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Floristic analyses of tropical deciduous forests were carried out in four randomly selected forest ranges (Palhara sub-division of Angul, Gandhamardan hills of Bargarh, Telkoi forests range of Keonjhar, and Similipal Biosphere Reserve of Mayurbhanj districts) of Odisha, Eastern India by laying 32 belt transects each of 0.10 ha size. A total of 65 plant species belonging to 56 genera and 32 families were recorded in the present study, of which Euphorbiaceae was the most dominant family with eight species (12.31%). Species diversity (Shannon–Wiener's Index) and concentration of dominance (Simpson’s Index) were 3.08 and 0.09 for trees, 2.72 and 0.12 for shrubs, and 2.83 and 0.08 for herbs, respectively. On the other hand, Margalef’s index of species richness (Dmg) was 8.08 for trees, 4.48 for shrubs, and 4.69 for herbs while, Menhinick’s index of species richness (Dmn) was 1.17 for trees, 0.23 for shrubs, and 1.17 for herbs in the studied forests of Eastern India. The total tree density was 1241 individuals per hectare, and Shorea robusta was the most dominant tree species that shared 40.12% of the total tree density. The total basal cover of the studied forests was quite low (18.93 m2 ha−1), and S. robusta contributed the most (33.54%). Most of the species (89%) were distributed contagiously, while 11% were distributed randomly in the studied forests. No regeneration of most documented tree species (47.83%) and the low family richness is a matter of concern that signifies the ongoing anthropogenic disturbances, including the collection of fuelwoods, timbers, and intentional fires. The eco-development process should be carried out to meet the local inhabitants' livelihood requirements residing in and around forests to reduce the ongoing pressure on the natural forests and also to maintain the species-rich forest ecosystems of Odisha, Eastern India. Besides, exploitation of natural resources poses the greatest threat to the survival of many valuable species and ecosystems, leading to the loss of important biodiversity. Therefore, there is an urgent need to conserve these important bioresources.

The present paper highlights the importance of lac cultivation through Butea monosperma. We have modelled the suitable habitat of major lac host B. monosperma using MAXENT for the current and future climatic scenarios (RCPs 2.6, 4.5, 6.0 and 8.5). The study suggested the dominance of suitable habitats of B. monosperma in central-eastern to eastern and southern parts of the country. Temperature seasonality (Bio_4) was the most significant bioclimatic variable in regulating the distribution of B. monosperma followed by elevation and annual precipitation (Bio_13). The projection for the year 2050 suggested the habitat shift towards the eastern and southern parts. The study indicated the major habitat of B. monosperma continued to exist in the Chotanagpur plateau in eastern India. The model predicted approximately a 9–13% decrease in the overall potential habitat of B. monosperma by 2050, and the distribution of species would be nearly extinct from the northern and western parts. Presently, only the 5% lac host trees are being utilised for lac cultivation, and the study suggested that conservation and promotion of B. monosperma on projected suitable habitats and even by utilising 25% of resources, the lac production may jump manifold catering to global demand, rural economy and employment and shall contribute towards ‘Self Reliant India’.

Soil physicochemical properties were studied using standard methods at 3 months’ interval in six different Sal forests of Ranchi (02 LDF: Low-density Sal forests, 02 MDF: Moderate-density Sal forests, and 02 HDF: High-density Sal forests). The present study examined the soil nutrients dynamics and their relationship with climate (temperature, relative humidity, and rainfall) and community parameters. Soil physicochemical properties ranged from 5.18 ± 0.06 to 6.52 ± 0.11 (pH), 0.82 ± 0.31 to 1.85 ± 0.54% (soil organic carbon), 106 ± 2.47 to 188 ± 6.52 kg ha−1 (available nitrogen), 87.53 ± 4.11 to 164.41 ± 4.21 kg ha−1 (available potassium), 9.76 ± 2.42 to 32.05 ± 3.46 kg ha−1 (available phosphorus) in different forests. Soil pH showed significant positive correlation with mean monthly rainfall (r = 0.326, p

The present study focused on forest types classification, and major plant species assemblages in Mudumalai forests region utilizing Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG). The phytosociological analysis exhibited a total of 657 individuals (1095 individuals ha-1) of 24 tree species belonging to 22 genera, and 18 families. The highest tree density was contributed by Tectona grandis (132 individuals ha-1 and 12.05% of total tree density) followed by Anogeissus latifolia (105 individuals ha-1 and 9.59% of total tree density). The support vector machine study showed the dominance of Southern Tropical Semi-Evergreen forests (31%) followed by Southern Tropical Moist Deciduous forests (26.7%), and Southern Tropical Dry Deciduous forests (24.8%) with very high accuracy (92.37%). The comparative analysis of the existing forest types with Champion and Seth’s (1968) classification of forests exhibited a change of 30% in forest types in terms of their structure, composition, and extent over a period of 50 years. The spectral angle mapper-based study emphasized the defining role of elevation, rainfall, and temperature in species distribution, and physiognomy with dominance of A. latifolia (∼19.22%). The study implies the high spectral fidelity of airborne images for forest type mapping, and plant species discrimination in tropical forests.

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness., Proceedings of the National Academy of Sciences of the United States of America, 119 (6), ISSN:0027-8424, ISSN:1091-6490

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

Forest biomass is one of the significant components for assessing the productivity and sustainability of the forest's ecosystem. In the present study, the mapping and estimation of above ground biomass (AGB) was performed using field observations employing allometric equations and C band Sentinel 1A and L band Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing satellite images for the parts of north eastern Himalayan forests, Arunachal Pradesh. Total 57 transects of 500 m × 10 m size was laid in West Kameng and Tawang districts (altitude range: 1047–4161 m) of Arunachal Pradesh, in which a total of 12203 individuals of 67 tree species (Diameter at Breast Height (DBH) range: 3.18–235.45 cm) was recorded. The study exhibited higher tree density (428 individuals' ha−1) and basal cover (7658.63 m2ha-1) in the studied forests. The highest above ground biomass was contributed by Castanopsis indica (103.82 t ha−1) followed by Pinus roxburghii (46.24 t ha−1) and Quercus semicarpifolia (23.14 t ha−1). Further, AGB was estimated using the Horizontal-Horizontal (HH), Horizontal-Vertical (HV) polarizations acquired by the ALOS PALSAR 50 m mosaic (2008) dataset and Vertical-Vertical (VV), Vertical-Horizontal (VH) polarizations acquired by the Sentinel 1A dataset. The relationship between field based AGB and radar backscatter for selected sample plots was established using pair wise correlation. While comparing the two satellite-based SAR images, it is observed that high correlation between radar backscatter and field based AGB was observed in ALOS PALSAR as compared to Sentinel 1A SAR. This is due to higher sensitive to backscatter values and higher penetration level of L band ALOS PALSAR as compared to C band Sentinel 1A SAR. The correlation study indicates high correlation of HV polarizations in ALOS PALSAR (HV polarization R2 = 0.63; HH polarization R2 = 0.58) as compared to Sentinel 1A (VV polarization R2 = 0.054 and VH polarization R2 = 0.044). The most accurate and reliable method to estimate AGB is the field based method, but it is time consuming and tedious. Whereas, AGB estimation by L band SAR remote sensing is time effective and can be used for larger area in comparatively less time. The present study exhibited poor species diversity in higher altitudes northeast Himalayan forests as species diversity decreases with increase in altitudes. On the other hand, the higher AGB in the studied forests may be due to favorable environmental conditions including less temperature and more nutrient availability as well as less biotic interferences in terms of anthropogenic disturbances, herbivory and grazing. The study signifies the potential use of L and C band backscatter information in the reliable estimation of spatial AGB in sub-tropical to alpine forests of Arunachal Pradesh.

The study aimed to determine the above-ground biomass (AGB) of tropical deciduous forests in Central India using field-based techniques and spaceborne quad-pol ALOS PALSAR-2 L-band and dual-pol Sentinel 1A C-band synthetic aperture radar (SAR). Field sampling based on 73 forested grids exhibited a diverse forest composition in the Satpura Tiger Reserve (STR) with a total of 54 tree species of which 31.48% species were IUCN Red-Listed. The forests were highly diverse (H′=3.22) with a low concentration of dominance (CD = 0.06) and a quite high species evenness (E = 0.81). The sensitivity and correlation between field-based AGB and SAR backscatter in STR were highest in cross-polarization due to its higher sensitivity to the vegetation structure in both L- and C-band SAR, where L-VH based AGB estimates were found more accurate (RMSE:5.17%). The study provided vital information for setting up strategies for biodiversity conservation, enhancing forest productivity, and restoring wildlife habitats.

Satellite-based navigation systems are one of the most indispensable technologies in the present-day world that have made a vast improvement since the day of its inception due to global availability of signal and performance. It allows measuring positions in real time with an accuracy of up to a few centimeters on the Earth. The advent of Global Positioning System (GPS) has led to technological revolutions in highly accurate navigation, positioning, and time that is being applied in various civilian, military, and scientific purposes. GPS works on the radio waves that are being transmitted from a space-based group of satellites to the terrestrial GPS receiver to deduce the exact position of the Earth. Although there are various errors related to clock errors, multipath error, receiver noise, and antenna phase center variations at satellite as well as receivers end, it is being resolved through technological advancement and methods. Incorporating both GPS and GLONASS constellations in the navigation system may significantly improve the accuracy of the navigational solution. This chapter aims to discuss the various concepts of GPS, including working principle, various errors, various Global Navigation Satellite System technologies evolving from GPS to Quasi-Zenith Satellite System, and its vivid applications.

The present paper highlights the importance of lac cultivation through Butea monosperma. We have modelled the suitable habitat of major lac host B. monosperma using MAXENT for the current and future climatic scenarios (RCPs 2.6, 4.5, 6.0 and 8.5). The study suggested the dominance of suitable habitats of B. monosperma in central-eastern to eastern and southern parts of the country. Temperature seasonality (Bio_4) was the most significant bioclimatic variable in regulating the distribution of B. monosperma followed by elevation and annual precipitation (Bio_13). The projection for the year 2050 suggested the habitat shift towards the eastern and southern parts. The study indicated the major habitat of B. monosperma continued to exist in the Chotanagpur plateau in eastern India. The model predicted approximately a 9–13% decrease in the overall potential habitat of B. monosperma by 2050, and the distribution of species would be nearly extinct from the northern and western parts. Presently, only the 5% lac host trees are being utilised for lac cultivation, and the study suggested that conservation and promotion of B. monosperma on projected suitable habitats and even by utilising 25% of resources, the lac production may jump manifold catering to global demand, rural economy and employment and shall contribute towards ‘Self Reliant India’.

The Severe Acute Respiratory Syndrome-Coronavirus Disease 2019 (COVID-19) pandemic caused by a novel coronavirus known as SARS-CoV-2 has caused tremendous suffering and huge economic losses. We hypothesized that extreme measures of partial-to-total shutdown might have influenced the quality of the global environment because of decreased emissions of atmospheric pollutants. We tested this hypothesis using satellite imagery, climatic datasets (temperature, and absolute humidity), and COVID-19 cases available in the public domain. While the majority of the cases were recorded from Western countries, where mortality rates were strongly positively correlated with age, the number of cases in tropical regions was relatively lower than European and North American regions, possibly attributed to faster human-to-human transmission. There was a substantial reduction in the level of nitrogen dioxide (NO2: 0.00002 mol m−2), a low reduction in CO (, Graphical abstract Unlabelled Image, Highlights • COVID-19 cases in the tropical regions were relatively lower than the European & American regions. • Observed a substantial reduction in NO2, low reduction in CO, and low to moderate reduction in AOD • High COVID-19 hazard (AH: 4 to 9 g m−3) in major of the globe during April–July 2020 • Northern Hemisphere may be more susceptible compared to tropical regions in May–July 2020. • Tropical regions may be comparatively more prone to outbreaks in October–November 2020.

Background Above ground biomass (AGB) is a useful measure for assessing changes in forest structure and functional, and play a significant role in studying carbon stocks, the effect of deforestation and carbon sequestration on the global carbon balance. The present study aimed to study the relationship between AGB and community parameters in Sal forests of Eastern India through stratified random sampling by lying 92 belt transects each of 0.5 ha size. Results It recorded a high AGB (410.70 Mg ha-1), and carbon stock (Cp) (193.06 Mg C ha-1), and forest wise AGB ranged from 0.19 to 24.75 Mg ha-1 (mean 4.45 ± 0.45 SE). The spatial pattern of AGB showed that maximum studied forests (65%) had very low AGB (20 Mg ha-1). Species wise AGB ranged from 0.001 to 7074.94 Mg ha-1 (mean 106 ± 71 SE) and Shorea robusta with maximum basal area (120.81 m2 ha−1) contributed maximum AGB (64.87% of the total AGB), however, no similar trends have been observed in any other tree species. A significant positive correlation was observed between AGB and Cp (r=1.00, p