Are phenological variations in natural teak (Tectona grandis) forests of India governed by rainfall? A remote sensing based investigation.

Monitoring and assessment of vegetation phenology at the regional to global scale are essential to understand the characteristics of various biophysical parameters in terrestrial ecosystems. Passive optical remote sensing data have been used extensively in the recent past to study phenology of vegetation, also called land surface phenology, at diverse landscapes across the globe. In the present study, the moderate resolution imaging spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) time series data (2000-2013) was used to study the phenology of dry and moist teak (Tectona grandis) forests of different biogeographic provinces of India. Four phenology metrics, viz., start of season (SOS), end of season (EOS), peak of season (POS) and length of season (LOS) were derived using the TIMESAT tool. The SOSs' of dry and moist teak were found during July-August. LOS of moist teak was found to be much longer (~ 48 days) than dry teak. Also, a significant difference of leaf area index (LAI) (~ 2.8) of dry and moist teak forests was noticed during peak season from MODIS LAI product (MOD15A2). Vegetation phenology is greatly responsive to the fluctuation of climatic parameters such as rainfall. Hence, pre-season cumulative rainfall data were analysed to understand the control of rainfall over phenological variations in natural teak forests of India. It was noticed that rainfall was reasonably well correlated with SOS (R2 = 0.57-0.72) for both types of teak forests. The study highlighted the efficacy of time series MODIS EVI data to study the phenological variations in different teak forest types of India in a data-limited situation.

How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)?

Invasive species and climate change are considered as the most serious global environmental threats. In this study, we investigated the influence of projected global climate change on the potential distribution of one of the world's most successful invader weed, bushmint (Hyptis suaveolens (L.) Poit.). We used spatial data on 20 environmental variables at a grid resolution of 5 km, and 564 presence records of bushmint from its native and introduced range. The climatic profiles of the native and invaded sites were analyzed in a multi-variate space in order to examine the differences in the position of climatic niches. Maximum Entropy (MaxEnt) model was used to predict the potential distribution of bushmint using presence records from entire range (invaded and native) along with 14 eco-physiologically relevant predictor variables. Subsequently, the trained MaxEnt model was fed with Hadley Centre Coupled Model (HadCM3) climate projections to predict potential distribution of bushmint by the year 2050 under A2a and B2a emission scenarios. MaxEnt predictions were very accurate with an Area Under Curve (AUC) value of 0.95. The results of Principal Component Analysis (PCA) indicated that climatic niche of bushmint on the invaded sites is not entirely similar to its climatic niche in the native range. A vast area spread between 34 ° 02' north and 28 ° 18' south latitudes in tropics was predicted climatically suitable for bushmint. West and middle Africa, tropical southeast Asia, and northern Australia were predicted at high invasion risk. Study indicates enlargement, retreat, or shift across bushmint's invasion range under the influence of climate change. Globally, bushmint's potential distribution might shrink in future with more shrinkage for A2a scenario than B2a. The study outcome has immense potential for undertaking effective preventive/control measures and long-term management strategies for regions/countries, which are at higher risk of bushmint's invasion.

Growing stock and woody biomass assessment in Asola-Bhatti Wildlife Sanctuary, Delhi, India.

Biomass is an important entity to understand the capacity of an ecosystem to sequester and accumulate carbon over time. The present study, done in collaboration with the Delhi Forest Department, focused on the estimation of growing stock and the woody biomass in the so-called lungs of Delhi--the Asola-Bhatti Wildlife Sanctuary in northern Aravalli hills. The satellite-derived vegetation strata were field-inventoried using stratified random sampling procedure. Growing stock was calculated for the individual sample plots using field data and species-specific volume equations. Biomass was estimated from the growing stock and the specific gravity of the wood. Among the four vegetation types, viz. Prosopis juliflora, Anogeissus pendula, forest plantation and the scrub, the P. juliflora was found to be the dominant vegetation in the area, covering 23.43 km(2) of the total area. The study revealed that P. juliflora forest with moderate density had the highest (10.7 m(3)/ha) while A. pendula forest with moderate density had the lowest (3.6 m(3)/ha) mean volume. The mean woody biomass was also found to be maximum in P. juliflora forest with moderate density (10.3 t/ha) and lowest in A. pendula forest with moderate density (3.48 t/ha). The total growing stock was estimated to be 20,772.95 m(3) while total biomass worked out to be 19,366.83 t. A strong correlation was noticed between the normalized difference vegetation index (NDVI) and the growing stock (R(2) = 0.84)/biomass (R(2) = 0.88). The study demonstrated that growing stock and the biomass of the woody vegetation in Asola-Bhatti Wildlife Sanctuary could be estimated with high accuracy using optical remote sensing data.

Identification of swamp deer (Cervus duvauceli duvauceli Cuvier) potential habitat in Jhilmil Jheel Conservation Reserve, Uttarakhand, India using multi-criteria analysis.

The present study aims to identify the potential habitat for swamp deer (Cervus duvauceli duvauceli Cuvier) in Jhilmil Jheel Conservation Reserve in the Uttarakhand province of India using multi-criteria analysis. The study area represents one of the last remnant habitats of the flagship species, the swamp deer in Uttarakhand, which is considered as vulnerable. The study showed that only 6.08% of the study area (225 km(2)) was highly suitable to suitable for the swamp deer. An area of 135.52 km(2) (60.23%) turned out to be moderately suitable. Within the officially designated Conservation Reserve (area 37.84 km(2)), 10.91% (4.13 km(2)) area was found highly suitable to suitable, while 74.19% (28.07 km(2)) happens to be moderately suitable. Only 14 km(2) area, which was found as suitable habitat for swamp deer falls short of the space required by a population of 134 animals. The problem could be mitigated if the agricultural land (2.47 km(2)) adjacent to the Jhilmil Jheel is brought under the Reserve management. This would provide additional area to meet the fodder requirement. The study brings out a particularly grim situation with limited options for conservation and management of the swamp deer in the Indo-Gangetic plains. It also emphasizes the role of geospatial techniques in quick appraisal of habitat attributes and identification of potential sites for protected areas.