Environ Monit Assess DOI 10.1007/s10661-008-0717-4 Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia P. Rama Chandra Prasad · Nidhi Nagabhatla · C. S. Reddy · Stutee Gupta · K. S. Rajan · S. H. Raza · C. B. S. Dutt Received: 9 July 2008 / Accepted: 23 December 2008 © Springer Science + Business Media B.V. 2009 Abstract The present study outlines an approach interpretation for the complete archipelago. In the to classify forest density and to estimate canopy second step, we identified two island groups from closure of the forest of the Andaman and the Andaman to investigate and compare the for- Nicobar archipelago. The vector layers generated est strata density. The third and final step involved for the study area using satellite data was vali- more of a localised phytosociological module that dated with the field knowledge of the surveyed focused on the North Andaman Islands. The re- ground control points. The methodology adopted sults based on the analysis of the high-resolution in this present analysis is three-tiered. First, the satellite data show that more than 75% of the density stratification into five zones using visual mangroves are under high- to very high-density canopy class. The framework developed would serve as a significant measure to forest health and evaluate management concerns whilst addressing B · P. R. C. Prasad ( ) K. S. Rajan issues such as gap identification, conservation pri- Laboratory for Spatial Informatics, International Institute of Information Technology, Gachibowli, oritisation and disaster management—principally Hyderabad 500 032, India to the post-tsunami assessment and analysis. e-mail: [email protected] Keywords Tropical · Islands · Forest · N. Nagabhatla · · · World Fish Centre, Batu Muang, Penang, Malaysia Canopy density South Asia Geospatial Management C. S. Reddy Forestry and Ecology Division, National Remote Sensing Agency, Department of Space, Balanagar, Hyderabad, India Introduction S. Gupta Tropical rainforests are bestowed with a wide Forest Research Institute, Dehradun, India variety of species richness and diversity patterns S. H. Raza (Alwyn and Calaway 1987; Jacobs 1988). Apart Aurora’s Scientific Technological & Research from these characteristics, they are recognised as Academy, Hyderabad, India dense forests due to the high density of vegetation formed by the clumped distribution of individ- C. B. S. Dutt Indian Space Research Organization, Department uals and also enormously tangled undergrowth of Space, Anthariksh Bhavan, Bangalore, India of different herbs, shrubs, lianas and climbers. Environ Monit Assess Detailed and accurate maps of these forests— parameters (forest canopy density, habitat diver- condition and structure—are needed for assess- sity, etc.). The satellite remote sensing is best ment of the flora and faunal biodiversity as well as suited for analysis of canopy closures, as elu- for sustainable ecosystem management (Blodgett cidated by Roy et al. (1994) whilst modelling et al. 2000). The conventional way of ground mon- the biophysical spectral response for forest den- itoring for density estimation can be tedious and sity stratification in evergreen forests of South time-consuming, whilst the use of geographical Andaman division and dry deciduous forests over information system (GIS) and remote sensing as a central India. The study was based on the as- platform for estimating the density of these forests sumption that any alteration in forest canopy is may speed up the process and provide for a more reflected in the crown structure. efficient option (Blodgett et al. 2000). Addition- With inspiration from the above statement, ally, the remote sensing data also facilitate spatial this study illustrates the density analysis and the delineation of vegetation density maps through associated attributes for the tropical Andaman various techniques using satellite imagery in con- and Nicobar (AN) archipelago, the islands char- junction with GIS and phytosociological ground acterised by notable ecological complexity, using data (Chauhan 2004; Prasad 2006). In addition, a three-tier approach. The biodiversity of these these data techniques can be combined for spatial islands is noteworthy and the detail assessment prediction and modelling of the vegetation’s bio- poses a challenge. Past extraction practices, over- physical properties (Muukkonen and Heiskanen exploitation of natural resources, and population 2005). pressure has seriously affected the ecosystem sta- Forest canopy density is one of the important bility and forest density. Currently, these forests parameters in the planning and implementation are prone to anthropogenic disturbances due to of forest rehabilitation programs (Rikimaru et al. the high immigration rates and the introduction 2002). It has been suggested that canopy density is of various exotic animals like elephants (Rauf an essential parameter to assess and analyse the 2004). This paper, whilst addressing the need to factors affecting forest growth, its regeneration understand the canopy closure or density of forest and to keep a check on management initiatives communities which are critical for a management in gap area plantations and regeneration status planning process, also describes the use of the spa- (Chauhan 2004). Further, as discussed by Blodgett tial tools viz., a combination of earth observation et al. (2000), the multi-spectral earth observation data along with the GIS tools that can pertinently data with appropriate ground measurements can contribute to the process. Towards this end, the suitably extrapolate across a large geographic re- authors have mapped the forest canopy density as gion, and this has significant advantages for for- part of the first tier for the complete archipelago, est management, especially in areas where forests whilst the second tier takes up an in-depth analysis are in remote locations or are inaccessible. Also, of two islands in the Andaman group—North and the spatial techniques to measure the canopy clo- Baratang Islands (BI) and the final tier relates the sure along with canopy height profiling has been phytodiversity and the density stratification for illustrated from different regions using a wide the North Andaman Islands (NAI). array of spatial and spectral resolutions (Roy et al. It is envisioned that the derived thematic out- 1990, 1996; Rikimaru and Miyatake 1997;Katul puts will provide a synoptic understanding of the and Albertson 1998; Harding et al. 2001; Marshall extent of open and closed forests in the island et al. 2002; Singh et al. 2003). landscape. The detailed analysis of the two islands will depict the scenario of forest density individu- Background ally, for each forest type, thereby providing infor- mation on forest stock which can enable selective Advancement in geospatial technologies provides logging operations by the forest department. In a medium to evaluate forest cover in the inaccessi- addition, the other outputs may assist in the iden- ble and remote isles. This can also enable a multi- tification of illegal logging sites and the extent of scalar assessment of the associated ecosystem forest fragmentation. Environ Monit Assess Study area and objectives into different forest density zones. At the second level, the goal is to understand the variation in The Andaman and Nicobar Islands (ANI) spreads the forest canopy closures in a continuous land over the geographic location of 6◦5 to 14◦45 N mass (NAI) vs. scattered islands (BI). These two lat. and 92◦ to 94◦ E long and vertically stretch tiers of the geospatial assignment encapsulate the over 800 km in the Bay of Bengal with a total statistics to decipher the dissimilarity and overlaps area of 8,249 km2. Nicobar Islands are a group of in the forest communities of the ANI in general 28 islands (total area, 1,841 km2) separated from and the two zones in detail both in terms of com- the Andaman group (6,408 km2) by a 10◦ channel. position and health. In the third tier, an effort ANI are one among the nation’s richest biodiver- has been made for in-depth analysis of the forest sity regions and contains a great assemblage of density patterns vis-a-vis phytodiversity for the endemic plant species coupled with high species NAI. diversity and density (Roy et al. 2005; Prasad et al. 2007a; Nagabhatla and Roy 2007). The second tier of the study focuses on NAI (12◦95 Nand 92◦86 E) and BI (12◦18 Nand92◦32 E) of Materials and method AN archipelago; NAI constitutes a continuous land mass (1,458 km2) surrounded by few small Satellite data islands like Paget, Point, Smith, Landfall, Inter- view and Narcondam, whilst the BI is more of Indian Remote Sensing Satellite (IRS) 1C/1D Lin- scattered (small/medium-sized islands separated ear Imaging Self Scanner-III (LISS III; 23.5-m res- by straits) landscape with 28 islands, among which olution), Panchromatic (PAN; 5.8-m resolution) the chief are Baratang, Evergreen, Colebrook, and merged LISS-PAN (multi-spectral 5.8 m) Spike, Havelock, Peel, Wilson, Henry Lawrence, datasets were used. Wherever IRS-1C LISS- John Lawrence, Outram and Neil. The islands III cloud-free data were not available, Landsat- have been selected with the purpose of studying Thematic Mapper (TM) data were schematically the variance in density and diversity in two varied referred. The datasets used in this study are listed landscapes. in Table 1 with relevant spectral and spatial res- Predominantly five major vegetation types, viz. olution. Further, the reconnaissance