Moderate resolution LAI prediction using Sentinel-2 satellite data and indirect field measurements in Sikkim Himalaya.

The leaf area index (LAI) has been traditionally used as a photosynthetic variable. LAI plays an essential role in forest cover monitoring and has been identified as one of the important climate variables. However, due to challenges in field sampling, complex topography, and availability of cloud-free optical satellite data, LAI assessment on larger scale is still unexplored in the Sikkim Himalayan area. We used two optical instruments, digital hemispherical photography (DHP) and LAI-2200C, to assess the LAI across four different forests following 20 × 20 m2 elementary sampling units (ESUs) in the Himalayan state of Sikkim, India. The use of Sentinel-2 derived vegetation indices (VIs) demonstrated a better correlation with the DHP based LAI estimates than using LAI-2200C. Further, the combination of both reflectance bands and VIs were integrated to predict the LAI maps using random forest model. The temperate evergreen forests demonstrated the highest LAI value, while the predicted maps exhibited LAI maxima of 3.4. The estimated vs predicted LAI for DHP and LAI-2200C based estimation demonstrated reasonably good (R2 = 0.63 and R2 = 0.68, respectively) agreement. Further, improvements on the LAI prediction can be attempted by minimizing errors from the inherent field protocols, optimizing the density of field measurements, and representing heterogeneity. The recent rise of frequent forest fires in Sikkim Himalaya prompts for better understanding of fuel load in terms of surface fuel or canopy fuel that can be linked to LAI. The high-resolution LAI map could serve as input to forest fuel bed characterization, especially in seasonal forests with significant variations in green leaves and litter, thereby offering inputs for forest management in changing climate.

Land-use and land-cover change in Western Ghats of India.

The Western Ghats (WG) of India, one of the hottest biodiversity hotspots in the world, has witnessed major land-use and land-cover (LULC) change in recent times. The present research was aimed at studying the patterns of LULC change in WG during 1985-1995-2005, understanding the major drivers that caused such change, and projecting the future (2025) spatial distribution of forest using coupled logistic regression and Markov model. The International Geosphere Biosphere Program (IGBP) classification scheme was mainly followed in LULC characterization and change analysis. The single-step Markov model was used to project the forest demand. The spatial allocation of such forest demand was based on the predicted probabilities derived through logistic regression model. The R statistical package was used to set the allocation rules. The projection model was selected based on Akaike information criterion (AIC) and area under receiver operating characteristic (ROC) curve. The actual and projected areas of forest in 2005 were compared before making projection for 2025. It was observed that forest degradation has reduced from 1985-1995 to 1995-2005. The study obtained important insights about the drivers and their impacts on LULC simulations. To the best of our knowledge, this is the first attempt where projection of future state of forest in entire WG is made based on decadal LULC and socio-economic datasets at the Taluka (sub-district) level.

Experience with telepathology at a tertiary cancer centre and a rural cancer hospital.

BACKGROUND: Telepathology allows quick and timely access to an expert opinion, no matter where the patient is located. We analysed the experience with the use of telepathology between a tertiary cancer centre and a rural cancer hospital. METHODS: Ninety-three cases were analysed in which static telepathology was used to obtain a consultation between Tata Memorial Centre and Nargis Dutt Memorial Cancer Hospital at Barshi, a rural area. RESULTS: Successful teleconsultation was achieved in all cases. A diagnosis was offered in 92 cases (98.9%) and was deferred in 1 case (1.1%). Complete concordance, clinically unimportant minor discrepancy and hedged diagnosis were obtained in 83 cases (90.2%). Major discrepancies were encountered in 9 cases (9.7%). The number of images per case ranged from 3 to 27 (average: 7 images). Images were of diagnosable quality in 89.2% of cases. Most of the cases (77.4%) were reported within 3 days; 32.2% were reported within 8 hours (a single working day) and 45.1% within 1-3 days. CONCLUSION: Telemedicine can be effectively used to bridge the gap between medically underprivileged, geographically distant rural areas and advanced centres using the static store and forward methodology.