tropical semi-evergreen, tropical deciduous, bamboo mixed, bamboo pure, degraded areas, shifting cultivation, permanent cultivation and water bodies were generated. Apart from showing that the technique can be used for mapping broad cover types, it brought out the extent of area degraded due to shifting cultivation in a single synoptic perspective. Similar studies in Mizoram (Roy et a11979; Roy & Madhavan Unni 1980) produced maps showing subtropical evergreen, tropical evergreen, tropical moist deciduous, bamboo, Quercus forests and areas affected by shifting cultivation (figure 3) and in Arunachal Pradesh (Roy et al 1983) temperate forests, coniferous types, subtropical broad-leaved, subtropical pine forests, semievergreen forest, Hollong and Nahor forests, alpine shrubs/grass land, bamboo brakes, degraded forests, shifting cultivation both current and abandoned. Forest survey and manaoement usin 9 remote sensin 9 221

Figure 3. Computer classified LANDSATdata showing forest classes in and around Chalfill hill range (Roy & Madhavan Unni 1980)

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Figure 4. Computer classified LANDSAT data showing redsanders, miscellaneous and degraded forest in the part of study area III (Part of Seshachalam hill range) (Madhavan Unni el al 1980) Forest survey and management using remote sensing 223

Attempts were also made to differentiate forest cover types on the basis of dominance of some trees in similar broad cover types. For example teak-dominated areas could be delineated apart from categories like teak, miscellaneous, miscellaneous with bamboo, degraded forests and scrub in Andhra Pradesh (Madhavan Unni & Roy 1978; NRSA 1978). Similarly areas dominated by teak, Kardhai, Khair, Salai, Babul, miscellaneous deciduous and degraded forests were delineated in Bundelkhand area in Uttar Pradesh (Kachhwaha 1981). Selecting a time of the year when maximum differences occur between the species or associations of species due to phenological changes such as leaf fall, flowering etc., improves the capability of LANDSAT data to differentiate forest cover types. Digital analysis of LANDSAT data acquired when Shorea robusta was flowering gave areas dominated by this type of tree a specific spectral signature different from other categories like Dipterocarpus turbinatus, evergreen, miscellaneous deciduous, some plantations, bamboo, degraded forests, scrub, shifting cultivation, non-forest land and water bodies in Tripura (Madhavan Unni et a11979; Madhavan Unni & Roy 1981). Red Sanders (Pterocarpus santalinus) growing areas could be differentiated from other deciduous forest types when the LANDSATdata over the Seshachalam ranges in Andhra Pradesh obtained in the leaf fall season was analysed (NRSA 1978; Madhavan Unni et al 1980). Bamboo growing areas in Nallamalai ranges were identified and delineated although bamboo grows as an under-growth in this region. Use of LANDSA'rdata of leaf fall season facilitated the identification of bamboo undergrowth, since they were exposed (NRSA1978; Madhavan Unni et al 1980). In a recent study conducted at NRSA (MadhaVan Unni & Roy, unpublished) the satellite digital data covering the entire Periyar-Thodupuzha drainage basin was analysed for producing maps of two different points (1973 and 1982) to bring out the changes in the forest/vegetation cover and study the impact of the hydroelectric project. This study has brought out the synoptic perspective of the changes in the vegetation cover and the forest land use during a span of 9 years.

5. Multistage approach

This approach is generally used to extract quantitative information like timber stand volumes using stratification of imageries obtained from space and aerial platforms and estimating the total volume using information obtained from ground samples. A two-stage sampling design with aerial photos and ground samples has been in vogue for quite some time (Rogers 1960, 1961; Brenac 1963; Shiue & John 1962; Bickford 1963; Miller & Choate 1964; Bickerstaff & Hirvonen 1969; Husch et al 1972; Ferguson & Kingsley 1972; Spencer & Essex 1976; Born 1977). Satellite photography was used as first level information ~n a multistage design with variable probability sampling theory (Langley 1969). Langley claims that the space photos helped to reduce the sampling error by a factor of 2"5. Although the error was calculated as 13 ~ the gross cubic meter volume over 2 million hectares was predicted as 63 million gross cubic meters with only 10 ground samples of 2.4 hectares actually measured on the ground. There had been other attempts to estimate stand volumes over large areas adopting multistage sampling techniques using LANDSATdata. Nichols et al (1974) interpreted and stratified the predominantly old growth areas by visual and computer classification of LANDSATimagery covering Plumas National Park in California and used it as the first 224 N V Madhavan Unni

stage of a multistage design. He claimed to have achieved results with the same accuracy as in other methods but with a 44 % cost savings. Harding & Scott (1978) demonstrated that forest inventory in Western Washington forest could be done by a multistage sampling technique which involved digital analysis of LANDSATdata, photointerpret- ation of sample units and limited actual measurements on the ground with 54 ~o cost saving than a design using only photointerpretation and ground sampling.

6. Other applications

Apart from forest land classification, stock mapping, and volume estimation, remote sensing is also used for damage assessment and fire detection.

6.1 Damage assessment Damages are caused due to external causes like fire, cyclone, landslides, human encroachment etc or due to biological causes like insects and diseases. While the former can completely destroy growing stock the latter may damage a few trees or large tracts of trees. They appear as completely defoliated or partially defoliated, leaves not consistent with the normal colour or internally affected but without visible symptoms. Heller et al (1959) found that colour film is better than panchromatic black and white while Ciesla et al (1967) found colour infrared superior to colour to detect southern pine-beetle infestation. Heller et al (1969) found that colour and colour infrared could not make pre-visual detection of mountain pine beetle attack on ponderosa pines. Detection of damages by the Dauglas-fir beetle by remote sensing has been studied (Wear et al 1966; Wert & Roettgering 1968; Ciesla et al 1971; McGregor et al 1972). To assess the damage caused in the mid sixties by the attack of mountain pine beetle in epidemic form in the northern Black Hills of South Dakota in United States, Heller & Wear (1969) adopted a multistage sampling design using colour photography and ground sampling. The stand damage inventories using IR colour photos has also been assessed in Europe (Wolff 1967, 1970). Sequentional colour photography has been used to monitor the effectiveness of chemical treatment as well as to record stand mortality caused by insects and worms (Caylor & Thorley 1970; Thorley et al 1965; Aldrich & Drooz 1967; Aldrich & Heller 1969). Other examples of use of remote sensing to detect diseases of forest trees in the temperate region are: colour photography for Ash dieback (Croxtan 1966), colour and colour IR photography for Dutch Elm disease (Meyer & French 1966; La Perriere & Howard 1971) and beech-bark dieback (Hildebrandt & Kenneweg 1968; Wolff 1970), Oak wilt (Roth et al 1963) and beech bark disease (Houston 1969), root rot disease (Wear 1971), certain fungal diseases of Pinus strobus (Hildebrandt & Kenneweg 1969) and another affecting scott pine (Kenneweg 1971) and red root rot disease (Wolff 1970). Very little information is available on the use of multispectral data obtained by line scanners for disease detection. Weber & Wear (1970) found that spectral and temperature differences were too small for airborne scanner to be used for pre-visual detection of Poria Weirii on Dauglas-fir trees. Shedha (1982) distinguished diseased and dead teak, sal and eucalyptus trees from healthy ones on colour infrared photographs. He also reports on the diseased Erythrina trees and dead bamboo clumps Forest survey and management usin9 remote sensin9 225 discriminated on colour composites of muhispectral photographs with hues different from the healthy ones. Detection of the effects of oxidant air pollution on ponderosa pine was investigated by Miller et al (1969), Heller (1969) and Wert (1969). It was found that colour aerial photography can be used for estimating the foliar damages. Hitdebrandt & Kenneweg (1969) noted that interpretation on colour infrared transparencies can differentiate all levels of damages to spruce stands due to SO2 and fluorine pollution. In East Germany the survey of air pollution damage to forests is a routine management practice where a two-stage probability sampling is practiced (Lux 1965; Wolff 1970). The first stage of this procedure involves interpretation of large scale spectra zonal photographs followed by the second stage of checking on the ground in randomly selected ground sample plots. Storm damage to the forests could be assessed efficiently by interpreting aerial photographs (Rohdy 1962; Tokmanoglu 1969; Neustein 1971).

6.2 Fire control Fire control is an important aspect of forest management and the application of remote sensing to fire control has not yet been taken up seriously in India. Forest management in the context of forest fires can be broadly classed into three types viz pre-fire planning, detection of active fire and post-fire evaluation. Pre-fire planning needs information pertaining to existing facilities, timber and fuel types, fire breaks and transportation facilities. Information is also required about topographic features to help in the construction of fire breaks and constructing quick approach roads. These can be obtained by interpretation of aerial photographs. Fire detection systems should operate irrespective of cloud cover and dense forest cover. There should also be effective operation round the clock and it should differentiate potentially dangerous fires from those of no concern (Hirsch 1964). The photographs can identify the fire in relation to fuel type and fire breaks during day- time, cloud- and smoke-free conditions. But during night and cloudy- and smoke- covered situations the detection will be difficult. However, in recent times there have been systems working in ~R thermal regions which have been successfully used to detect fires in the above mentioned situations (Hirsch et al 1965, 1971; Kruckeberg 1971). The best method for post fire damage evaluation is aerial photography using infrared colour films which provide better contrast than panchromatic black and white photographs (Benson & Sims 1967; Minnich 1974; Madhavan Unni et al 1983). Photographs from Gemini and Apollo have shown forest fires in remote areas (Thorley et al 1975). Lauer & Krumpe (1973) demonstrated that computer-generated map from LANDSAT data gave a 10 to 1 cost advantage for mapping fire affected areas than preparation of an operation map by ground survey. It was also demonstrated that the accuracy of area estimated by analysing LANDSATdata through a computer was much higher. The computer-generated map showed considerable unburnt areas within the fire perimeter which the ground survey overlooked.

7. Forest management and remote sensing

Remote sensing has been accepted as an operational tool for information gathering system for forest management in developed countries. The main applications have been 226 N V Madhavan Unni for timber harvest planning and monitoring and . Although modern remote sensing techniques have yet to be accepted as a routine system to gather information, there had been attempts to use the maps generated from aerial photointerpretation for use in forest management in India. The Forest Survey of India has suggested about 30 industries relating to pulp and paper mills, , saw mills, fibre board, hard board and newsprint based on the results of forest inventory using aerial photointerpretation. Tomar (1976) used stock maps prepared by interpretation of small scale aerial photographs for preparing the working plan of Vidisha Forest division of Madhya Pradesh. Venugopal (1978) used growing stock estimates through photo-interpretation for preparing his working plan of Karimnagar east forest Division. Gupta (1980) also used aerial photographs in working plan revision of Terrai and Bhabar, Bijnor and Siwalik Forest Divisions of Uttar Pradesh. It is also reported that the Hindustan Paper Corporation Limited has been using the photo-interpreted maps for making raw material assessment and preparing extraction management plans for some of their paper mills. Successful use of aerial photo-interpretation and preparation of maps for site quality assessment (Shedha & Suresh Kumar 1980) land suitability for (Pandey & Shedha 1981; Sharma & Negi 1981) soil suitability for plantations like teak (Prasad & Manchanda 1981; Tamsanga et al 1981) also have been reported. Seth & Tomar (1973) used aerial photographs for selection of ground samples to prepare land-use and forest map of a part of east Godavari which stratified various volume and crown density classes of natural forests and plantations and sites suitable for plantations.

8. Promises for the future and research needs

There is a great need to improve the technique of data acquisition, processing and analysis techniques to make remote sensing to forestry more meaningful. There are problems of rectifying airborne multispectral data. It has to be made more economical (less computer time) and new types of scanners which will reduce the geometrical distortions to the minimum have to be introduced. Incorporating photo-elements other than spectral response like texture in the automated analysis technique to imporve the categorisation accuracy may have to be tried. Similarly bringing other supporting information like elevation, aspect, slope etc into the multispectral data stream for the multivariate analysis scheme can improve the species identification possibilities to a great extent. Automated change detection techniques and using of phenological changes by simultaneous use of multidate data also require more attention. Research and development in the above mentioned areas are being done in India and elsewhere. Introducing radar and interpretation techniques may be tried to acquire data over perpetually cloud covered areas.

9. Conclusion

The technique of remote sensing has ushered in a revolutionary change in the methods of studying, surveying and monitoring forest features and phenomena. The data acquisition and data analysis methods are numerous and it is possible to pick up the right one for the objective in hand. One real advantage of remote sensing techniques is Forest survey and management using remote sensing 227

that it can give information about a very large area with considerable reduction in time and efforts needed for ground surveys. The capability to provide real time information makes it possible to have meaningful repetitive surveys which can give an understand- ing of the changes which have taken place and their tendencies and trends so that the immediate and important problems can be dealt with.

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