Mining impacts on forest cover change in a tropical forest using remote sensing and spatial information from 2001-2019: A case study of Odisha (India).

Worldwide mining activities are one of the major anthropogenic activities that have caused high forest cover loss (FCL). In this study, we have quantified FCL in Odisha State due to mining activities analyzing Hansen Global Forest Change (HGFC) time series data for the period of 2001-2019 in Google Earth Engine platform. Our analysis suggests that Nabarangpur, Puri, Kendrapara, and Kalahandi districts lost more than 20% of their forest cover during this period. Rayagada and Koraput were the top two districts that recorded the highest FCL with mean change rates of 13.81 km2/year and 7.17 km2/year, respectively. The results point out that mining operations have grown in recent years in Odisha State, and the increase in these activities has contributed to the increase in FCL. This study offers a cost-effective methodology to monitor FCL in mining areas which will eventually contribute to the protection of forest biodiversity and forest dwelling tribal population.

Analysis of long- and short-term shoreline change dynamics: A study case of João Pessoa city in Brazil.

The coastal area of João Pessoa city, Paraíba, Brazil, is densely populated and has a large flow of trade and services. More recently, this region has been suffering from the advance of the sea, which has caused changes in the shoreline and caused a decrease in the beach area and damage to various urban facilities. Thus, the spatiotemporal changes of the short- and long-term characteristics of the shoreline of João Pessoa city over the past 34 years (1985-2019) were calculated and the forcing mechanisms responsible for the shoreline changes were analyzed. Remote sensing data (Landsat 5-TM and 8-OLI) and statistical techniques, such as endpoint rate (EPR), linear regression rate (LRR) and weighted linear regression (WLR), using Digital Shoreline Analysis System (DSAS), were used. In this study, 351 transects ranging from ~1.1 km to ~6 km were analyzed within four zones (Zones I to IV), and the main controlling factors that influence the shoreline changes in these zones, such as sea level, tidal range, wave height, beach morphology and ocean currents, were discussed. The long-term change from 1985 to 2019 showed primarily accretion on the shoreline of João Pessoa city, with the rate of 0.55 m/year (WLR method); 282 transects showed accretion. The results showed that Zone-I, which was located in the south of the study area, was the only zone that primarily recorded erosion from 1985 to 2019, with a mean rate of -0.23 m/year according to the WLR method. According to the short-term shoreline change analysis, a cyclical pattern of erosion was observed in the 1985-1990, 1993-1999, 2005-2011 and 2014-2019 periods, and accretion was observed in the 1990-1993, 1999-2005 and 2011-2014 periods. It was inferred that the patterns of all analyzed zones were similar, i.e., when majority accretion was detected within a zone, the other zones also recorded accretion, except for during the period from 2014 to 2019, when Zone-I showed different behavior. Finally, the long- and short-term analyses showed that the coastal area of João Pessoa city is influenced by various forcing mechanism responsible for the shoreline changes.

Geospatial drought severity analysis based on PERSIANN-CDR-estimated rainfall data for Odisha state in India (1983-2018).

Studying the behavior of drought and its short-, medium- and long-term features throughout a region is very important for the creation of adequate public policies and actions aimed at the economic and social development of the region. Furthermore, the frequency and intensity of weather-related natural hazards (rainfall, heatwaves and droughts) are increasing every year, and these extreme weather-related events are potent threats worldwide, particularly in developing countries, such as India. Thus, this paper aims to evaluate the drought behavior in the Odisha region of India (1983-2018) by using the standardized precipitation index (SPI) and the new drought severity classification (DS). PERSIANN-CDR-estimated rainfall data were used to provide 271 time series, which were equally spaced at intervals of 0.25°, over Odisha state. The accuracy of these time series was evaluated with rain gauge-measured data at multiple time scales, and it was observed that the PERSIANN-CDR-estimated rainfall data effectively captured the pattern of rainfall over Odisha state. It was noted that almost half of the mean annual rainfall was concentrated in July and August. On addition, northeastern Odisha and areas near the coast were the rainiest regions. Furthermore, the drought pattern was evaluated based on nine distinct four-year periods (SPI-48), and the results indicated that there was high spatiotemporal variability in drought occurrence among those periods; e.g., in the last four years, extreme drought events occurred throughout the state. For the DS severity index analysis, it was noted that the values tended to be more significant with the increase in the drought time scale. For short-term droughts, DS values were less significant throughout the region, whereas for the medium-term droughts, there was an increase in the DS values in all regions of Odisha, especially in the north-central region. For long-term droughts, the values were more significant throughout the region, especially in the areas with the highest rainfall levels. Finally, the PERSIANN-CDR data should also be analyzed in other regions of India, and the obtained results are useful for the identification of droughts throughout the region and for the management of water resources and can be replicated in any part of the world.