Global biodiversity monitoring systems through remote sensing can support consistent assessment, monitoring, modelling and reporting on biodiversity which are key activities intended for sustainable ...management. This work presents an overview of biodiversity monitoring components, i.e. biodiversity levels, essential biodiversity variables, biodiversity indicators, scale, biodiversity inventory, biodiversity models, habitat, ecosystem services, vegetation health and biogeochemical heterogeneity and discusses what remote sensing through Earth Observations has contributed to the study of biodiversity. The technological advancements in remote sensing have enabled information-rich data on biodiversity. Remote sensing data are making a strong contribution in providing unique information relevant to various biodiversity research and conservation applications. The extensive use of Earth observation data are not yet realized in biodiversity assessment, monitoring and conservation. The development of direct remote sensing approaches and the techniques for quantifying biodiversity at the community to species level is likely to be a great challenge for comprehensive earth observation-based monitoring strategy.
The cement industry plays critical role in any country’s development and economic growth. Cement is extensively used in construction sector and infrastructural projects. Abundant raw material ...availability, infrastructure demands, urbanization, and recent government initiatives—Atal Mission for Rejuvenation and Urban Transformation (AMRUT) project and housing for all under the Pradhan Mantri Awas Yojana (PMAY), India—stood at second place in cement-producing country in the world. Cement plants are emitting 15% of global pollutions into the environment among various industries. Cement industry byproducts include dust/particulate matter (PM
2.5
and PM
10
), toxic gases (COx, NO
x
, SOx, CH
4
, and VOCs), noise, and heavy metals (Cr, Ni, Co, Pb, and Hg), which cause climate change, global warming, and health risks as well as negative impact on flora and fauna. The Terra, Aura, Sentinel-5P, GOSAT, and other satellite datasets allow estimation of cement industry major air pollutants such as particulate matter (PM), sulfur dioxide (SO
2
), nitrogen dioxide (NO
2
), carbon dioxide (CO
2
), and volatile organic compounds (VOCs) using regression models, artificial neural network-based models, machine learning models, and the tropospheric NO2 vertical column density (VCD) retrieval algorithm. This review article explores the evolution of the Indian cement industry, air pollutants from the cement industry, social and environmental implications, satellite datasets, models used for assessing air pollutants, and challenges for the long-term sustainability of the cement industry.
Forest cover is the primary determinant of elephant distribution, thus, understanding forest loss and fragmentation is crucial for elephant conservation. We assessed deforestation and patterns of ...forest fragmentation between 1930 and 2020 in Chure Terai Madhesh Lanscape (CTML) which covers the entire elephant range in Nepal. Forest cover maps and fragmentation matrices were generated using multi-source data (Topographic maps and Landsat satellite images of 1930, 1975, 2000, and 2020) and spatiotemporal change was quantified. At present, 19,069 km
forest cover in CTML is available as the elephant habitat in Nepal. Overall, 21.5% of elephant habitat was lost between 1930 and 2020, with a larger (12.3%) forest cover loss between 1930 and 1975. Area of the large forests (Core 3) has decreased by 43.08% whereas smaller patches (Core 2, Core 1, edge and patch forests) has increased multifold between 1930 and 2020. The continued habitat loss and fragmentation probably fragmented elephant populations during the last century and made them insular with long-term ramifications for elephant conservation and human-elephant conflict. Given the substantial loss in forest cover and high levels of fragmentation, improving the resilience of elephant populations in Nepal would urgently require habitat and corridor restoration to enable the movement of elephants.
This study quantifies the nationwide land cover and long-term changes in forests and its implications on forest fragmentation in Nepal. The multi-source datasets were used to generate the forest ...cover information for 1930, 1975, 1985, 1995, 2005 and 2014. This study analyzes distribution of land cover, rate of deforestation, changes across forest types, forest canopy density and pattern of fragmentation. The land cover legend for 2014 is consisting of 21 classes: tropical dry deciduous sal forest, tropical moist deciduous sal forest, subtropical broad-leaved forest, subtropical pine forest, lower temperate broad leaved forest, upper temperate broad leaved forest, lower temperate mixed broad leaved forest, upper temperate mixed broad leaved forest, temperate needle leaved forest, subalpine forest, plantations, tropical scrub, subtropical scrub, temperate scrub, alpine scrub, grassland, agriculture, water bodies, barren land and settlements. The forest cover statistics for Nepal obtained in this study shows an area of 76,710 km
2
in 1930 which has decreased to 39,392 km
2
in 2014. A net loss of 37,318 km
2
(48.6%) was observed in last eight decades. Analysis of annual rate of net deforestation for the recent period indicates 0.01% during 2005–2014. An increase in the number of forest patches from 6925 (in 1930) to 42,961 (in 2014) was noticed. The significant observation is 75.5% of reduction in core 3 forest, whereas, patch, perforated and edge classes show the increase in percentage of fragmentation classes from 1930 to 2014. The results of this work will support the understanding of deforestation and its consequences on fragmentation for maintaining and improving the forest resources of Nepal.
Forest fire has been identified as one of the key environmental issue for long-term conservation of biodiversity and has impact on global climate. Spatially multiple observations are necessary for ...monitoring of forest fires in tropics for understanding conservation efficacy and sustaining biodiversity in protected areas. The present work was carried out to estimate the spatial extent of forest burnt areas and fire frequency using Resourcesat Advanced Wide Field Sensor (AWiFS) data (2009, 2010, 2012, 2013 and 2014) in Andhra Pradesh, India. The spatio-temporal analysis shows that an area of 7514.10 km
2
(29.22 % of total forest cover) has been affected by forest fires. Six major forest types are distributed in Andhra Pradesh, i.e. semi-evergreen, moist deciduous, dry deciduous, dry evergreen, thorn and mangroves. Of the total forest burnt area, dry deciduous forests account for >75 %. District-wise analysis shows that Kurnool, Prakasam and Cuddapah have shown >100 km
2
of burnt area every year. The total forest burnt area estimate covering protected areas ranges between 6.9 and 22.3 % during the study period. Spatial burnt area analysis for protected areas in 2014 indicates 37.2 % of fire incidences in the Nagarjunasagar Srisailam Tiger Reserve followed by 20.2 % in the Sri Lankamalleswara Wildlife Sanctuary, 20.1 % in the Sri Venkateswara Wildlife Sanctuary and 17.4 % in the Gundla Brahmeswaram Wildlife Sanctuary. The analysis of cumulative fire occurrences from 2009 to 2014 has helped in delineation of conservation priority hotspots using a spatial grid cell approach. Conservation priority hotspots I and II are distributed in major parts of study area including protected areas of the Nagarjunasagar Srisailam Tiger Reserve and Gundla Brahmeswaram Wildlife Sanctuary. The spatial database generated will be useful in studies related to influence of fires on species adaptability, ecological damage assessment and conservation planning.
Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have ...undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924–1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km
2
(52.5 %), 56,661.1 km
2
(36.4 %), 51,642.3 km
2
(33.2 %), 49,773 km
2
(32 %) and 48,669.4 km
2
(31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 % year
−1
during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km
2
) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.
Climate change is one of the factors contributing to the spread of invasive alien species. As a result, it is critical to investigate potential invasion dynamics on a global scale in the face of ...climate change. We used updated occurrence data, bioclimatic variables, and Köppen-Geiger climatic zones to better understand the climatic niche dynamics of
Prosopis juliflora
L. (Fabaceae). In this study, we first compared several algorithms—MaxEnt, generalized linear model (GLM), artificial neural network (ANN), generalized boosted model (GBM), generalized additive model (GAM), and random forest (RF)—to investigate the relationships between species-environment and climate for mesquite. We identified the global climate niche similarity sites (NSSs) using the coalesce approach. This study focused on the current and future climatic suitability of
P. juliflora
under two global circulation models (GCMs) and two climatic scenarios, i.e., Representative Concentration Pathways (RCPs), 4.5 and 8.5, for 2050 and 2070, respectively. Sensitivity, specificity, true skill statistic (TSS), kappa coefficient, and correlation were used to evaluate model performance. Among the tested models, the machine learning algorithm random forest (RF) demonstrated the highest accuracy. The vast swaths of currently uninvaded land on multiple continents are ideal habitats for invasion. Approximately 9.65% of the area is highly suitable for the establishment of
P. juliflora
. Consequently, certain regions in the Americas, Africa, Asia, Europe, and Oceania have become particularly vulnerable to invasion. In relation to RCPs, we identified suitable area changes (expansion, loss, and stability). The findings of this study show that NSSs and RCPs increase the risk of invasion in specific parts of the world. Our findings contribute to a cross-border continental conservation effort to combat
P. juliflora
expansion into new potential invasion areas.
Forest fire is considered as one of the major threats to global biodiversity and a significant source of greenhouse gas emissions. Rising temperatures, weather conditions, and topography promote the ...incidences of fire due to human ignition in South Asia. Because of its synoptic, multi-spectral, and multi-temporal nature, remote sensing data can be a state of art technology for forest fire management. This study focuses on the spatio-temporal patterns of forest fires and identifying hotspots using the novel geospatial technique “emerging hotspot analysis tool” in South Asia. Daily MODIS active fire locations data of 15 years (2003–2017) has been aggregated in order to characterize fire frequency, fire density, and hotspots. A total of 522,348 active fire points have been used to analyze risk of fires across the forest types. Maximum number of forest fires in South Asia was occurring during the January to May. Spatial analysis identified areas of frequent burning and high fire density in South Asian countries. In South Asia, 51% of forest grid cells were affected by fires in 15 years. Highest number of fire incidences was recorded in tropical moist deciduous forest and tropical dry deciduous forest. The emerging hotspots analysis indicates prevalence of sporadic hotspots, followed by historical hotspots, consecutive hotspots, and persistent hotspots in South Asia. Of the seven South Asian countries, Bangladesh has highest emerging hotspot area (34.2%) in forests, followed by 32.2% in India and 29.5% in Nepal. Study results offer critical insights in delineation of fire vulnerable forest landscapes which will stand as a valuable input for strengthening management of fires in South Asia.
India is home of the largest remaining population of the Asian elephant (
Elephas maximus
L.) in the South and Southeast Asia. The forest loss and fragmentation is the main threat to the long-term ...survival of Asian elephants. In the present study, we assessed forest loss and fragmentation in the major elephant ranging provinces in India, viz., north-eastern, north-western, central, and southern since the 1930s. We quantified forest cover changes by generating and analyzing forest cover maps of 1930, 1975, and 2013, whereas fragmentation of contiguous forest areas was quantified by applying landscape metrics on the temporal forest cover maps. A total of 21.49% of the original forest cover was lost from 1930 to 1975, while another 3.19% forest cover was lost from 1975 to 2013 in the elephant ranges in India. The maximum forest loss occurred in the southern range (13,084 km
2
) followed by north-eastern (10,188 km
2
), central (5614 km
2
), and north-western (4030 km
2
) elephant ranges in the past eight decades. The forests in the central range were the most fragmented followed by southern, north-eastern, and north-western elephant ranges. The forest fragmentation in the southern range occurred at the fastest rate than central, north-eastern, and north-western ranges. The core forest areas shrunk by 39.6% from 1930 to 2013. The causative factors of forest change and situation of elephant-human conflict have been discussed. Study outcomes would be helpful in planning effective conservation strategies for Asian elephants in India.
India, a mega-diverse country, possesses a wide range of climate and vegetation types along with a varied topography. The present study has classified forest types of India based on multi-season IRS ...Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data. The study has characterized 29 land use/land cover classes including 14 forest types and seven scrub types. Hybrid classification approach has been used for the classification of forest types. The classification of vegetation has been carried out based on the ecological rule bases followed by Champion and Seth’s (1968) scheme of forest types in India. The present classification scheme has been compared with the available global and national level land cover products. The natural vegetation cover was estimated to be 29.36 % of total geographical area of India. The predominant forest types of India are tropical dry deciduous and tropical moist deciduous. Of the total forest cover, tropical dry deciduous forests occupy an area of 2,17,713 km² (34.80 %) followed by 2,07,649 km² (33.19 %) under tropical moist deciduous forests, 48,295 km² (7.72 %) under tropical semi-evergreen forests and 47,192 km² (7.54 %) under tropical wet evergreen forests. The study has brought out a comprehensive vegetation cover and forest type maps based on inputs critical in defining the various categories of vegetation and forest types. This spatially explicit database will be highly useful for the studies related to changes in various forest types, carbon stocks, climate-vegetation modeling and biogeochemical cycles.
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