Results

3.1. Spatio temporal land use changes

KNP region has witnessed an increase in deciduous forest and scrub forest cover by 1.27% and 3.04% respectively at the cost of evergreen forest (reduction by 6.24%) during 1973e2016 (Fig. 4A). The increase in commercial plantation cover (4.48%) in buffer region and mining of iron ore (Kudremukh Iron Ore Company) are major agents of changes in the forest cover. Lakya dam was constructed (for the operational purpose of mining industry- Kudremukh Iron Ore Company) which has further submerged some of the forest area. RTR region has lost deciduous forest from 52.72% to 42.77% (1973e2016), with an increase in agriculture cover 6.29%, built-up area by 0.35% (Fig. 4B). The open area has increased by 2.14% due to increase in rampant grazing activities and overexploitation of natural resources. Water bodies show an increase from 0.36% to 2.01% due to the construction of Taraka dam in 1984. BTR shows an increase of 18.71% scrub forest cover with a sharp decline of deciduous cover from 61.69 to 47.30% (Fig. 4C). The ever increasing human intervention in the form of tourism activities, plantations of teak, eucalyptus and frequent forest fires responsible for greater transition in this region. The human settlements in the buffer region contribute to 1.08% of the total region. Overgrazing has created more forest blanks (open spaces) in the core and also in the buffer region (increase in open areas by 2.16%), and these regions are infested with weeds such as Parthenium hysterophorus (Congress weed), Chromolaena odorata (Devil weed) affecting native flora of BTR. Fig. 5 (AeC) provides the relative share of temporal land uses in KNP, RTR, BTR during four decades.

Fig. 4. (AeC): Temporal land use analysis for KNP, RTR, BTR from 1973 to 2016

Fig. 5. (AeC): Variations in land use across three PAs for 1973, 1992 and 2016.

3.2. The role of LU changes on LST

Temperature and the mean temperature across the region was analyzed pixel-wise for 5 broad categories -
(i) Vegetation includes evergreen forest, deciduous forest, scrub forest and forest plantations
(ii) Agriculture includes horticulture and agriculture
(iii)water bodies (lakes, streams, rivers)
(iv) Built-up
(v) Open area (fallow land), by reclassifying earlier 9 land use categories (Figs. 4and 5)
. Thermal signatures of different LU types in the study area helped in understanding the role of respective LU category to heat phenomenon. In KNP, the average temperature across the National Park has increased by around 4 °C due to the loss of evergreen forest which has allowed the soil beneath the canopy to get exposed to sunlight and absorb more heat, leading to a rise in mean temperature. The dam has a lot of deposits of dumped iron ore waste which has led to the rise in temperature of the water body as well. The mean vegetation temperature is increased by around 9 °C in RTR due to removal of native forest and increase in teak, eucalyptus plantations, which exposes the ground to direct sunlight. This region contributes to a highest change in temperature among the study area. BTR shows that there is an increase of around 1 °C in the region during the 1992e2016 due to transition of forest cover. RTR and BTR areas are infested by various non-native species which has led to a reduction in water retaining capability of the soil with rise in temperature. There were incidences of forest fire (natural and man-made), due to which the surface gets exposed and heats up more which further increases the temperature. The increase in temperature has also enhanced the chances of forest fire again, as it is a cyclic process (Vlassova et al., 2014). The intensified LULC changes induces prolonged drought, which can trigger large-scale landscape changes through vegetation mortality due to water stress (Fensham and Holman, 1999; Ruthrof et al., 2016), loss of productivity (Cooper et al., 2017) and land surface conditions for decades (Breshears et al., 2005; Kulakowski et al., 2011).

3.3. Modelling and visualization of land use

Modelling and visualization of LU dynamics in 3 PAs were analyzed using CA-Markov and validation was done through Kappa statistics. Transition probability matrices were generated for PAs as shown in Table 1, simulated and current land use maps were validated. Fig. 6 shows the simulated 2016 and predicted land uses for 2026. Table 2 shows the percentage of various land use categories. Modelling LU in KNP region shows evergreen forest will continue to degrade and will cover only 23.04% by 2026. The same trend continues for deciduous with loss of 6%. Commercial plantations in the region will increase tremendously and will cover almost 1/3rd of the total region. Deciduous forest will cover 40.16% with increases in scrub forest cover by 2026 in RTR region. Agricultural and Horticultural activities are predicted to remain same. Evergreen forests show a slight increase as it is present on the hill tops, thus making it difficult for humans to intervene. BTR region also would experience the degradation of forests with 3.21% reduction in deciduous cover by 2026. Scrub forest will be the second land use after deciduous forest to dominate the region and would cover 31%. All these regions depict an increase in built-up area and horticulture, which signifies increased human interventions, and the continued forest vegetation degradation. This will have a cascading impact with increments in temperature, disturbing the biodiversity, species regeneration, etc. and may lead to vegetation die-off. It will be difficult for the sustenance of native cover in changed environment leading to an extinction of endemic species. Implementation of conservation norms with rigid protection measures, enrichment of degraded forest patches with the native vegetation, enriching the Gomala Lands (grass lands) are necessary to sustain natural resources in PAs. The government needs to take appropriate mitigation measures in the animal movement paths by
(i) creating water bodies
(ii) growing fodder crops
(iii) restrictions of inappropriate crops
(iv) eviction of an unauthorized occupation of forest lands.
Human interferences are evident either through diversions of natural drains or senseless commercial crops, which have further complicated the conservation of endangered fauna (see Fig. 7).

Table 1 Transition probability matrix for KNP for 1992 2016.

Fig. 6. (AeC): Temporal pixel level LST across PAs.

Fig. 7. (AeC): Simulated and predicted land uses in PAs.

Table 2 Simulated and predicted Land use details of PAs for 2016 & 2026.