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Land use change detection in Solan Forest Division, Himachal Pradesh, India

Land use change detection in Solan Forest Division, Himachal Pradesh, India Background: Monitoring the changing pattern of vegetation across diverse landscapes through remote sensing is instrumental in understanding the interactions of human activities and the ecological environment. Land use pattern in the state of Himachal Pradesh in the Indian Western Himalayas has been undergoing rapid modifications due to changing cropping patterns, rising anthropogenic pressure on forests and government policies. We studied land use change in Solan Forest Division of Himachal Pradesh to assess species wise area changes in the forests of the region. Methods: The supervised classification (Maximum likelihood) on two dates of IRS (LISS III) satellite data was performed to assess land use change over the period 1998–2010. Results: Seven land use categories were identified namely, chir pine (Pinus roxburghii) forest, broadleaved forest, bamboo (Dendrocalamus strictus)forest, banoak (Quercus leucotrichophora)forest, khair(Acacia catechu)forest, culturable blank and cultivation. The area under chir pine, cultivation and khair forests increased by 191 ha (4.55 %), 129 ha (13.81 %) and 77 ha (23.40 %), whereas the area under ban oak, broadleaved, culturable blank and bamboo decreased by 181 ha (16.58 %), 152 ha (6.30 %), 71 ha (2.72 %) and 7 ha (0.47 %), respectively. Conclusions: The study revealed a decrease in the area under forest and culturable blank categories and a simultaneous increase in the area under cultivation primarily due to the large scale introduction of horticultural cash crops in the state. The composition of forests alsoexhibited some majorchanges,withanincreaseinthe area of commercially important monoculture plantation species such as pine and khair, and a decline in the area of oak, broadleaved and bamboo which are facing a high anthropogenic pressure in meeting the livelihood demands of forest dependent communities. In time deforestation, forest degradation and ecological imbalances due to the changing forest species composition may inflict irreversible damages upon unstable and fragile mountain zones such as the Indian Himalayas. The associated common property externalities involved at local, regional and global scales, necessitate the monitoring of land use dynamics across forested landscapes in developing future strategies and policies concerning agricultural diversification, natural forest conservation and monoculture tree plantations. Keywords: Land use; Solan Forest Division; Supervised Classification; Maximum likelihood * Correspondence: drshiprashah1984@gmail.com Department of Forestry, College of Agriculture, Fisheries and Forestry, Fiji National University, NasinuP.O. Box 7222, Fiji Islands Full list of author information is available at the end of the article © 2015 Shah and Sharma. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Shah and Sharma Forest Ecosystems (2015) 2:26 Page 2 of 12 Background one region to another. In order to capture the unique- Very few landscapes remain on Earth that have not been ness of each region it is necessary that LULC studies are significantly altered by the human race in one way or an- conducted at local to regional scales to better under- other (Yang 2001). Land use land cover (LULC) change stand the cumulative impacts at multiple scales (Sohl et is the primary modifier of landscapes, affecting a wide al. 2004). Over the years land use and cropping patterns range of socioeconomic, biological, climatic, and hydro- in the western Himalayan state of Himachal Pradesh logical systems (Sohl and Sohl 2012). Understanding the have been changing. The expansion of cultivation at the spatial extent and distribution of LULC change is vital to expense of forests has been relatively more profound in the study of environmental changes at various levels Himachal Pradesh than other parts of India (Melkania (Ojima et al. 1994). LULC change is one of the key and Melkania 1987). The introduction of horticultural drivers of local and regional climate change (Chase et al. cash crops and the commercialization of agriculture 1999), biodiversity decline (Sala et al. 2000), soil degrad- have placed greater demands on the forests (Gouri et al. ation (Tolba and El-Kholy 1992) and the loss of ecosys- 2004) of the state which have not only been undergoing tem services, thereby affecting the ability of ecosystems a change in area but also a change in composition pri- to support human needs (Vitousek et al. 1997). Popula- marily due to human interference (Deshingkar et al. tion growth, rapid economic development and poverty 1997). To arrest forest degradation the state government have been identified as the underlying causes of land use has been involved in raising monoculture tree planta- change resulting in deforestation and land degradation tions of species such as pine which offer relatively less (Giri et al. 2003; Bolland et al. 2007). ecological and economic services to forest dependent During the 1960s the concept of vegetation mapping communities as compared to mixed broadleaved forests gained momentum, resulting in an increase in studies of (Baland et al. 2008). Another grievous cause of concern LULC change worldwide (Yang 2001). It is universally is the replacement of oak by pine in the Indian Western acknowledged that declining forest cover triggers eco- Himalayas. Thus, the forested landscapes in the state are logical problems like changes in global climate, habitat undergoing major transformations. Some LULC change degradation and unprecedented species extinction studies have been conducted in Himachal Pradesh (Goldsmith 1998). Atmospheric carbon dioxide concen- (Sharma et al. 2014, Singh et al. 2014, Chand and Cha- tration is now higher than at any time in the past 10 tranta 2014, Pareta 2014, Pareta and Pareta 2014, Rama- million years (Kennedy and Hanson 2006). It is esti- chandra et al. 2012) but none have specifically focused mated that forests (including associated soils, peat de- on the change in the forests of the region to highlight posits and lake sediments) hold 62–78 % of the world’s specific trends in species area and composition. There- terrestrial biospheric carbon (IPCC 2001). Forest clear- fore, the present study is an attempt to study land use ing is one of the major drivers of global warming and change in Solan Forest Division, to highlight species climate change. The world’s forests particularly those in wise area changes which can play an indispensible the tropics are shrinking at a dramatic rate (Apan 1999). role in developing future strategies and policies con- Anthropogenic activities involving forests, such as land cerning natural forest conservation and monoculture use change can alter the amount and temporal distribu- tree plantations. tion of C storage (Adams et al. 1993; Haynes et al. 1994). According to Achard et al. (2014), the gross loss Methods −1 of tropical forests was 8.0 million ha yr in the 1990s Study area −1 and 7.6 million ha yr in the 2000s, which resulted in The population of Himachal Pradesh as per the Census −1 −1 carbon losses of 887 MtC yr and 880 MtC yr re- of India, 2011 is 6.86 million, with a decadal growth rate spectively during the same period. It is imperative that of 12.81 %. Around 90 % of the total population lives in regular periodic assessments of forest cover change in rural areas and consequently people are highly tropical regions are carried out to recognize previous dependent upon forests for their livelihoods (Gouri et al. patterns, assist proper planning and predict future 2004). Local communities depend on forests for fuel- trends. The use of remote sensing data has been instru- wood, fodder, grazing, non-timber forest products and mental in monitoring the changing pattern of vegetation construction timber through Timber Distribution (TD) across diverse landscapes. The capacity for large geo- rights. There has been an ever increasing pressure on graphical coverage, high temporal frequency and wide the forest resources due to the rising population on one selection of spatial and spectral resolution options, fur- hand as well as limited and fragmented agricultural land ther enhances the use of remotely sensed imagery for on the other. LULC change detection. The state has a predominantly agro-horti-pastoral LULC change is primarily a local event, since the char- economy. Apart from forests around 87 % of the popu- acteristics of such change can vary dramatically from lation also depends on agriculture (Gouri et al. 2004). Shah and Sharma Forest Ecosystems (2015) 2:26 Page 3 of 12 While in the lower valleys agriculture and animal hus- Methodology bandry form the backbone of the economy, at higher al- Cloud free IRS 1D (LISS-III) satellite data were acquired titudes, agro-pastoral systems predominate. Due to a for winter months, 26 December, 1998 and 17 January, high population density per unit area of agricultural 2010 (Fig. 1), from the National Remote Sensing Agency land, and small size of landholdings– 64 % are less than (NRSA), Government of India, Hyderabad. Obtaining 1 ha (Gouri et al. 2004), agriculture which was primar- images at near anniversary dates is considered important ily for subsistence and considered an uneconomic voca- for change detection studies (Jensen, 2007). The stock tion was diversified into horticulture which has map and inventory reports available with Solan Forest relatively higher yields per unit area in terms of money Division were used to derive information on forest than other agricultural crops (Ahluwalia 1998). Tem- ranges, forest compartments and land uses. These were perate fruits like apple, pear, stone fruits, nuts; sub- further confirmed by ground truthing. The stock map tropical fruits like mango, litchi, guava, citrus fruits; was digitized for forest compartments and land use types and new fruits crops like kiwi, strawberry, olive and distributed in the different ranges of the division. Forest hazelnuts have been introduced in recent years and compartment coverage was then imported into IDRISI have sincebecomeamajor source of theexportearn- Taiga (Clark lab, Clark University, Worcester MA, USA) ings of the state. Himachal Pradesh is now known as for further processing. Toposheets (1: 25000 scale) per- the fruit bowl of India and today the agriculture sector taining to Solan Forest Division were procured from FSI provides direct employment to around 71 % of the total (Forest Survey of India) and used to digitise contours at working population (Kumar and Prashar 2012). 20 m interval using CARTALINX 1.2 data builder. The The present study was carried out in Solan Forest Digital Elevation Model (DEM) for the division was pre- Division of Himachal Pradesh, which is located be- pared by processing these cartographic features using tween 30° 45’ 00” to 31° 10’00” Nlatitude and 76° IDRISI Taiga. 55’00” to 77° 15’00’ E longitude. The area falls under Preprocessing of the images was done through atmos- subtropical region where climate varies from extreme pheric and geometric rectifications. The LISS III images hot in the lower elevations to extreme cold in higher were corrected for atmospheric path radiance using dark elevations. Temperature in lower areas ranges between object subtraction method (Chavez 1988), wherein the 15 and 36 °C and in higher areas it varies from 0 to pixel (associated with the dark object) having minimum 24 °C. Precipitation is in the form of rains mainly dur- brightness value in the near infrared band was detected ing rainy season and the area receives on an average and the corresponding pixel values in all other bands 1000 mm annual rainfall. were subtracted from the specific raw bands. This re- The forests of the division are pure and mixed stands sulted in an image corrected for atmospheric scattering. of chir pine and mostly conform to Champion and Seth The images were re-sampled for all bands using the (1968) 9C a - lower Shiwalik chir pine forests, lying be- nearest neighbor method, so that the original brightness tween an elevation of 900–2100 m a.s.l. The area above values of pixels were kept unchanged. The resultant root 1800 m a.s.l is inhabited by oak forests. Low lying areas mean squared error was found to be less than 1 pixel for show widespread population of bamboos and are also a both the images, 0.001465 pixels for the 1998 image and transitional zone for Acacia catechu. 0.000201 pixels for the 2010 image. Image enhancement a) 1998 b) 2010 30.45 30.45 77.00 77.00 Fig. 1 IRS 1D (LISS-III) satellite images a) 1998 and b) 2010 Shah and Sharma Forest Ecosystems (2015) 2:26 Page 4 of 12 techniques such as histogram stretching, median filter- probability that a class identified from the reference data ing and compositing were also applied to aid visual in- is correctly classified on the map. The KHAT statistics terpretation. While histogram stretching is a linear (an estimate of Kappa), which provides a measure of stretch technique used for adjusting image intensities to how many more pixels were correctly classified than ex- enhance contrast; median filtering aids in random noise pected by chance (Congalton and Green 1999), was removal by creating an output image in which each pix- computed through the following formula: el's value is based on its value and those of its immediate neighbors in an input image. Colour composite images 0‐E k ¼ allow us to view the reflectance information from three 1‐E separate bands in a single image, and were created by using the bands which had the greatest amount of infor- where: k represents the Kappa Index of Agreement; O mation, least redundancy and low relativity. is the observed accuracy or proportion of matching The training sites were determined and a supervised values (the matrix diagonal) i.e. the sum of the diagonal classification was performed on both images using Max- elements divided by the overall total; and E is the ex- imum Likelihood algorithm. The supervised classifica- pected proportion of matches in this diagonal i.e. the tion technique is preferred, because the data of the study sum for each diagonal cell of the row total times the col- area is available and the authors have a prior knowledge umn total divided by the overall total squared. of the area. The Maximum Likelihood decision rule is one of the most widely used supervised classification al- Results gorithms (Wu and Shao 2002; McIver and Friedl 2002). Cartographic features obtained from the stockmap of Land use classification was first done for the whole area Solan Forest Division revealed that it covers five forest of Solan Forest Division, 57,158 ha (recorded forest area + ranges namely Dharampur, Parwanoo, Solan, Kandaghat non-forest area) and then from these classified images, the and Subathu (Fig. 3). The area under these ranges was in forest compartments 13,067 ha (recorded forest area) the order, Subathu (14198 ha) > Dharampur (11564 ha) > within the division were extracted, to assess the land use Solan (11027 ha) > Parwanoo (10688 ha) > Kandaghat change in the forests of the division. As a result the land (9681 ha). Within these five forest ranges are distributed use maps depicting seven different land uses were derived. around 256 forest compartments (Fig. 4). Dharampur is The flowchart elucidating the development of land use the largest range having the maximum number of forest maps is given in Fig. 2. compartments (94), followed by Parwanoo (75), Solan A complete accuracy assessment was performed on (37), Kandaghat (35) and Subathu range (15). In these classified images of the two dates generated during this forest compartments, seven types of land uses were study. A sample of ground truth points were distributed identified. These are: 1) chir pine (Pinus roxburghii) for- randomly on the classified image. Total 450 points were ests, 2) ban oak (Quercus leucotricophora) forests 3) overlaid over the classified image to assign to each point khair (Acacia catechu) forests 4) bamboo (Dendrocala- an identifier of the land cover type. The ground truth mus strictus) forests 5) broad leaved forests 6) cultiva- data (reference data) used were collected from field sur- tion and 7) culturable blank. veys and existing stock map that had been field checked. An error matrix was established to evaluate the accuracy Accuracy assessment of the classification. This is a very effective way to repre- The user accuracy ranged between 74–93 % in 1998 sent the accuracy of the classification results, as the ac- and, 85–92 % in 2010 (Table 1). Producer accuracy curacy of each category is clearly described (Fan et al. ranged between 79–96 % in 1998 and, 86 and 92 % in 2007). Overall accuracy, user and producer accuracies 2010 (Table 1). Classification accuracy as a result of and the Kappa statistics, were derived from the error maximum likelihood classification resulted in an overall matrices. The overall accuracy was defined as the total accuracy of 87 and 89 % and KHAT accuracy of 85 and number of correctly classified pixels divided by the total 87 % in 1998 and 2010, respectively (Table 2). number of reference pixels (total number of sample points) (Rogan et al. 2002). The user accuracy was de- Changes in Land use (1998–2010) fined as the proportion of the correctly classified pixels Land use classification was first done for the whole area in a class to the total pixels that were classified in that of Solan Forest Division which is spread over 57,158 ha class. It indicates the probability that a classified pixel and includes both recorded forest area and non-forest actually represents that category in reality. The producer area (Fig. 5) and then from these classified images, the accuracy was calculated by dividing the total number of forest compartments spread over 13,067 ha within the correctly classified pixels in a class by the total number division (recorded forest area) were extracted, to assess of reference measurements of that class and it is the the land use change in the forests of the division (Fig. 6). Shah and Sharma Forest Ecosystems (2015) 2:26 Page 5 of 12 Forest stockmap Toposheets Digitization Boundaries Rivers Roads Contours (Division, Ranges, Forest compartments) Satellite data D E M Inventory Data and Field Survey IRS (LISS III)-2010 IRS (LISS III)-1998 Geometric Geometric Rectification Rectification Atmospheric Atmospheric Rectification Rectification Signature Signature Development Development Supervised Supervised Classification Classification Landuse Map Landuse Change Map Fig. 2 Flow chart of the procedure followed to develop land use maps for Solan Forest Division In 1998 the distribution of the various land use cat- oak, broadleaved, culturable blank and bamboo de- egories i.e. chirpine, culturable blank, broadleaved, bam- creased in area by 181 ha (16.58 %), 152 ha (6.30 %), boo, ban oak, cultivation and khair was over 4200, 2608, 71 ha (2.72 %) and 7 ha (0.47 %) respectively (Table 3). 2413, 1491, 1092, 934 and 329 ha area which accounted The area under forest and culturable blank decreased by for 32.14, 19.96, 18.47, 11.41, 8.36, 7.15, and 2.52 % of 72 and 71 ha respectively while that under cultivation the total area, respectively (Table 3). In 2010 chirpine, increased by 129 ha. culturable blank, broadleaved, bamboo, cultivation, ban oak and khair land use categories covered 4391, 2537, Discussion 2261, 1498, 1063, 911 and 406 ha which accounted for The computed accuracies of the classification process 33.60, 19.42, 17.30, 11.46, 8.13, 6.97 and 3.11 % of the were reasonable which can be explained by the fact that total area, respectively. Chirpine, cultivation and khair the total number of correctly classified pixels was high. increased in area by 191 ha (4.55 %), 129 ha (13.81 %) User accuracy as a result of maximum likelihood classifi- and 77 ha (23.40 %) respectively from 1998 to 2010. Ban cation ranged between 74–93 % in 1998 and 85–92 % in Shah and Sharma Forest Ecosystems (2015) 2:26 Page 6 of 12 Fig. 3 Basemap of Solan Forest Division showing the five Forest Ranges 2010; while producer accuracy ranged between 79–96 % between 83–100 %. Sharma and Leon (2005)) while in 1998 and 86–92 % in 2010. Pareta (2014) in a study working on land cover classification of Solan district on land use and land cover change in Baddi district of using IRS (LISS III) data of different seasons reported a Solan, has reported user accuracy for various lands cover user accuracy ranging between 64–89 %, 32 and 68 %, classes between 93–100 % while producer accuracy 23–62 %; and producer accuracy between 46–100 %, 33 Shah and Sharma Forest Ecosystems (2015) 2:26 Page 7 of 12 30.45 77.00 Fig. 4 Data Layer of Forest Compartments in Solan Forest Division and 74 %, 28–80 %, in summer, winter and spring data- watershed, district Solan, where the area under forest de- sets respectively, as a result of maximum likelihood clas- creased by 537.04 ha and the area under cultivation in- sification. Overall accuracy in the present study for the creased by 33.93 ha over the period 1972–2007. Pareta different land use categories was 87–89 % in 1998 and (2014) reported a decrease in the forest cover by 2 2 2010, respectively. Overall accuracies of 76 % in summer 54.95 km and an increase in cropland by 1.66 km in dataset as compared to 49 % and 46 % in winter and Baddi town of district Solan over 1990–2013. Pareta and spring datasets respectively, have been reported by Pareta (2014) reported a decline in agricultural land and 2 2 Sharma and Leon (2005). Ramachandra et al. (2012) dense forest by 41.42 km and 112 km respectively over while studying landscape dynamics of Mandhala water- 2001–2013 in Chamba district of Himachal Pradesh. shed in Solan district of Himachal Pradesh using IRS Land use pattern in Himachal Pradesh has undergone LISS III and Landsat data reported overall accuracies tremendous transformation due to changes in agricul- ranging between 78.52–86.52 %. KHAT accuracies of 85 ture cropping pattern, urbanization and industrialization. % and 87 % in 1998 and 2010 land use classifications re- Crop diversification gained momentum in the nineties spectively were computed in the present study. Com- and now covers several new areas in low and mid hill paratively lower accuracy as per 1998 classification was districts of the state (Sharma, 2011). With the integra- due to relatively higher mixing of spectral signatures be- tion of horticultural cash crops, changing land use and tween different land use categories in 1998 than in 2010. cropping patterns have evolved (Sharma et al. 2007; Sharma and Leon (2005) have reported KHAT accuracies Singh 1999). The area under horticulture in the state of 71 %, 40 % and 38 % for summer, winter and spring registered an enormous increase from 86230 ha in datasets respectively. 1980–1981 to 186900 ha in 2005–2006. In Solan the per The land use classification showed a decrease in the cent share of area under non-foodgrain crops increased area under both forest and culturable blank, and an in- from 8.25 % in 1982–1983 to over 12.96 % in 2004– crease in the area under cultivation. Similar results have 2005 (Sharma, 2011). While only about 11 % of the total been reported by Ramachandra et al. (2012), in Mandhala geographical area of the state is available for cultivation Shah and Sharma Forest Ecosystems (2015) 2:26 Page 8 of 12 Table 1 Error matrices as a result of land use classification for 1998 and 2010 Reference data 1998 Classified data Ban oak Chirpine Culturable Blank Broadleaved Bamboo Cultivation Khair Total User accuracy Ban oak 46 3 1 0 0 1 0 51 90 Chirpine 1 64 2 4 0 0 0 71 90 Culturable Blank 1 6 65 0 0 3 0 75 87 Broadleaved 1 0 3 59 2 0 3 68 87 Bamboo 1 0 0 9 48 5 2 65 74 Cultivation 0 1 4 0 0 66 0 71 93 Khair 0 1 0 3 0 0 45 49 92 Total 50 75 75 75 50 75 50 450 Producer accuracy 92 85 87 79 96 88 90 87 Reference data 2010 Ban oak 43 3 1 0 0 0 0 47 91 Chirpine 4 67 1 3 0 1 0 76 88 Culturable Blank 0 1 68 3 0 3 0 75 91 Broadleaved 1 2 1 65 2 1 2 74 88 Bamboo 0 1 0 3 46 1 3 54 85 Cultivation 2 1 4 0 0 68 0 75 91 Khair 0 0 0 1 2 1 45 49 92 Total 50 75 75 75 50 75 50 450 Producer accuracy 86 89 91 87 92 91 90 89 (Sharma, 2011), over the years growing area under horti- Rising population pressure and inappropriate policies cultural cash crops has put increasing pressure on forest of the government have further threatened forest sus- resources through land clearing (Sharma et al. 2010). In tainability and are contributing to deforestation and for- a study on forest based livelihoods in Himachal Pradesh, est degradation. Nautor is an ancient right under which Gouri et al. (2004) reported that in two of the villages landless people are permitted to break fresh agricultural where apple cultivation was adopted 35 years ago, en- land in common land areas by village elders (ODA 1993). croachment in forest areas for fruit production has re- In 1968 the Himachal Pradesh Nautor Land Rules came duced the forest cover to just 5 % in Kiari and 3 % in into force whereby the government started granting nau- Dhadi Rawat village. In Kullu district of Himachal, en- tor land (redistributed land) upto one acre to landless and croachment of apple orchards into un-demarcated pro- other eligible people for agriculture and horticulture tected forest and demarcated protected forest is a (Chowdhry, 2008). The un-demarcated forest is the land common practice (ODA 1993). The use of wood for fruit that was designated for allocation under nautor rights. packing cases also resulted in extensive deforestation in This practice of giving away un-demarcated forest land to the state (Singh 1992). Up to mid-1980s, 0.2 million m landless cultivators under the provisions of the said rules of wood was extracted to make 20 million boxes annu- has resulted in deforestation in the Himalayan state ally for packaging of fruits and vegetables (Stokes, 1983). (Gupta 2007). Another policy in the history of forest man- Alternative packaging materials like corrugated card- agement in Himachal is the Timber Distribution (TD) sys- board are being experimented with to deal with the tem under which landowners or right holders claim rights problem of forest exploitation; however fruit growers to timber, primarily to meet house construction or repair have issues with the quality of the boxes. needs. This policy while meeting the basic needs of the local population has also been the single largest reason for timber harvest in the western Himalayan region Table 2 Comparison of two classification accuracy measures for (Vasan 1998) due to the rampant misuse of these rights. two dates Although the system was directed towards villagers, TD Classification Overall accuracy (%) KHAT accuracy (%) rights extended in urban areas as well. While previously 1998 87 85 the right to timber was unlimited it was later restricted 2010 89 87 to one or two trees once every five years. However, Shah and Sharma Forest Ecosystems (2015) 2:26 Page 9 of 12 a) 1998 b) 2010 30.45 30.45 77.00 77.00 Fig. 5 Land use classification based on Minimum distance classifier for the whole area of Solan Forest Division – recorded forest area plus non-forest area a) 1998 and b) 2010 people were still paying the same nominal amount fixed annually in the state under the TD rights, being si- in the last century which was about 20 % of the market phoned off to timber industries (Gouri et al. 2004). This rate at the time of the initial forest settlements (Vasan policy was later reviewed in light of a strong opposition, 1998) i.e. a right holder could buy a tree for a subsi- and the Himachal Pradesh Forest (Timber Distribution dized rate as low as Rs 3 to 5. In 1992–1993 the subsidy to the Right Holders) Rules, 2013 were notified on 26 provided by the forest department under TD was to the December, 2013, which enhanced both the rates and tune of Rs.795,600,000 (DFFC 1994). In time, the trad- periodicity for grant of TD rights. The government has itional timber distribution system was overrun by a also been funding massive monoculture plantation drives timber mafia with approximately 150,000 m of timber across the state to arrest forest degradation which how- (against a total growing stock of 96.8 million m )carry- ever is able to generate relatively less ecological and eco- ing a market value of around Rs 750 million, extracted nomic services to forest dependent communities when a) 1998 b) 2010 30.45 30.45 77.00 77.00 Fig. 6 Land use classification based on Minimum distance classifier for forest compartments extracted from Solan Forest Division – recorded forest area a) 1998 and b) 2010 Shah and Sharma Forest Ecosystems (2015) 2:26 Page 10 of 12 Table 3 Territorial (compartment wise) Land use change in 17.64 % of the total area under plantations, and over Solan Forest Division 1998–2001 period, there has been reported an increase Category Area under Land Use (ha) % Change by over 7497 ha in the area under this species (Planning in Land Commission 2005). 1998 2010 use Area Conversely, a decrease in the area of ban oak may be Cultivation 934 1063 129 (+) attributed to heavy anthropogenic pressure on oak for- (7.15) (8.13) (13.81) ests in hill states which results in indiscriminate lopping Culturable Blank 2608 2537 71(−) of the species. Ban oak is a multipurpose species having a good fodder and fuel quality; hence, it is under a high (19.96) (19.42) (2.72) biotic stress in Himalayan forests (Saxena et al. 1978; Forest Land use Tiwari and Singh 1982; Bankoti et al. 1986; Joshi and Chir Pine 4200 4391 191 (+) Tiwari, 2011). Gouri et al. (2004), reported scarcity of (32.14) (33.6) (4.55) fodder in Himachal Pradesh due to factors like forest Ban oak 1092 911 181 (−) fire, decrease in grasslands and mounting land utilisation (8.36) (6.97) (16.58) pressure due to the adoption of horticulture. This has resulted in people keeping fewer cattle thereby affecting Broad Leaved 2413 2261 152 (−) the availability of cow dung and natural manure. As a re- (18.47) (17.30) (6.30) sult the fuel requirements which were to some extent Khair 329 406 77 (+) met through cow dung cakes, are now solely dependent (2.52) (3.11) (23.40) on forests (Gouri et al. 2004) such as those of oak which Bamboo 1491 1498 7 (−) are facing the problem of severe degradation. The total (11.41) (11.46) (0.47) annual consumption of rural domestic fuel in the state is around 2.5 to 3.2 million tones, half of which is ex- Total 13,067 13,067 tracted from public forests (Singh and Sikka 1992). De- Figures in parenthesis are in percent crease in the area of ban oak may also be due to poor compared to mixed broadleaved forests and so the vicious regeneration observed in these forests compared to for- cycle of forest degradation has remained unchecked. Such ests of chir pine in ground surveys of the study area. policies have a profound impact on forested landscapes According to Bhandari et al. (1997), the regeneration and play a pivotal role in change of land use. potential of Pinus roxburghii is greater than that of Within the forest land use category, chirpine and khair Quercus species. showed an increase in area, while ban oak, broadleaved In the present study it is pertinent to observe that the and bamboo decreased in area over 1998–2010 period. increase in the area of pine namely 191 ha, is compar- Both chir pine and khair have been the primary planta- able with the decline in the area of oak namely 181 ha. tion species of the state, where the focus has revolved This may be due to the replacement of oak by pine more around commercially important species rather which has become an ever increasing phenomenon in than ecologically valuable ones. An increase in the area the Western Himalayas (Singh et al. 1984). The exploit- of chir pine over the period 1998–2010 may be attrib- ive management practices exerted by hill population in uted to an increase in the area under plantations of chir oak forests have encouraged pine (Saxena et al.1984). pine. This species is being tapped for resin and is the lar- Forests in the Indian Himalayas are burnt periodically gest species raised under plantations in Himachal Pra- by the local communities to encourage the growth of desh constituting about 30.60 % of the total area under grasses. This increases the preponderance of fire-resistant plantations and it registered an increase in area of species such as chir pine. The aggressiveness of chir pine and its capacity to colonize disturbed sites have enabled it 8265 ha over 1998–2001 (Planning Commission 2005). Also profuse regeneration under chir pine forests was to spread at the expense of ban oak forests which are observed during ground surveys of the study area. An under immense biotic pressure (Singh and Singh 1984). A decrease in the area of broadleaved forests which increase in the area of khair may also be attributable to an increase in the area under plantations of khair which includes species such as Terminalia belerica, T. che- are a commercial source of kattha and cutch. While kat- bula, Dalbergia sissoo, Pyrus pashia, Albizzia chinensis, Juglans regia and Celtis australis, has also been ob- tha is used in betel-vine preparations normally referred to as paan, cutch is a by-product of the kattha industry served in the present study, primarily due to the an- used for dyeing, colouring pulp in paper industry, water thropogenic pressure of the forest fringe communities. According to Baland et al., 2008, broad-leaved forests softening and in deep oil drilling operations. Khair is the second single largest species being raised under tend to be more useful and consequently more de- plantations in the state after chirpine constituting graded than coniferous forests. The former are of Shah and Sharma Forest Ecosystems (2015) 2:26 Page 11 of 12 greater utility to adjacent villages due to the superior Authors’ information 1) SS participated in this research as a doctoral student. Currently she is quality of firewood and fodder, and are also a source of working in the Department of Forestry, College of Agriculture, Fisheries and several non-timber forest products. The decrease in the Forestry, Fiji National University, Nasinu, Fiji Islands. area under bamboo forests may be attributed to pressure 2) DP is a professor of Silviculture at Dr. Y.S. Parmar University of Horiculture and Forestry, Solan, Himachal Pradesh, India. on bamboo for its commercial purposes. In Himachal Pradesh, bamboo forests are considered economically very important and support local livelihoods. Several Acknowledgements Authors are grateful to Forest Division Solan, Himachal Pradesh for providing villagers generate income from them by making use of necessary help and guidance. bamboo in basket making (Gouri et al. 2004). Author details Department of Forestry, College of Agriculture, Fisheries and Forestry, Fiji Conclusion National University, NasinuP.O. Box 7222, Fiji Islands. Department of Land use change reflects the role of human activities on Silviculture and Agroforestry, Dr. Y.S. Parmar University of Horiculture and natural resources and the environment. Analysis of the Forestry, Solan, Himachal Pradesh, India. spatial and temporal pattern of land use and assessment Received: 23 June 2015 Accepted: 24 September 2015 of the key driving factors behind the associated changes is imperative for sustainable use of land and its re- sources. 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Land use change detection in Solan Forest Division, Himachal Pradesh, India

Shah, Shipra; Sharma, DP
"Forest Ecosystems" , Volume 2 (1): 12 – Dec 1, 2015
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2015 Shah and Sharma.
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Abstract

Background: Monitoring the changing pattern of vegetation across diverse landscapes through remote sensing is instrumental in understanding the interactions of human activities and the ecological environment. Land use pattern in the state of Himachal Pradesh in the Indian Western Himalayas has been undergoing rapid modifications due to changing cropping patterns, rising anthropogenic pressure on forests and government policies. We studied land use change in Solan Forest Division of Himachal Pradesh to assess species wise area changes in the forests of the region. Methods: The supervised classification (Maximum likelihood) on two dates of IRS (LISS III) satellite data was performed to assess land use change over the period 1998–2010. Results: Seven land use categories were identified namely, chir pine (Pinus roxburghii) forest, broadleaved forest, bamboo (Dendrocalamus strictus)forest, banoak (Quercus leucotrichophora)forest, khair(Acacia catechu)forest, culturable blank and cultivation. The area under chir pine, cultivation and khair forests increased by 191 ha (4.55 %), 129 ha (13.81 %) and 77 ha (23.40 %), whereas the area under ban oak, broadleaved, culturable blank and bamboo decreased by 181 ha (16.58 %), 152 ha (6.30 %), 71 ha (2.72 %) and 7 ha (0.47 %), respectively. Conclusions: The study revealed a decrease in the area under forest and culturable blank categories and a simultaneous increase in the area under cultivation primarily due to the large scale introduction of horticultural cash crops in the state. The composition of forests alsoexhibited some majorchanges,withanincreaseinthe area of commercially important monoculture plantation species such as pine and khair, and a decline in the area of oak, broadleaved and bamboo which are facing a high anthropogenic pressure in meeting the livelihood demands of forest dependent communities. In time deforestation, forest degradation and ecological imbalances due to the changing forest species composition may inflict irreversible damages upon unstable and fragile mountain zones such as the Indian Himalayas. The associated common property externalities involved at local, regional and global scales, necessitate the monitoring of land use dynamics across forested landscapes in developing future strategies and policies concerning agricultural diversification, natural forest conservation and monoculture tree plantations. Keywords: Land use; Solan Forest Division; Supervised Classification; Maximum likelihood * Correspondence: drshiprashah1984@gmail.com Department of Forestry, College of Agriculture, Fisheries and Forestry, Fiji National University, NasinuP.O. Box 7222, Fiji Islands Full list of author information is available at the end of the article © 2015 Shah and Sharma. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Shah and Sharma Forest Ecosystems (2015) 2:26 Page 2 of 12 Background one region to another. In order to capture the unique- Very few landscapes remain on Earth that have not been ness of each region it is necessary that LULC studies are significantly altered by the human race in one way or an- conducted at local to regional scales to better under- other (Yang 2001). Land use land cover (LULC) change stand the cumulative impacts at multiple scales (Sohl et is the primary modifier of landscapes, affecting a wide al. 2004). Over the years land use and cropping patterns range of socioeconomic, biological, climatic, and hydro- in the western Himalayan state of Himachal Pradesh logical systems (Sohl and Sohl 2012). Understanding the have been changing. The expansion of cultivation at the spatial extent and distribution of LULC change is vital to expense of forests has been relatively more profound in the study of environmental changes at various levels Himachal Pradesh than other parts of India (Melkania (Ojima et al. 1994). LULC change is one of the key and Melkania 1987). The introduction of horticultural drivers of local and regional climate change (Chase et al. cash crops and the commercialization of agriculture 1999), biodiversity decline (Sala et al. 2000), soil degrad- have placed greater demands on the forests (Gouri et al. ation (Tolba and El-Kholy 1992) and the loss of ecosys- 2004) of the state which have not only been undergoing tem services, thereby affecting the ability of ecosystems a change in area but also a change in composition pri- to support human needs (Vitousek et al. 1997). Popula- marily due to human interference (Deshingkar et al. tion growth, rapid economic development and poverty 1997). To arrest forest degradation the state government have been identified as the underlying causes of land use has been involved in raising monoculture tree planta- change resulting in deforestation and land degradation tions of species such as pine which offer relatively less (Giri et al. 2003; Bolland et al. 2007). ecological and economic services to forest dependent During the 1960s the concept of vegetation mapping communities as compared to mixed broadleaved forests gained momentum, resulting in an increase in studies of (Baland et al. 2008). Another grievous cause of concern LULC change worldwide (Yang 2001). It is universally is the replacement of oak by pine in the Indian Western acknowledged that declining forest cover triggers eco- Himalayas. Thus, the forested landscapes in the state are logical problems like changes in global climate, habitat undergoing major transformations. Some LULC change degradation and unprecedented species extinction studies have been conducted in Himachal Pradesh (Goldsmith 1998). Atmospheric carbon dioxide concen- (Sharma et al. 2014, Singh et al. 2014, Chand and Cha- tration is now higher than at any time in the past 10 tranta 2014, Pareta 2014, Pareta and Pareta 2014, Rama- million years (Kennedy and Hanson 2006). It is esti- chandra et al. 2012) but none have specifically focused mated that forests (including associated soils, peat de- on the change in the forests of the region to highlight posits and lake sediments) hold 62–78 % of the world’s specific trends in species area and composition. There- terrestrial biospheric carbon (IPCC 2001). Forest clear- fore, the present study is an attempt to study land use ing is one of the major drivers of global warming and change in Solan Forest Division, to highlight species climate change. The world’s forests particularly those in wise area changes which can play an indispensible the tropics are shrinking at a dramatic rate (Apan 1999). role in developing future strategies and policies con- Anthropogenic activities involving forests, such as land cerning natural forest conservation and monoculture use change can alter the amount and temporal distribu- tree plantations. tion of C storage (Adams et al. 1993; Haynes et al. 1994). According to Achard et al. (2014), the gross loss Methods −1 of tropical forests was 8.0 million ha yr in the 1990s Study area −1 and 7.6 million ha yr in the 2000s, which resulted in The population of Himachal Pradesh as per the Census −1 −1 carbon losses of 887 MtC yr and 880 MtC yr re- of India, 2011 is 6.86 million, with a decadal growth rate spectively during the same period. It is imperative that of 12.81 %. Around 90 % of the total population lives in regular periodic assessments of forest cover change in rural areas and consequently people are highly tropical regions are carried out to recognize previous dependent upon forests for their livelihoods (Gouri et al. patterns, assist proper planning and predict future 2004). Local communities depend on forests for fuel- trends. The use of remote sensing data has been instru- wood, fodder, grazing, non-timber forest products and mental in monitoring the changing pattern of vegetation construction timber through Timber Distribution (TD) across diverse landscapes. The capacity for large geo- rights. There has been an ever increasing pressure on graphical coverage, high temporal frequency and wide the forest resources due to the rising population on one selection of spatial and spectral resolution options, fur- hand as well as limited and fragmented agricultural land ther enhances the use of remotely sensed imagery for on the other. LULC change detection. The state has a predominantly agro-horti-pastoral LULC change is primarily a local event, since the char- economy. Apart from forests around 87 % of the popu- acteristics of such change can vary dramatically from lation also depends on agriculture (Gouri et al. 2004). Shah and Sharma Forest Ecosystems (2015) 2:26 Page 3 of 12 While in the lower valleys agriculture and animal hus- Methodology bandry form the backbone of the economy, at higher al- Cloud free IRS 1D (LISS-III) satellite data were acquired titudes, agro-pastoral systems predominate. Due to a for winter months, 26 December, 1998 and 17 January, high population density per unit area of agricultural 2010 (Fig. 1), from the National Remote Sensing Agency land, and small size of landholdings– 64 % are less than (NRSA), Government of India, Hyderabad. Obtaining 1 ha (Gouri et al. 2004), agriculture which was primar- images at near anniversary dates is considered important ily for subsistence and considered an uneconomic voca- for change detection studies (Jensen, 2007). The stock tion was diversified into horticulture which has map and inventory reports available with Solan Forest relatively higher yields per unit area in terms of money Division were used to derive information on forest than other agricultural crops (Ahluwalia 1998). Tem- ranges, forest compartments and land uses. These were perate fruits like apple, pear, stone fruits, nuts; sub- further confirmed by ground truthing. The stock map tropical fruits like mango, litchi, guava, citrus fruits; was digitized for forest compartments and land use types and new fruits crops like kiwi, strawberry, olive and distributed in the different ranges of the division. Forest hazelnuts have been introduced in recent years and compartment coverage was then imported into IDRISI have sincebecomeamajor source of theexportearn- Taiga (Clark lab, Clark University, Worcester MA, USA) ings of the state. Himachal Pradesh is now known as for further processing. Toposheets (1: 25000 scale) per- the fruit bowl of India and today the agriculture sector taining to Solan Forest Division were procured from FSI provides direct employment to around 71 % of the total (Forest Survey of India) and used to digitise contours at working population (Kumar and Prashar 2012). 20 m interval using CARTALINX 1.2 data builder. The The present study was carried out in Solan Forest Digital Elevation Model (DEM) for the division was pre- Division of Himachal Pradesh, which is located be- pared by processing these cartographic features using tween 30° 45’ 00” to 31° 10’00” Nlatitude and 76° IDRISI Taiga. 55’00” to 77° 15’00’ E longitude. The area falls under Preprocessing of the images was done through atmos- subtropical region where climate varies from extreme pheric and geometric rectifications. The LISS III images hot in the lower elevations to extreme cold in higher were corrected for atmospheric path radiance using dark elevations. Temperature in lower areas ranges between object subtraction method (Chavez 1988), wherein the 15 and 36 °C and in higher areas it varies from 0 to pixel (associated with the dark object) having minimum 24 °C. Precipitation is in the form of rains mainly dur- brightness value in the near infrared band was detected ing rainy season and the area receives on an average and the corresponding pixel values in all other bands 1000 mm annual rainfall. were subtracted from the specific raw bands. This re- The forests of the division are pure and mixed stands sulted in an image corrected for atmospheric scattering. of chir pine and mostly conform to Champion and Seth The images were re-sampled for all bands using the (1968) 9C a - lower Shiwalik chir pine forests, lying be- nearest neighbor method, so that the original brightness tween an elevation of 900–2100 m a.s.l. The area above values of pixels were kept unchanged. The resultant root 1800 m a.s.l is inhabited by oak forests. Low lying areas mean squared error was found to be less than 1 pixel for show widespread population of bamboos and are also a both the images, 0.001465 pixels for the 1998 image and transitional zone for Acacia catechu. 0.000201 pixels for the 2010 image. Image enhancement a) 1998 b) 2010 30.45 30.45 77.00 77.00 Fig. 1 IRS 1D (LISS-III) satellite images a) 1998 and b) 2010 Shah and Sharma Forest Ecosystems (2015) 2:26 Page 4 of 12 techniques such as histogram stretching, median filter- probability that a class identified from the reference data ing and compositing were also applied to aid visual in- is correctly classified on the map. The KHAT statistics terpretation. While histogram stretching is a linear (an estimate of Kappa), which provides a measure of stretch technique used for adjusting image intensities to how many more pixels were correctly classified than ex- enhance contrast; median filtering aids in random noise pected by chance (Congalton and Green 1999), was removal by creating an output image in which each pix- computed through the following formula: el's value is based on its value and those of its immediate neighbors in an input image. Colour composite images 0‐E k ¼ allow us to view the reflectance information from three 1‐E separate bands in a single image, and were created by using the bands which had the greatest amount of infor- where: k represents the Kappa Index of Agreement; O mation, least redundancy and low relativity. is the observed accuracy or proportion of matching The training sites were determined and a supervised values (the matrix diagonal) i.e. the sum of the diagonal classification was performed on both images using Max- elements divided by the overall total; and E is the ex- imum Likelihood algorithm. The supervised classifica- pected proportion of matches in this diagonal i.e. the tion technique is preferred, because the data of the study sum for each diagonal cell of the row total times the col- area is available and the authors have a prior knowledge umn total divided by the overall total squared. of the area. The Maximum Likelihood decision rule is one of the most widely used supervised classification al- Results gorithms (Wu and Shao 2002; McIver and Friedl 2002). Cartographic features obtained from the stockmap of Land use classification was first done for the whole area Solan Forest Division revealed that it covers five forest of Solan Forest Division, 57,158 ha (recorded forest area + ranges namely Dharampur, Parwanoo, Solan, Kandaghat non-forest area) and then from these classified images, the and Subathu (Fig. 3). The area under these ranges was in forest compartments 13,067 ha (recorded forest area) the order, Subathu (14198 ha) > Dharampur (11564 ha) > within the division were extracted, to assess the land use Solan (11027 ha) > Parwanoo (10688 ha) > Kandaghat change in the forests of the division. As a result the land (9681 ha). Within these five forest ranges are distributed use maps depicting seven different land uses were derived. around 256 forest compartments (Fig. 4). Dharampur is The flowchart elucidating the development of land use the largest range having the maximum number of forest maps is given in Fig. 2. compartments (94), followed by Parwanoo (75), Solan A complete accuracy assessment was performed on (37), Kandaghat (35) and Subathu range (15). In these classified images of the two dates generated during this forest compartments, seven types of land uses were study. A sample of ground truth points were distributed identified. These are: 1) chir pine (Pinus roxburghii) for- randomly on the classified image. Total 450 points were ests, 2) ban oak (Quercus leucotricophora) forests 3) overlaid over the classified image to assign to each point khair (Acacia catechu) forests 4) bamboo (Dendrocala- an identifier of the land cover type. The ground truth mus strictus) forests 5) broad leaved forests 6) cultiva- data (reference data) used were collected from field sur- tion and 7) culturable blank. veys and existing stock map that had been field checked. An error matrix was established to evaluate the accuracy Accuracy assessment of the classification. This is a very effective way to repre- The user accuracy ranged between 74–93 % in 1998 sent the accuracy of the classification results, as the ac- and, 85–92 % in 2010 (Table 1). Producer accuracy curacy of each category is clearly described (Fan et al. ranged between 79–96 % in 1998 and, 86 and 92 % in 2007). Overall accuracy, user and producer accuracies 2010 (Table 1). Classification accuracy as a result of and the Kappa statistics, were derived from the error maximum likelihood classification resulted in an overall matrices. The overall accuracy was defined as the total accuracy of 87 and 89 % and KHAT accuracy of 85 and number of correctly classified pixels divided by the total 87 % in 1998 and 2010, respectively (Table 2). number of reference pixels (total number of sample points) (Rogan et al. 2002). The user accuracy was de- Changes in Land use (1998–2010) fined as the proportion of the correctly classified pixels Land use classification was first done for the whole area in a class to the total pixels that were classified in that of Solan Forest Division which is spread over 57,158 ha class. It indicates the probability that a classified pixel and includes both recorded forest area and non-forest actually represents that category in reality. The producer area (Fig. 5) and then from these classified images, the accuracy was calculated by dividing the total number of forest compartments spread over 13,067 ha within the correctly classified pixels in a class by the total number division (recorded forest area) were extracted, to assess of reference measurements of that class and it is the the land use change in the forests of the division (Fig. 6). Shah and Sharma Forest Ecosystems (2015) 2:26 Page 5 of 12 Forest stockmap Toposheets Digitization Boundaries Rivers Roads Contours (Division, Ranges, Forest compartments) Satellite data D E M Inventory Data and Field Survey IRS (LISS III)-2010 IRS (LISS III)-1998 Geometric Geometric Rectification Rectification Atmospheric Atmospheric Rectification Rectification Signature Signature Development Development Supervised Supervised Classification Classification Landuse Map Landuse Change Map Fig. 2 Flow chart of the procedure followed to develop land use maps for Solan Forest Division In 1998 the distribution of the various land use cat- oak, broadleaved, culturable blank and bamboo de- egories i.e. chirpine, culturable blank, broadleaved, bam- creased in area by 181 ha (16.58 %), 152 ha (6.30 %), boo, ban oak, cultivation and khair was over 4200, 2608, 71 ha (2.72 %) and 7 ha (0.47 %) respectively (Table 3). 2413, 1491, 1092, 934 and 329 ha area which accounted The area under forest and culturable blank decreased by for 32.14, 19.96, 18.47, 11.41, 8.36, 7.15, and 2.52 % of 72 and 71 ha respectively while that under cultivation the total area, respectively (Table 3). In 2010 chirpine, increased by 129 ha. culturable blank, broadleaved, bamboo, cultivation, ban oak and khair land use categories covered 4391, 2537, Discussion 2261, 1498, 1063, 911 and 406 ha which accounted for The computed accuracies of the classification process 33.60, 19.42, 17.30, 11.46, 8.13, 6.97 and 3.11 % of the were reasonable which can be explained by the fact that total area, respectively. Chirpine, cultivation and khair the total number of correctly classified pixels was high. increased in area by 191 ha (4.55 %), 129 ha (13.81 %) User accuracy as a result of maximum likelihood classifi- and 77 ha (23.40 %) respectively from 1998 to 2010. Ban cation ranged between 74–93 % in 1998 and 85–92 % in Shah and Sharma Forest Ecosystems (2015) 2:26 Page 6 of 12 Fig. 3 Basemap of Solan Forest Division showing the five Forest Ranges 2010; while producer accuracy ranged between 79–96 % between 83–100 %. Sharma and Leon (2005)) while in 1998 and 86–92 % in 2010. Pareta (2014) in a study working on land cover classification of Solan district on land use and land cover change in Baddi district of using IRS (LISS III) data of different seasons reported a Solan, has reported user accuracy for various lands cover user accuracy ranging between 64–89 %, 32 and 68 %, classes between 93–100 % while producer accuracy 23–62 %; and producer accuracy between 46–100 %, 33 Shah and Sharma Forest Ecosystems (2015) 2:26 Page 7 of 12 30.45 77.00 Fig. 4 Data Layer of Forest Compartments in Solan Forest Division and 74 %, 28–80 %, in summer, winter and spring data- watershed, district Solan, where the area under forest de- sets respectively, as a result of maximum likelihood clas- creased by 537.04 ha and the area under cultivation in- sification. Overall accuracy in the present study for the creased by 33.93 ha over the period 1972–2007. Pareta different land use categories was 87–89 % in 1998 and (2014) reported a decrease in the forest cover by 2 2 2010, respectively. Overall accuracies of 76 % in summer 54.95 km and an increase in cropland by 1.66 km in dataset as compared to 49 % and 46 % in winter and Baddi town of district Solan over 1990–2013. Pareta and spring datasets respectively, have been reported by Pareta (2014) reported a decline in agricultural land and 2 2 Sharma and Leon (2005). Ramachandra et al. (2012) dense forest by 41.42 km and 112 km respectively over while studying landscape dynamics of Mandhala water- 2001–2013 in Chamba district of Himachal Pradesh. shed in Solan district of Himachal Pradesh using IRS Land use pattern in Himachal Pradesh has undergone LISS III and Landsat data reported overall accuracies tremendous transformation due to changes in agricul- ranging between 78.52–86.52 %. KHAT accuracies of 85 ture cropping pattern, urbanization and industrialization. % and 87 % in 1998 and 2010 land use classifications re- Crop diversification gained momentum in the nineties spectively were computed in the present study. Com- and now covers several new areas in low and mid hill paratively lower accuracy as per 1998 classification was districts of the state (Sharma, 2011). With the integra- due to relatively higher mixing of spectral signatures be- tion of horticultural cash crops, changing land use and tween different land use categories in 1998 than in 2010. cropping patterns have evolved (Sharma et al. 2007; Sharma and Leon (2005) have reported KHAT accuracies Singh 1999). The area under horticulture in the state of 71 %, 40 % and 38 % for summer, winter and spring registered an enormous increase from 86230 ha in datasets respectively. 1980–1981 to 186900 ha in 2005–2006. In Solan the per The land use classification showed a decrease in the cent share of area under non-foodgrain crops increased area under both forest and culturable blank, and an in- from 8.25 % in 1982–1983 to over 12.96 % in 2004– crease in the area under cultivation. Similar results have 2005 (Sharma, 2011). While only about 11 % of the total been reported by Ramachandra et al. (2012), in Mandhala geographical area of the state is available for cultivation Shah and Sharma Forest Ecosystems (2015) 2:26 Page 8 of 12 Table 1 Error matrices as a result of land use classification for 1998 and 2010 Reference data 1998 Classified data Ban oak Chirpine Culturable Blank Broadleaved Bamboo Cultivation Khair Total User accuracy Ban oak 46 3 1 0 0 1 0 51 90 Chirpine 1 64 2 4 0 0 0 71 90 Culturable Blank 1 6 65 0 0 3 0 75 87 Broadleaved 1 0 3 59 2 0 3 68 87 Bamboo 1 0 0 9 48 5 2 65 74 Cultivation 0 1 4 0 0 66 0 71 93 Khair 0 1 0 3 0 0 45 49 92 Total 50 75 75 75 50 75 50 450 Producer accuracy 92 85 87 79 96 88 90 87 Reference data 2010 Ban oak 43 3 1 0 0 0 0 47 91 Chirpine 4 67 1 3 0 1 0 76 88 Culturable Blank 0 1 68 3 0 3 0 75 91 Broadleaved 1 2 1 65 2 1 2 74 88 Bamboo 0 1 0 3 46 1 3 54 85 Cultivation 2 1 4 0 0 68 0 75 91 Khair 0 0 0 1 2 1 45 49 92 Total 50 75 75 75 50 75 50 450 Producer accuracy 86 89 91 87 92 91 90 89 (Sharma, 2011), over the years growing area under horti- Rising population pressure and inappropriate policies cultural cash crops has put increasing pressure on forest of the government have further threatened forest sus- resources through land clearing (Sharma et al. 2010). In tainability and are contributing to deforestation and for- a study on forest based livelihoods in Himachal Pradesh, est degradation. Nautor is an ancient right under which Gouri et al. (2004) reported that in two of the villages landless people are permitted to break fresh agricultural where apple cultivation was adopted 35 years ago, en- land in common land areas by village elders (ODA 1993). croachment in forest areas for fruit production has re- In 1968 the Himachal Pradesh Nautor Land Rules came duced the forest cover to just 5 % in Kiari and 3 % in into force whereby the government started granting nau- Dhadi Rawat village. In Kullu district of Himachal, en- tor land (redistributed land) upto one acre to landless and croachment of apple orchards into un-demarcated pro- other eligible people for agriculture and horticulture tected forest and demarcated protected forest is a (Chowdhry, 2008). The un-demarcated forest is the land common practice (ODA 1993). The use of wood for fruit that was designated for allocation under nautor rights. packing cases also resulted in extensive deforestation in This practice of giving away un-demarcated forest land to the state (Singh 1992). Up to mid-1980s, 0.2 million m landless cultivators under the provisions of the said rules of wood was extracted to make 20 million boxes annu- has resulted in deforestation in the Himalayan state ally for packaging of fruits and vegetables (Stokes, 1983). (Gupta 2007). Another policy in the history of forest man- Alternative packaging materials like corrugated card- agement in Himachal is the Timber Distribution (TD) sys- board are being experimented with to deal with the tem under which landowners or right holders claim rights problem of forest exploitation; however fruit growers to timber, primarily to meet house construction or repair have issues with the quality of the boxes. needs. This policy while meeting the basic needs of the local population has also been the single largest reason for timber harvest in the western Himalayan region Table 2 Comparison of two classification accuracy measures for (Vasan 1998) due to the rampant misuse of these rights. two dates Although the system was directed towards villagers, TD Classification Overall accuracy (%) KHAT accuracy (%) rights extended in urban areas as well. While previously 1998 87 85 the right to timber was unlimited it was later restricted 2010 89 87 to one or two trees once every five years. However, Shah and Sharma Forest Ecosystems (2015) 2:26 Page 9 of 12 a) 1998 b) 2010 30.45 30.45 77.00 77.00 Fig. 5 Land use classification based on Minimum distance classifier for the whole area of Solan Forest Division – recorded forest area plus non-forest area a) 1998 and b) 2010 people were still paying the same nominal amount fixed annually in the state under the TD rights, being si- in the last century which was about 20 % of the market phoned off to timber industries (Gouri et al. 2004). This rate at the time of the initial forest settlements (Vasan policy was later reviewed in light of a strong opposition, 1998) i.e. a right holder could buy a tree for a subsi- and the Himachal Pradesh Forest (Timber Distribution dized rate as low as Rs 3 to 5. In 1992–1993 the subsidy to the Right Holders) Rules, 2013 were notified on 26 provided by the forest department under TD was to the December, 2013, which enhanced both the rates and tune of Rs.795,600,000 (DFFC 1994). In time, the trad- periodicity for grant of TD rights. The government has itional timber distribution system was overrun by a also been funding massive monoculture plantation drives timber mafia with approximately 150,000 m of timber across the state to arrest forest degradation which how- (against a total growing stock of 96.8 million m )carry- ever is able to generate relatively less ecological and eco- ing a market value of around Rs 750 million, extracted nomic services to forest dependent communities when a) 1998 b) 2010 30.45 30.45 77.00 77.00 Fig. 6 Land use classification based on Minimum distance classifier for forest compartments extracted from Solan Forest Division – recorded forest area a) 1998 and b) 2010 Shah and Sharma Forest Ecosystems (2015) 2:26 Page 10 of 12 Table 3 Territorial (compartment wise) Land use change in 17.64 % of the total area under plantations, and over Solan Forest Division 1998–2001 period, there has been reported an increase Category Area under Land Use (ha) % Change by over 7497 ha in the area under this species (Planning in Land Commission 2005). 1998 2010 use Area Conversely, a decrease in the area of ban oak may be Cultivation 934 1063 129 (+) attributed to heavy anthropogenic pressure on oak for- (7.15) (8.13) (13.81) ests in hill states which results in indiscriminate lopping Culturable Blank 2608 2537 71(−) of the species. Ban oak is a multipurpose species having a good fodder and fuel quality; hence, it is under a high (19.96) (19.42) (2.72) biotic stress in Himalayan forests (Saxena et al. 1978; Forest Land use Tiwari and Singh 1982; Bankoti et al. 1986; Joshi and Chir Pine 4200 4391 191 (+) Tiwari, 2011). Gouri et al. (2004), reported scarcity of (32.14) (33.6) (4.55) fodder in Himachal Pradesh due to factors like forest Ban oak 1092 911 181 (−) fire, decrease in grasslands and mounting land utilisation (8.36) (6.97) (16.58) pressure due to the adoption of horticulture. This has resulted in people keeping fewer cattle thereby affecting Broad Leaved 2413 2261 152 (−) the availability of cow dung and natural manure. As a re- (18.47) (17.30) (6.30) sult the fuel requirements which were to some extent Khair 329 406 77 (+) met through cow dung cakes, are now solely dependent (2.52) (3.11) (23.40) on forests (Gouri et al. 2004) such as those of oak which Bamboo 1491 1498 7 (−) are facing the problem of severe degradation. The total (11.41) (11.46) (0.47) annual consumption of rural domestic fuel in the state is around 2.5 to 3.2 million tones, half of which is ex- Total 13,067 13,067 tracted from public forests (Singh and Sikka 1992). De- Figures in parenthesis are in percent crease in the area of ban oak may also be due to poor compared to mixed broadleaved forests and so the vicious regeneration observed in these forests compared to for- cycle of forest degradation has remained unchecked. Such ests of chir pine in ground surveys of the study area. policies have a profound impact on forested landscapes According to Bhandari et al. (1997), the regeneration and play a pivotal role in change of land use. potential of Pinus roxburghii is greater than that of Within the forest land use category, chirpine and khair Quercus species. showed an increase in area, while ban oak, broadleaved In the present study it is pertinent to observe that the and bamboo decreased in area over 1998–2010 period. increase in the area of pine namely 191 ha, is compar- Both chir pine and khair have been the primary planta- able with the decline in the area of oak namely 181 ha. tion species of the state, where the focus has revolved This may be due to the replacement of oak by pine more around commercially important species rather which has become an ever increasing phenomenon in than ecologically valuable ones. An increase in the area the Western Himalayas (Singh et al. 1984). The exploit- of chir pine over the period 1998–2010 may be attrib- ive management practices exerted by hill population in uted to an increase in the area under plantations of chir oak forests have encouraged pine (Saxena et al.1984). pine. This species is being tapped for resin and is the lar- Forests in the Indian Himalayas are burnt periodically gest species raised under plantations in Himachal Pra- by the local communities to encourage the growth of desh constituting about 30.60 % of the total area under grasses. This increases the preponderance of fire-resistant plantations and it registered an increase in area of species such as chir pine. The aggressiveness of chir pine and its capacity to colonize disturbed sites have enabled it 8265 ha over 1998–2001 (Planning Commission 2005). Also profuse regeneration under chir pine forests was to spread at the expense of ban oak forests which are observed during ground surveys of the study area. An under immense biotic pressure (Singh and Singh 1984). A decrease in the area of broadleaved forests which increase in the area of khair may also be attributable to an increase in the area under plantations of khair which includes species such as Terminalia belerica, T. che- are a commercial source of kattha and cutch. While kat- bula, Dalbergia sissoo, Pyrus pashia, Albizzia chinensis, Juglans regia and Celtis australis, has also been ob- tha is used in betel-vine preparations normally referred to as paan, cutch is a by-product of the kattha industry served in the present study, primarily due to the an- used for dyeing, colouring pulp in paper industry, water thropogenic pressure of the forest fringe communities. According to Baland et al., 2008, broad-leaved forests softening and in deep oil drilling operations. Khair is the second single largest species being raised under tend to be more useful and consequently more de- plantations in the state after chirpine constituting graded than coniferous forests. The former are of Shah and Sharma Forest Ecosystems (2015) 2:26 Page 11 of 12 greater utility to adjacent villages due to the superior Authors’ information 1) SS participated in this research as a doctoral student. Currently she is quality of firewood and fodder, and are also a source of working in the Department of Forestry, College of Agriculture, Fisheries and several non-timber forest products. The decrease in the Forestry, Fiji National University, Nasinu, Fiji Islands. area under bamboo forests may be attributed to pressure 2) DP is a professor of Silviculture at Dr. Y.S. Parmar University of Horiculture and Forestry, Solan, Himachal Pradesh, India. on bamboo for its commercial purposes. In Himachal Pradesh, bamboo forests are considered economically very important and support local livelihoods. Several Acknowledgements Authors are grateful to Forest Division Solan, Himachal Pradesh for providing villagers generate income from them by making use of necessary help and guidance. bamboo in basket making (Gouri et al. 2004). Author details Department of Forestry, College of Agriculture, Fisheries and Forestry, Fiji Conclusion National University, NasinuP.O. Box 7222, Fiji Islands. Department of Land use change reflects the role of human activities on Silviculture and Agroforestry, Dr. Y.S. Parmar University of Horiculture and natural resources and the environment. Analysis of the Forestry, Solan, Himachal Pradesh, India. spatial and temporal pattern of land use and assessment Received: 23 June 2015 Accepted: 24 September 2015 of the key driving factors behind the associated changes is imperative for sustainable use of land and its re- sources. 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Journal

"Forest Ecosystems" – Springer Journals

Published: Dec 1, 2015

Keywords: Ecology; Ecosystems; Forestry

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