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Materials and Methods
3.1. Study Area
The WG is one among 36 global biodiversity hotspots (https://www.conservation.org/) and 8 hottest hotspots of biodiversity (https://www.iucn.org/) with the exceptional endemic flora and fauna. The region is endowed with 4600 species of flowering plants (38% endemics), 330 butterflies (11% endemics), 156 reptiles (62% endemics), 508 birds (4% endemics), 120 mammals (12% endemics), 289fishes (41% endemics) and 135 amphibians (75% endemics). It covers an area of approximately 160,000 sq. km and extends from 8° Nto21° N latitudes and 73° Eto77°E longitudes. It is considered as a water tower of India due to numerous streams originates and draining millions of hectares. The rivers of WG sustain water ensuring water and food security of 245 + million people in the peninsular Indian states. WG has 261 persons per square kilometer located in six states under such as Kerala, Tamil Nadu, Karnataka, Goa, Maharashtra, Gujrat and Dadra and Nagar Haveli (Union Territory). The region has tropical evergreen forests, moist deciduous forests, scrub jungles, sholas, savannas, including the high rainfall savannas, of which 10% of the forest area is under protection legally. The region has 261 persons per square kilometer density of population. Areca nut, coconut, coffee, tea, rubber, spices, paddy, sugarcane, cereals and cotton are major agriculture and horticulture products grown across the regions. The prime forests are being transformed to other land-uses from past 4 decades due to commercial establishments, hydroelectric projects, industries and monoculture plantations (Fig. 1).
Figure 1. Study Area—Western Ghats, India.
3.2.Methodology
The approach followed to quantify carbon sequestration potential of WG and climate variation, as portrayed in Fig. 2, included
(i) spatial analyses of land-use dynamics and modeling,
(ii) assessment of carbon sequestration potential of forests ecosystems in WG, and
(iii) assessment of climate variability with land-use dynamics.
Figure 2. Method used for the analysis..
3.2.1. Quantification and Modeling of Land-use Changes
The land-use analysis is performed by using the Landsat 8 Operational Land Imager (OLI-30 m resolution) 2018 data integrated with field estimations and decadal land-use (1985, 1995, 2005–100 m resolution) available from International Geosphere-Biosphere Programme (IGBP). The collateral data included the vegetation maps developed by French institute Puducherry, topographic maps (the Survey of India) and virtual earth data (Google Earth, Bhuvan). GPS- and AGPS-based field surveys were done in order to supplement land-use analysis and geometric correction. The process of classification involves the following. Firstly, creation of false color composite (FCC) using three bands of Landsat data, which helped in identifying the heterogeneous regions and the selection of training sites. Secondly, collection of attribute data fromthe field for the training polygons and virtual data (Google Earth, Bhuvan). Thirdly, land-use classification information derivation from RS data through Gaussian maximum likelihood algorithm using training data. Finally, accuracy assessment through computation of error matrix (confusion matrix) and kappa statistics. The Gaussian maximum likelihood classifier (GMLC) is proved to be an efficient supervised classification technique for deriving 8 different land-use categories from RS data (Vinay et al. 2013;Bharath et al. 2014; Ramachandra et al. 2016;Ramachandra and Bharath 2018) using free and open source GRASS GIS (Geographical Analysis Support System—http://wgbis.ces.iisc.ernet.in/grass/). The training data (60%) collected have been used for classification, while the alance for accuracy assessment and validation (Lillesand et al. 2014). The forest fragmentation has been analyzed using two prime indicators such as Pf and P ff(cardinal direction) (Riitters et al. 2002; Ramachandra et al. 2016). These were computed as per Eqs. (1)and(2), respectively, through a moving window of 5*5 pixels in order to maintain a fair representation of the proportion as given that the results of the model are scale dependent and threshold dependent (Riitters et al. 2000, 2002;Kue as et al. 2011). Water bodies or river courses are considered non-fragmenting features and constitute natural corridors in a forested landscape, while anthropogenic landscape elements (such as buildings, roads, agricultural field, and barren land) are drivers of forest fragmentation.
The constrained Cellular Automata and Markov chain (CA-Markov) has been used for the simulation of likely land-use (2031). The CA-Markov modeling technique has been widely used and efficient technique for land-use simulation across the globe due to its simplicity and predictive power (Fuet al. 2018). CA-Markov uses the spatial arrangement, states, neighborhood, rules of transition, temporal scale of cells for enhanced simulation (Arsanjani et al. 2013). Water bodies or river courses and protected areas are considered as constraints of land-use change. The transition probability and area metrics were generated and used for the simulation as outlined in Bharath et al. (2014).
3.2.2. Carbon Sequestration Potential of Forests and their Future Status
The carbon sequestration potential of forest ecosystems was assessed based on
(i) published literature based on the standard biomass experiments and
(ii) field-based measurements collected across the forests of WG of Karnataka using transect-based quadrat sampling techniques (Fig. 3).
The field estimations were done across the varied forest types, which cover ˜300 transects in Uttara Kannada, Shimoga, Chikmagalur, Kodagu, Dakshina Kannada and Udupi districts. The WG is divided into 5' * 5'grids (2300) for biomass and carbon estimation. The biomass was estimated using GBH (girth at breast height) for the trees >30 cm. The transect data and standard literature data were used for the quantification.
Figure 3. Transect cum quadrat method.
The Western Ghats consists of three diverse agro-climatic zones, namely
(i) coastal,
(ii) Sahyadri interior and
(iii) Plains. Probable relationship between basal area (BA) and forest cover and extent of interior forest given in Eq. (3) is based on the field data (standing biomass) coupled with land-use data (land-use—forests and interior forest/contiguous forests from the fragmentation analyses).
The multivariate statistical analysis was done for estimating the relationship between a dependen(standing biomass) and independent variables (BA, forest cover, percentage of interior forests-computed from land-use analysis). The standing biomass and carbon stock in each grid of Western Ghats region were quantified as per Eq. (3).
Standing biomass = F {basal area, interior forest, forest cover} (3)
Statistically significant equations based on the BA with land-use and interior forest were obtained and given in Eqs. (4), (5) and (6), respectively, for coastal, Sahyadri and plains. Validation of BA based on Eqs. (2)to(4) was done with the known BA (collected through field sampling) in the respective grids. Later, BA (Table 2) for all grids in the coast, Sahyadri interior and plains were computed considering forest land-use and interior forests (in the respective grids) using Eqs. (4), (5) and (6).
Coastal regions:
BA = {30.1 + (0.0414 * (forest land use)+ 0.053 * (interior forest))}; n = 50, SE = 6.2 (4)
Sahyadri interior regions:
BA = {39.1 + (-0.099* (forest land use)+ 0.091 * (interior forest))}; n = 55, SE = 6.3 (5)
Plain regions:
BA = {34.8 + (-0.186 * (forest land use)+ 0.12 * (interior forest))}; n = 11, SE = 5.5 (6)
where n is a number of transects and SE refers to standard error. Comparisons of predicted BA (as per Eqs. (2), (3)and(4) for different agro-climatic regions) and BA quantified from the field showed a reasonable agreement with the coefficient of determination (R) of 0.878 and standard error of 11.73. Parameters such as annual increment of biomass (standing biomass) and carbon were evaluated based on field measurement and the review of literatures based on forest types as listed in Table 1. Carbon storage in forests is estimated by taking 50% of the biomass as carbon. The carbon is stored in the soil as soil organic matter (SOM) in both organic and inorganic forms. Soil carbon is calculated based on the field estimations in top 30 cm soil for different forests and extrapolated for all grids as per Table 2. Sequestered carbon and annual increment of carbon have been computed considering various land-use types as per Table 3.
| Forest type |
Standing biomass (t/ha) |
| Dense evergreen to semi-evergreen |
485.67–833.22 |
| Low evergreen |
4226.55 |
| Dense deciduous |
258.12 |
| Degraded deciduous |
129.92 |
| Savanna woodlands |
74.25–90 |
| Thorn degraded |
40 |
| Littoral and swamp |
213.8 |
Table 1. Above-ground biomass for various forest types Source: Rai and Proctor (1986),Ramachandra et al. (2000a, b), Chandran et al. (2010), Ramachandra et al. (2010), Rao et al. (2013), Ramachandra et al. (2014), Ramachandra and Bharath (2019)
| Forest type |
Mean soil carbon in top 30 cm (t/ha) |
| Tropical wet evergreen forest |
132.8 |
| Tropical semi-evergreen forest |
171.75 |
| Tropical moist deciduous forest |
57.14 |
| Littoral and swamp forest |
34.9 |
| Tropical dry deciduous forest |
58 |
| Tropical thorn forest |
44 |
| Tropical dry evergreen forest |
33 |
Table 2. Soil carbon storage in different forest types Source: Swamy (1992), Ravindranath et al. (1997), Ravindranath and Ostwald (2008), Ramachandra and Bharath (2019)
| Index |
Equation |
Significance |
Forest type |
| Biomass (t/ha) |
(Forest cover) * 485.67
(Forest cover) * 258.12
(Forest cover) * 74.25 |
Above-ground biomass content |
Evergreen
Deciduous
Scrub |
| Carbon stored (t/ha) |
(Estimated biomass) * 0.5 |
Sequestered carbon content |
All |
| Annual increment in biomass (t/ha) |
(Forest cover) * 10.48
(Forest cover) * 13.82
(Forest cover) * 5.4 |
Incremental growth in biomass (Ramachandra et al. 2000b; Pandey et al. 2011; Do et al. 2018) |
Evergreen
Deciduous
Scrub |
| Annual increment in carbon (t/ha) |
(Annual increment in Biomass) * 0.5 |
Incremental growth in carbon storage |
All |
| Net annual biomass productivity (t/ha) |
(Forest cover) * 3.6
(Forest cover) * 3.9
(Forest cover) *0.5 |
Used to compute the annual availability of woody biomass in the region (Ramachandra et al. 2000b) |
Evergreen
Deciduous
Scrub |
| Carbon sequestration of forest soil (t/ha) |
(Forest cover) * 132.8
(Forest cover) * 58
(Forest cover) * 44 |
Carbon stored in soil (Ravindranath et al. 1997) |
Evergreen
Deciduous
Scrub |
| Annual increment in carbon (t/ha) |
(Forest cover) * 2.5 |
Annual increment in carbon stored in the soil |
All |
Table 2. Soil carbon storage in different forest types Source: Swamy (1992), Ravindranath et al. (1997), Ravindranath and Ostwald (2008), Ramachandra and Bharath (2019)
3.2.3. Assessment of Climate Variability and Trend with Land-Use Changes
Daily rainfall and temperature data (of 0.50° resolution) for the period 1901 and 2017 were collated from Princeton University Database (Princeton data—THRG 2019), NCAR climate data guide (NCAR 2019), Indian Meteorological Department (IMD 2018) and Local climate data (Karnataka State Natural Resource Disaster Monitoring Centre(KSNDMC 2018)). Princeton data and NCAR datawere validated at latitude levels by comparison with the surface measurements of IMD and local climate data. The mean and variance were computed and compared, which illustrates the global data are comparable to surface measurements (with deviation £ 4.3%) These climatic data at latitude level were further analyzed for variability and trend. Climate (rainfall and temperature) data were compared with land-use changes to understand the role of land-use in the regional climate variability.
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