Academic literature on the topic 'Forest fire, eviction, climate change, economics'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Forest fire, eviction, climate change, economics.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Forest fire, eviction, climate change, economics"

1

Mayans, Juan José, José A. Torrent-Bravo, and Leticia Lopéz. "Energy Use of Mediterranean Forest Biomass in Sustainable Public Heating Systems and its Effects on Climate Change – Case of Study." International Journal of Renewable Energy Development 10, no. 2 (December 15, 2020): 229–38. http://dx.doi.org/10.14710/ijred.2021.34276.

Full text
Abstract:
The municipality of Serra, Valencia, located in the Spanish Mediterranean east coast, covers an area of 5,730 hectares, with 95% of this territory lying within the Sierra Calderona Natural Park and 85% being forest. The main axis of the municipality’s economy has been the construction, reducing the primary sector, resulting in uncontrolled growth of forest and deterioration of the landscape. All this has raised forest fire risk to dangerous levels threatening the natural heritage of Serra and the future of the Serra Calderona Natural Park. The study shows how an adequate model of forest biomass management, through energetic use in sustainable public heating systems, can have positive direct effects in the fight against climate change, considering both economics aspects and environmental effects, and its capacity to contribute to the socioeconomic development of agro forestry regions, fixing its habitants and offering a rural development based on the rational use of their natural resources
APA, Harvard, Vancouver, ISO, and other styles
2

Kantartzis, A., G. Arabatzis, O. Christopoulou, A. Sfougaris, S. Sakellariou, Ch Malesios, E. Tsiaras, F. Samara, and S. Th Tampekis. "Forest roads planning and management in terms of Social-Ecological Systems (SES) framework." IOP Conference Series: Earth and Environmental Science 899, no. 1 (November 1, 2021): 012052. http://dx.doi.org/10.1088/1755-1315/899/1/012052.

Full text
Abstract:
Abstract Adaptation to climate change as well as the increasing demand for a new approach in post fire socioecological resilience and Nature-Based Solutions (NBS) in forest management requires a different way of thinking of forest roads planning, in terms of Social-Ecological Systems (SES) Framework. Social-ecological systems are complex, adaptive and emphasize that social and ecological systems are linked through feedback mechanisms, and that both display resilience and complexity. In this frame, it is important to clarify the considerable dynamic elements for the future development of forest roads planning and management that promote natural, socio-economic, and cultural well-being. The main objective of this paper is to identify important new challenges concerning the forest roads planning and management and to propose a conceptual paradigm towards SES in a continuing changing climate, social needs and environmental conditions. Hence, a newly developed concept under the prism of SES forest roads planning, is presented. Eight key performance areas to ensure the forest operations as SES include: (i) nature’s services; (ii) ergonomics; (iii) environmental economics; (iv) quality optimization of products and production based on NBS; (v) the use as evacuation routes; (vi) access to renewable energy sources; (vii) people and society; and (viii) resilience. The conceptual frame of SES provides a close to nature perspective which addresses the ongoing and foreseeable challenges that the global forest ecosystems face, based on harmonized forest operations performance across economic, environmental and social sustainability. In this new concept, we demonstrate how these eight interconnected principles interact to each other and are related to forest operations achieving Nature Based Solutions in forest management and climate change mitigation.
APA, Harvard, Vancouver, ISO, and other styles
3

Сауткин, Илья Сергеевич, and Татьяна Владимировна Рогова. "ВАРИАБИЛЬНОСТЬ ФУНКЦИОНАЛЬНЫХ ПРИЗНАКОВ ЛИСТЬЕВ НЕКОТОРЫХ ВИДОВ ЛУГОВЫХ РАСТЕНИЙ." Российский журнал прикладной экологии, no. 1 (March 25, 2022): 4–14. http://dx.doi.org/10.24852/2411-7374.2022.1.4.14.

Full text
Abstract:
Исследование внутривидовой изменчивости трех функциональных признаков листьев: площади – LA, сухой массы – LDW и удельной площади – SLA показало их взаимообусловленность и зависимость значений признаков от благопритяности условий местообитания и антропогенной нагрузки. Анализ полученных данных исследования показал, что универсальные информационные показатели LA и LDW являются низкими в неблагоприятных и низкопродуктивных местообитаниях и более высокими при изобилии ресурсов в более продуктивных условиях существования. Полученные значения SLA видов растений, произрастающих в сообществах интенсивно эксплуатируемых пастбищ, часто имеют более высокие значения. Возможно, адаптация в условиях постоянного изъятия биомассы на сенокосах и пастбищах идет в первую очередь через сокращение массы листьев при сохранении листовой поверхности. В сообществах мезофитных лугов в условиях заповедного режима, характеризующихся высокой продуктивностью, показатели функциональных признаков всех исследованных видов выше по сравнению с менее продуктивными лугами, существующими в условиях дефицита увлажнения. Пастбищные нагрузки, оказывающие отрицательное воздействие на луговые пастбищные травостои, вызывают не только сокращение запасов общей биомассы лугового сообщества, но и изменение индивидуальных функциональных признаков видов растений, их образующих. Библиографические ссылки 1. Воронов А.Г. Геоботаника. М.: Высшая школа, 1973. 384 с.2. Ackerly D., Knight C., Weiss S., Barton K., Starmer K. Leaf size, specific leaf area and microhabitat distribution of chaparral woody plants: contrasting patterns in species level and community level analyses // Oecologia. 2002. Vol. 130, №3. P. 449‒457. doi: 10.1007/s004420100805.3. Bolnick D.I., Svanbäck R., Fordyce J.A., Yang L.H., Davis J.M., Hulsey C.D., Forister M.L. The ecology of individuals: incidence and implications of individual specialization // The American naturalist. 2003. Vol. 161, №1. P. 1‒28. doi:10.1086/343878.4. Chapin F.S. The mineral-nutrition of wild plants // Annual review of ecology, eVolution, and systematics. 1980. Vol. 11. P. 233–260. doi:10.1146/annureVol.es.11.110180.001313.5. Cheng J., Chu P., Chen D., Bai Y., Niu S. Functional correlations between specific leaf area and specific root length along a regional environmental gradient in Inner Mongolia grasslands // Functional ecology. 2016. Vol. 30. P. 985–997. doi: 10.1111/1365-2435.12569.6. Chown S.L., Gaston K.J., Robinson D. Macrophysiology: large-scale patterns in physiological traits and their ecological implications // Functional ecology. 2004. Vol. 18. P. 159–167. doi: 10.1111/j.0269-8463.2004.00825.x.7. Davies C.E., Moss D., Hill M.O. EUNIS habitat classification revised 2004 // Report to: European Environment Agency – European Topic Centre on Nature Protection and Biodiversity. 2004. P. 127‒143.8. Firn J., McGree J.M., Harvey E., Flores-Moreno H., Schutz M., … Risch A.C. Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs // Nature ecology and eVolution. 2019. Vol. 3. P. 400–406. doi: 10.1038/s41559-018-0790-1.9. Funk J.L., Cornwell W.K. Leaf traits within communities: Context may affect the mapping of traits to function // Ecology. 2013. Vol. 94. P. 1893–1897. doi: 10.1890/12-1602.1.10. Garnier E. Resource capture, biomass allocation and growth in herbaceous plants // Trends in ecology and eVolution. 1991. VOL. 6. P. 126–131. doi: 10.1016/0169-5347(91)90091-B.11. Garnier E., Cortez J., Billes G., Navas M. L., Roumet C., Debussche M., … Toussaint J. P. Plant functional markers capture ecosystem properties during secondary succession // Ecology. 2004. Vol. 85. P. 2630–2637. doi: 10.1890/03-0799.12. Garnier E., Shipley B., Roumet C., Laurent G. A standardized protocol for the determination of specific leaf area and leaf dry matter content // Functional ecology. 2001. P. 688‒695.13. Givnish T.J. Leaf form in relation to environment: a theoretical study. Princeton University, 1976. 482 p.14. Grime J.P. Trait convergence and trait divergence in herbaceous plant communities: mechanisms and consequences // Journal of vegetation science. 2006. Vol. 17, №2. P. 255‒260. doi: 10.1111/j.1654-1103.2006.tb02444.x.15. Gunn S., Farrar J.F., Collis B.E., Nason M. Specific leaf area in barley: individual leaves versus whole plants // Newphytologist. 1999. Vol. 143. P. 45–51. doi: 10.1046/j.1469-8137.1999.00434.x.16. Hulshof C.M., Swenson N.G. Variation in leaf functional trait values within and across individuals and species: an example from a Costa Rican dry forest // Functional ecology. 2010. Vol. 24, №1. P. 217‒223. doi: 10.1111/j.1365-2435.2009.01614.x.17. Lavorel S., Garnier É. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail // Functional ecology. 2002. Vol. 16, №5. P. 545‒556. doi: 10.1046/j.1365-2435.2002.00664.x.18. Linhart Y.B., Grant M.C. Evolutionary significance of local genetic differentiation in plants // Annual review of ecology and systematics. 1996. Vol. 27, №1. P. 237‒277.19. Nicotra A.B., Atkin O.K., Bonser S.P., Davidson A.M., Finnegan E.J., Mathesius U., ... van Kleunen M. Plant phenotypic plasticity in a changing climate // Trends in plant science. 2010. Vol. 15, №12. P. 684‒692. doi: 10.1016/j.tplants.2010.09.008.20. Niklas K.J., Cobb E.D., Niinemets U., Reich P.B., Sellin A., Shipley B., Wright I.J. «Diminishing returns» in the scaling of functional leaf traits across and within species groups // PNAS. 2007. Vol. 104. P. 8891–8896. doi: 10.1073/pnas.0701135104.21. Pérez-Harguindeguy N., Díaz S., Garnier E., Lavorel S., Poorter H., … Cornelissen J.H.C. Corrigendum to: new handbook for standardized measurement of plant functional traits worldwide // Australian journal of botany. 2016. Vol. 64, №8. P. 715‒716. doi: 10.1071/BT12225_CO.22. Poorter H., Niinemets Ü., Poorter L., Wright I.J., Villar R. Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis // New Phytologist. 2009. Vol. 182. P. 565–588. doi: 10.1111/j.1469-8137.2009.02830.x.23. Poorter L., Wright S.J., Paz H., Ackerly D.D., Condit R., Ibarra-Manríquez G., … Wright I.J. Are functional traits good predictors of demographic rates? Evidence from five neotropical forests // Ecology. 2008. Vol. 89. P. 1908–1920. doi: 10.1890/07-0207.1.24. Reich P.B. The world-wide «fast-slow» plant economics spectrum: a traits manifesto // Journal of ecology. 2014. Vol. 102. P. 275–301. doi:10.1111/1365-2745.12211.25. Runions A., Fuhrer M., Lane B., Federl P., Rolland- Lagan A.-G., Prusinkiewicz P. Modeling and visualization of leaf venation patterns // ACM Transaction on Graphics. 2005. Vol. 24. P. 702–711. doi: 10.1145/1186822.1073251.26. Smith W.K., Vogelmann T.C., DeLucia E.H., Bell D.T., Shepherd K.A. Leaf form and photosynthesis: Do leaf structure and orientation interact to regulate internal light and carbon dioxide? // BioScience. 1997. Vol. 47. P. 785–793.27. Tichý L. JUICE, software for vegetation classification // Journal of vegetation science. 2002. Vol. 13, №3. P. 451‒453. doi: 10.1111/j.1654-1103.2002.tb02069.x.28. Violle C., Enquist B.J., McGill B.J., Jiang L.I.N., Albert C.H., Hulshof C., ... Messier J. The return of the variance: intraspecific variability in community ecology // Trends in ecology and eVolution. 2012. Vol. 27, №4. P. 244‒252. doi: 10.1016/j.tree.2011.11.014.29. Whitlock C., Shafer S.L., Marlon J. The role of climate and vegetation change in shaping past and future fire regimes in the northwestern US and the implications for ecosystem management // Forest ecology and management. 2003. Vol. 178, №1‒2. P. 5‒21. doi: 10.1016/S0378-1127(03)00051-3.30. Worthy S.J., Laughlin D.C., Zambrano J., Umana M.N., Zhang C., Lin L., Cao M., Swenson N.G. Alternative designs and tropical tree seedling growth performance landscapes // Ecology. 2020. Vol. 101. P. e03007. doi: 10.1002/ecy.3007.31. Wright I.J., Reich P.B., Westoby M., Ackerly D.D., Baruch Z., Bongers F., … Prior L. The worldwide leaf economics spectrum // Nature. 2004. Vol. 428. P. 821–827. doi: 10.1038/nature02403.32. Wright I.J., Reich P.B., Cornelissen J.H.C., Falster D.S., Groom P.K., ... Westoby M. Modulation of leaf economic traits and trait relationships by climate // Global ecology and biogeography. 2005. Vol. 14. P. 411–421. doi: 10.1111/j.1466-822x.2005.00172.x.33. Wright I.J., Westoby M. Leaves at low versus high rainfall: coordination of structure, lifespan and physiology // New phytologist. 2002. Vol. 155. P. 403–416. doi: 10.1046/j.1469-8137.2002.00479.x.
APA, Harvard, Vancouver, ISO, and other styles
4

Yakubu, Bashir Ishaku, Shua’ib Musa Hassan, and Sallau Osisiemo Asiribo. "AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES." Geosfera Indonesia 3, no. 2 (August 28, 2018): 27. http://dx.doi.org/10.19184/geosi.v3i2.7934.

Full text
Abstract:
Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). Geological Investigation of Tagwai Dams using Remote Sensing Technique, Minna Niger State, Nigeria. Journal of Environment, 1(01), pp. 26-32. Amadi, A., & Olasehinde, P. (2010). Application of remote sensing techniques in hydrogeological mapping of parts of Bosso Area, Minna, North-Central Nigeria. International Journal of Physical Sciences, 5(9), pp. 1465-1474. Aplin, P., & Smith, G. (2008). Advances in object-based image classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), pp. 725-728. Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., . . . Teshale, E. Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p 1178622117751603. Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heat island in Birmingham, UK: a comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), p 153. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, pp. 180-191. Bukata, R. P., Jerome, J. H., Kondratyev, A. S., & Pozdnyakov, D. V. (2018). Optical properties and remote sensing of inland and coastal waters: CRC press. Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, 31(1), pp. 45-54. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., . . . Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp. 7-27. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), pp. 171-209. Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE transactions on geoscience and remote sensing, 53(8), pp. 4238-4249. Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, pp. 119-132. Coale, A. J., & Hoover, E. M. (2015). Population growth and economic development: Princeton University Press. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press. Corner, R. J., Dewan, A. M., & Chakma, S. (2014). Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651. Debnath, A., Debnath, J., Ahmed, I., & Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309. Desheng, L., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401. Dronova, I., Gong, P., Wang, L., & Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272. Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1) Farhani, S., & Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676. Feng, L., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328. Fensham, R., & Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429. Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52. Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents. Government, N. S. (2007). Niger state (The Power State). Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337. Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734. Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261. Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016. Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124. Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., & Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406. Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120. Jiang, L., Wu, F., Liu, Y., & Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600. Jin, S., Yang, L., Zhu, Z., & Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70. Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380. Koop, S. H., & van Leeuwen, C. J. (2015). Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670. Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234. Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity Object-based image analysis (pp. 3-27): Springer. Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35(3), pp. 286-304. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons. Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., & Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285. Luo, M., & Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720. Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., & Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250. Mas, J.-F., Velázquez, A., Díaz-Gallegos, J. R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., . . . Pérez-Vega, A. (2004). Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5(4), pp. 249-261. Mathew, A., Chaudhary, R., Gupta, N., Khandelwal, S., & Kaul, N. (2015). Study of Urban Heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters. International Journal of Computer & Mathematical Science, 4, pp. 2347-2357. Megahed, Y., Cabral, P., Silva, J., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), pp. 1750-1769. Metternicht, G. (2001). Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological modelling, 144(2-3), pp. 163-179. Miller, R. B., & Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy, 6(2), pp. 129-137. Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. Mohammed, I., Aboh, H., & Emenike, E. (2007). A regional geoelectric investigation for groundwater exploration in Minna area, north west Nigeria. Science World Journal, 2(4) Morenikeji, G., Umaru, E., Liman, S., & Ajagbe, M. (2015). Application of Remote Sensing and Geographic Information System in Monitoring the Dynamics of Landuse in Minna, Nigeria. International Journal of Academic Research in Business and Social Sciences, 5(6), pp. 320-337. Mukherjee, A. B., Krishna, A. P., & Patel, N. (2018). Application of Remote Sensing Technology, GIS and AHP-TOPSIS Model to Quantify Urban Landscape Vulnerability to Land Use Transformation Information and Communication Technology for Sustainable Development (pp. 31-40): Springer. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), pp. 1145-1161. Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), pp. 125-133. Niu, X., & Ban, Y. (2013). Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), pp. 1-26. Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854. Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88. Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84. Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234. Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611. Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, p 16. Shrivastava, L., & Nag, S. (2017). MONITORING OF LAND USE/LAND COVER CHANGE USING GIS AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF SAGAR RIVER WATERSHED, TRIBUTARY OF WAINGANGA RIVER OF MADHYA PRADESH, INDIA. Shuaibu, M., & Sulaiman, I. (2012). Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res, 5, pp. 43-55. Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J. M., . . . Chanussot, J. (2014). Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE transactions on geoscience and remote sensing, 52(8), pp. 5122-5136. Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, pp. 1-13. Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, pp. 459-469. Teodoro, A. C., Gutierres, F., Gomes, P., & Rocha, J. (2018). Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 579-587): Springer. Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36. Tuholske, C., Tane, Z., López-Carr, D., Roberts, D., & Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Applied Geography, 88, pp. 84-93. Tuia, D., Flamary, R., & Courty, N. (2015). Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions. ISPRS Journal of Photogrammetry and Remote Sensing, 105, pp. 272-285. Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer. Wang, L., Sousa, W., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International journal of remote sensing, 25(24), pp. 5655-5668. Wang, Q., Zeng, Y.-e., & Wu, B.-w. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews, 54, pp. 1563-1579. Wang, S., Ma, H., & Zhao, Y. (2014). Exploring the relationship between urbanization and the eco-environment—A case study of Beijing–Tianjin–Hebei region. Ecological Indicators, 45, pp. 171-183. Weitkamp, C. (2006). Lidar: range-resolved optical remote sensing of the atmosphere: Springer Science & Business. Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., & Lausch, A. (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, pp. 190-203. Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), pp. 884-893. Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS congress. Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., . . . Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), pp. 2310-2323. Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations: Springer. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), pp. 799-811. Zhou, D., Zhao, S., Zhang, L., & Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. Remote Sensing of Environment, 176, pp. 272-281. Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., . . . Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, pp. 243-257.
APA, Harvard, Vancouver, ISO, and other styles
5

Otrachshenko, Vladimir, and Luis C. Nunes. "Fire takes no vacation: impact of fires on tourism." Environment and Development Economics, March 9, 2021, 1–16. http://dx.doi.org/10.1017/s1355770x21000012.

Full text
Abstract:
Abstract Many Mediterranean-type climates around the world will face increased risks of wildfires as a consequence of climate change. In this study we consider the case of Portugal and estimate the impact of the increasing risk of forest fires on tourism. Using data for 278 municipalities for the 2000–2016 period, we find a considerable negative impact of burned areas on the number of tourist arrivals, both domestic and inbound. We go beyond the traditional impact analysis and provide predictions for 2030 and 2050. The estimated annual costs to the Portuguese economy due to the impact of burned areas in 2030 range between €17.03 and 24.18 million for domestic tourist arrivals and between €18.26 and 38.08 million for inbound ones. In 2050, those costs will increase at least fourfold. These findings underscore the importance of taking the forest fire risks into account when planning local investments.
APA, Harvard, Vancouver, ISO, and other styles
6

"Forest Research Priorities in Canada, 1990: An Overview for the Canadian Council of Forest Ministers Prepared by the Forestry Research Advisory Council of Canada In Cooperation with the Forestry Research Advisory Committees in The Provinces & Territories Ottawa, July, 1990." Forestry Chronicle 67, no. 1 (February 1, 1991): 64–72. http://dx.doi.org/10.5558/tfc67064-1.

Full text
Abstract:
In 1987 the Forestry Research Advisory Council of Canada (FRACC) proposed an annual Canada-wide survey of research priorities and emerging issues to be presented to the Canadian Council of Forest Ministers (CCFM). This was accepted as a way to improve research dialogue, coordination and application. The provincial and territorial forestry research advisory committees contributed to the first overview presented to CCFM in October 1989 and published in the December 1989 Forestry Chronicle. This second report is based on material provided by the provincial and territorial advisory groups in early 1990.Forestry research is often long term but priorities do evolve. Twenty three research topics are new in this report. Several were identified as emerging concerns last year. Many of the new items fall under a new heading "FOREST POLICY AND ECONOMICS" or under "ENVIRONMENTAL CONCERNS" and highlight the need for socioeconomic studies, predictive models, resource and land use decision methods and concern about climate change. This evolving emphasis reflects the increasing attention forest managers in industry and government are giving to public attitudes on resource and environmental matters. These attitudes are being more forcefully articulated as the sustainable development ethic gains wider acceptance. At the same time, the more traditional concerns of forest managers about protecting, harvesting and regenerating the forest are still prominent in research priorities.Policy and economics matters needing research attention are:• Predictive supply/demand models for timber & non timber values;• Broad socio-economic studies on the implications of new policies;• Economics of private woodlands;• Methods for land use decisions;• Novel tenure and timber sales arrangements.Integrated forest resource management requires research on:• Managing for all values, wildlife, recreation, timber etc.;• Ecosystem functioning;• Decision criteria and information systems.Environmental research priorities include:• The effects of forest management and harvesting on the forest ecosystem;• The forest effects of atmospheric pollution and climate change;• The fate of pesticides applied to the forest;• The reforestation of contaminated sites;• The implications of pesticide residues on planting stock.Forest pest priorities for research are:• Alternatives to chemical control methods;• Improved risk assessment and management methods;• Work on specific pests such as the spruce budworm;• Pests of nurseries and young stands;• Damage appraisal methods;• Spray technology and drift prevention.The many forest fires of 1989 give priority to research on:• Wildfire prediction, detection and control;• Improving and applying integrated fire management systems;• Fire ecology.In silviculture, forest regeneration and tending are high priority with research particularly needed on:• Improving planting stock quality;• Control of competing vegetation;• Tree improvement;• Seed and seed orchard management;• Regenerating hybrid poplars and aspens;• Cost reduction and increased effectiveness;• Improved growth and yield information and site data;• The culture of high quality hardwoods in eastern Canada.Forest products research needs include:• Underutilized hardwoods;• The quality of wood harvested from second growth forests;• Improving the manufacture of pulp, paper and solid wood products;• Devising new higher value products.The major emerging issues likely to affect future priorities centre around:• The increasing public involvement in resource management;• The growing prominence of the sustainable development ethic;• Canada's weak commitment to research and development;• Recycling and its impact on both forest management and products;• The growing commitment to integrated forest resource management.There is also concern that popular environmental matters may skew research priorities away from less glamorous but important topics.The importance of Forest Resource Development Agreements in furthering forestry research is universally recognized as is the importance of their continuation.Research advisory structures are in place in most jurisdictions although in one or two locations progress has not been rapid. Present research programs are considered scientifically and technically sound for the most part. Lack of funding continues to be a matter of serious concern.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Forest fire, eviction, climate change, economics"

1

International Symposium on Fire Economics, Planning, and Policy (4th 2012 Mexico City, Mexico). Fourth International Symposium on Fire Economics, Planning, and Policy: Climate change and wildfires, Mexico City, Mexico, November 5-11, 2012. Fort Collins, CO: United States Department of Agriculture, Forest Service, Pacific Southwest Research Station, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Forest fire, eviction, climate change, economics"

1

Lorbiecki, Marybeth. "Aldo’s Students and Colleagues." In A Fierce Green Fire. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780199965038.003.0022.

Full text
Abstract:
As you walk into the current University of Wisconsin-Madison’s Department of Forest and Wildlife Ecology, on the second floor of Russell Laboratories, you’ll see it is a far cry from Leopold’s 424 Farm Place, next to the university cow barns. Even so, resting just outside the department’s office door is a handmade Leopold Bench—one of those simply designed pieces with crossed-plank legs holding up a plank seat and back. As the Environmental Protection Agency’s Landscaping site states: “To spy a Leopold bench in someone’s yard is to know something about the family who there resides. … Its form, resting alone under a tree or in congregation around a fire-pit, reminds us of Leopold’s thoughtfulness.” This handmade blond bench, though, is over a half-century old. It was a gift to the Professor from his department—and wood-burned into it are the names of Aldo’s secretaries and graduate students for him to remember them by, and now for us to do the same. The department, of course, has changed radically since Aldo unexpectedly left. It web page displays a photo of Aldo in the upper corner and lists twenty-two faculty members, four of whom are women (which he would have liked). The fields of expertise presented at first seem like Leopold methods and topics on steroids: forest biometry, forest genetics, molecular ecology, forest remote sensing, spatial analysis, modern climate change. Other specialties are perspectives he had already been integrating into his thinking and planning: landscape ecology, forest ecosystem ecology, tree physiology, forest and environmental history, conservation biology, land use/land cover change, hydrology, population dynamics, conservation management extension, resource policy, ecosystem management, society and natural resources. Scanning the expertise of the emeritus and affiliate faculty, you can see even further outgrowths of Leopold’s far-ranging, integrated thinking and imagining: forest pathology, natural resource and land economics, biogeochemistry, international forestry, development planning, recreation management, economic forecasting, forest soils, human behavior and resource management, nutrient and carbon cycling in forest, nursery, and urban ecosystems.
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography