Academic literature on the topic 'N.S.W. H.S.C. Geography Syllabus'

Bioclimatology is applied for growing tea in the West of Nghe An province, where the tea is considered as a high economic efficient plant to be priorly cultivated for reducing poverty and getting rich. Based on the bioclimatic characteristics of tea plant and regional climatic data from 1980 to 2014, the bioclimatic diagrams are built and the tea cultivability is mapped in term of annual average temperature and total precipitation, for this region with regarding its district of Con Cuong as an analytical key. The climate, including both temperature and precipitation, in Con Cuong is relatively suitable for the tea plantation. The Western Nghe An, a land of approx. 1.4 million ha, could be classified in five areas with different suitability for tea plant. The unfavorable area occupies only 1% of total region and the four favorable rests account for 99% of total, in which, the most favorable area is largest with about 746,355 ha, i.e. over 50% of whole region. The three other areas are cultivable but they are less favorable in terms of either temperature or precipitation. Growing tea in Western Nghe An, even in favorable areas, it should be taken into account of the weather disadvantages in certain moments of the year such as extreme dry, cold, hot and rainy events.ReferencesAhmed S., 2014. Tea and the taste of climate change, www.herbalgram.org, issue, 103, 44–51.Ahmed S., Stepp J.R., Orians C., Griffin T., Matyas C., 2014. Effects of extreme climate events on tea (Camellia sinensis) functional quality validate indigenous farmer knowledge and sensory preferences in tropical China. PloS one, 9(10), e109126.Bhagat R.M., Deb Baruah R., Safique S., 2010. climate and tea [camellia sinensis (l.) o. kuntze] Production with Special Reference to North Eastern India: A Review. Journal of Environmental Research And Development, 4(4), 1017–1028.Carr M., 1972. The Climatic Requirements of the Tea Plant: A Review. Experimental Agriculture, 8(01), 1–14. https://doi.org/10.1017/S0014479700023449.Carr M.K.V., Stephen W., 1992. Climate, weather and the yield of tea. In: Tea Cultivation to consumtpion. K.C. Wilson and M.N. Clifford (Eds). Chapman and Hall, 87–135.Daleen Lotter, David le Maitre, 2014. Modeling the distribution of Aspalathus linearis (Rooibos tea): implications of climate change for livelihoods dependent on both cultivation and harvesting from the wild. Ecology and Evolution, 4(8), 1209–1221.Ducan J.M.A., Saikia S.D., Gupta N., Biggs E.M., 2016. Observing climate impacts on tea yield in Assam, India. Applied Geogr., 77, 64–71.Institute of Geography, 2016. Department of Climatically Geography. The precipitation and temperature data at meteorological measuring stations in the West of Nghe An Province between 1984 and 2014. Data stored at Department of Climatically Geography, Institute of Geography, Ha Noi, 46p.Gaussen H., 1954. 8 ème Congrès international de Botanique. Section 7 et 3. Paris.Hadfield W., 1976. The effect of high temperature on some aspects of the physiology and cultivation of tea bush (Camellia sinensis) in North East India. In: Light as an Ecological factor. G.C. Evans, R. Bainbridge and O. Rackham (Eds.) Blackwel Sci. Publ., London, 477–495.Hoang Luu Thu Thuy, 2012. The comprehensive assessment of natural, socio-economic and environmental conditions for environmental protection planning in Nghe An Province. Doctoral Thesis. Institude of Geography, Hanoi, 150p.Huang Shoubo, 1989. Meteorology of tea plants in China: a review. Agri. Forest Meteorol., 47, 19–30.Huang Shoubo, 1991. A study on the ecological climates of some famous tea growing areas in high mountainous regions of China. Chinese Geographical Science, 1(2), 121–128.International Center for Tropical Agriculture, 2017. Identification of suitable tea growing areas in Malawi under climate change scenarios. Ciat report, Cali, Colombia, 39p.Kabir S.E., 2001. A study on Ecophysiology of Tea (Camellia sinensis) with special reference to the influence of climatic factors on physiology of a few selected Tea clones of Darjeering. International Journal of Tea Science, 1(4), 1–9.Kandiah S., Thevadasan T., 1980. Quantification of weather parameters to predict tea yields. Tea Q., Srilanka, 49(1), 25–33.Kaye L., 2014. Climate change threatens Sri Lanka’s tea industry. Triple Pundit: People, Planet, Profit. Available at: www.triplepundit.com/2014/06/climate-changethreatens-sri-lanka-tea-industry. Accessed July 25, 2014.Nakayama A., Harada S., 1962. Studies on the effect on the growth of tea plant. IV. The effect of temperature on the growth of young plants in summer. Bull. Tea Res. Station, Japan, 1, 28–40.Nguyen Bao Ve, 2005. The syllabus of industrial trees. Hanoi Argricultural Publishing House, 224p.Nguyen Dai Khanh, 2003. The assessment of agricultural climatic conditions for tea’s growth in major tea regions of Vietnam. Doctoral Thesis. Institute of Meteorology and Hydrology, 149p.Nguyen Khanh Van, Nguyen Thi Hien, Phan Ke Loc, Nguyen Tien Hiep, 2000. The bioclimatic diagrams of Vietnam. Vietnam National University Publishing House, Ha Noi, 126p.Nguyen Van Hong, 2017. Analyzing, assessing landscape for agriculture, forestry development and biodiversity conservation in the southwestern border districts in Nghe An province. Doctoral thesis. Vietnam National University, Hanoi, 150p.Nguyen Van Tao (ed.), 2004. Completing the asexual propagation process of LDP1 and LDP2 cultivars by cuttings in order to transfer to production. State Project of production pilot, coded KC.06.DA.09.NN. Institute of Tea Research, Phu Tho, 50p.Nkomwa E.C., Joshua M.K., Ngongondo C., Monjerezi M., Chipungu F., 2014. Assessing indigenous knowledge systems and climate change adaptation strategies in agriculture: A case study of Chagaka Village, Chikhwawa, Southern Malawi. Physics and Chemistry of the Earth, Parts A/B/C, 67–69, 164–172.Pham Hoang Ho, 2003. An Illustrated Flora of Vietnam, 2, 430–434. Youth Publishing House, 952p.Rebecca Boehm, Sean B. Cash, Bruce T. Anderson, Selena Ahmed, Timothy S. Griffin, Albert Robbat Jr., John Richard Stepp, Wenyan Han, Matt Hazel and Colin M. Orians, 2016. Association between Empirically Estimated Monsoon Dynamics and Other Weather Factors and Historical Tea Yields in China: Results from a Yield Response Model. Climate, 4, 20; doi:10.3390/cli4020020. www.mdpi.com/journal/climate.Schepp K., 2014. Strategy to adapt to climate change for Michimikuru tea farmers in Kenya. Adap CC Report. 2008. Available at: www.adapcc.org/en/kenya.htm. Accessed July 25, 2014.Sen A.R., Biswas A.K., Sanyal D.K., 1966. The Influence of Climatic Factors on the Yield of Tea in the Assam Valley, J. App. Meteo., 5(6), 789–800.Statistics Office of Nghe An Province, 2016. The annual abstracts of statistics 2015. Nghe An Publishing House, Nghe An, 453p.Tanton T.W., 1982. Environmental factors affecting yield of tea (camellia sinensis). Effect of air temperature. Expl. Agri., 18, 47–52.The People’s Committee of Nghe An Province, 2013. The Decision No. 448/QĐ-UBND dated 31/01/2013 to approve the hi-tech agriculture planning on the production of tea in Nghe An Province.The People’s Committee of Nghe An Province, 2013. The Decision No. 6290/QĐ-UBND dated 24/12/2013 to approve the adjustments and supplements for the development of Nghe An tea Industrial zone planning in 2013–2020.Walter H, Lieth, 1967. Klimadiagram - Weltatlas. Veb Gustav Fischer Verlag Jena.Wijeratne M.A., 1996. Vulnerability of Sri Lanka tea production to global climate change. Water, Air and Soil Pollution, 92(1-2), 87–94.Wijeratne M.A., Anandacoomaraswamy A., Amarathunga M., Ratnasiri J., 2007. Assessment of impact of climate change on productivity of tea (Camellia sinensis L.) plantations in Sri Lanka, 119–126.http://nghean.gov.vn, 05/06/2015. Many crops are withered in Con Cuong.http://baonghean.vn, 25/03/2013. Drought threaten rice and tea in Con Cuong. http://baonghean.vn/con-cuong-han-han-de-doa-lua-che-44581.html.

This study aims to determine the utilization of forests conducted by communities in forest area of Wolasi sub-district. This research was conducted in Wolasi forest sub-district of Southeast Sulawesi Province. The research methods used were survey and interview with 86 and 87 respondents with purposive sampling by plotting two different villages characteristics to represent forest utilization form in different area (villages in lowland landforms) and Aoma village (village has the shape of hilly terrain). The result of this study examining the forests utilization of inhabitants in Wolasi sub-district, which is represented by two topographic characteristics. Reseacher selected two similar conditions that engage the forest as a source of daily needs and workface that is considered as a resource, producing timber and non-timber products. Ranowila inhabitants occupations are dominated with farmers, while in Leleka village, despite being farmers, some inhabitants are craftsmen of non-timber forest products such as bamboo and rattan as well as furniture entrepreneur. This fact is influenced by residential areas that close to the forest and can be observed through the neighbourhood area which is close to the temporary forest area in Leleka Village. Temporary forest is located in an area which always extends land, since the topography circumstance encourages numerous people prefer to live closely towards their managed land. The forest is beneficial both in the term of land utilization or forest products,due to the distance there is limited knowledge,as well as limited work, it enhances low expenditure obtained. Keywords: Utilization, Forest, Community in District Wolasi References Adhikari, B., Di Falco, S., & Lovett, J. C. (2004). Household characteristics and forest dependency: evidence from common property forest management in Nepal. Ecological economics, 48(2), 245-257. Nurbaya & Efransjah (2018). The State of Indonesia’s Forests 2018. Jakarta: Ministry of Environment and Forestry Republic of Indonesia. Aryadi, M. (2012). Hutan rakyat: fenomenologi adaptasi budaya masyarakat. UPT Penerbitan, Universitas Muhammadiyah Malang. Banowati, E., & Sriyanto.(2013). Geografi Pertanian. Yogyakarta:Ombak. Cannon, C. H., Summers, M., Harting, J. R., & Kessler, P. J. (2007). Developing conservation priorities based on forest type, condition, and threats in a poorly known ecoregion: Sulawesi, Indonesia. Biotropica, 39(6), 747-759. Dudley, N., Schlaepfer, R., Jackson, W., Jeanrenaud, J. P., & Stolton, S. (2012). Forest quality: assessing forests at a landscape scale. Routledge. Gibson, L., Lee, T. M., Koh, L. P., Brook, B. W., Gardner, T. A., Barlow, J., ... & Sodhi, N. S. (2011). Primary forests are irreplaceable for sustaining tropical biodiversity. Nature, 478(7369), 378. Parrotta, J.A.(2015). The Historical, Environmental and Socio-economic Context of Forests and Tree-based Systems for Food Security and Nutrition. 1(3), 72-136. Pongtuluran, Y. (2015). Manajemen sumber daya alam dan lingkungan. Penerbit Andi. Silaen, A.P. (2008).Preservation of Forest and Environmental Functions Environmental Law Perspectives. 16 (3), 218-594. Suradi, S. (2012). Pertumbuhan Ekonomi dan Kesejahteraan Sosial. Sosio Informa, 17(3). Wagner, S., Nocentini, S., Huth, F., & Hoogstra-Klein, M. (2014). Forest management approaches for coping with the uncertainty of climate change: trade-offs in service provisioning and adaptability. Ecology and Society, 19(1). Wirakusumah, S. (2003). Mendambakan Kelestarian Sumber Daya Hutan Bagi Sebesar-Besarnya Kemakmuran Rakyat. Penerbit Universitas Indonesia. Jakarta. Cetakan Pertama. Copyright (c) 2019 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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). 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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. 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This article presents the results of the first implementation of map literacy learning model in middle school classes - this is the preliminary test. The implementation of this learning model will gain optimal results when it is conducted by following all the component of the model such as the syntax, theoretical framework, social system, teachers' roles, and support system. After the model implementation has been completed, the results showed that there was significantly different in students' spatial thinking skills before and after the treatment. However, the implementation also revealed that the model has some technical issues and thus to be improved. In a social system revision, the teacher has to be flexibly provide scaffolding every time he/she sees that the students need it. Teacher's book is significantly important to help a teacher lead the learning process. After improvement of the model has been completed, then it is ready to be implemented in the main field testing stage. Keywords: map literacy, social studies learning, spatial thinking References Abbasnasab, S., Rashid, M., & Saad, M. (2012). Knowledge with Professional Practice A Sociocultural Perspective on Assessment for Learning : The Case of a Malaysian Primary School ESL Context, 66, 343–353. http://doi.org/10.1016/j.sbspro.2012.11.277 Adeyemi, S. B., & Cishe, E. N. (2015). Effects of Cooperative and Individualistic Learning Strategies on Students’ Map Reading and Interpretation. International Journal of Arts & Sciences, 8(7), 383–395. Bednarz, S. W., Acheson, G., & Bednarz, R. S. (2006). Maps and Map Learning in Social Studies. Social Education, 70(7), 398–404. http://doi.org/10.4324/9780203841273 Brophy, J., & Alleman, J. (2009). Meaningful social studies for elementary students. Teachers and Teaching, 15(3), 357–376. http://doi.org/10.1080/13540600903056700 Cohen, L., Manion, L., Morrison, K., & Wyse, D. (2010). A Guide To Teaching Practice (5th ed.). London and New York: Rotledge. Churcher, K. M. A., Downs, E., & Tewksbury, D. (2014). “ Friending ” Vygotsky : A Social Constructivist P edagogy of Knowledge Building Through Classroom Social Media Use, 14(1), 33–50. Durmuş, Y. T. (2016). Effective Learning Environment Characteristics as a requirement of Constructivist Curricula: Teachers’ Needs and School Principals’ Views. International Journal of Instruction, 9(2), 183–198. http://doi.org/10.12973/iji.2016.9213a Fani, T., & Ghaemi, F. (2011). Implications of Vygotsky ’ s Zone of Proximal Development ( ZPD ) in Teacher Education : ZPTD and Self-scaffolding. Procedia - Social and Behavioral Sciences, 29(Iceepsy), 1549–1554. http://doi.org/10.1016/j.sbspro.2011.11.396 Gauvain, M. (1993). The Development of Spatial Thinking in Everyday Activity. Developmental Review, 13, 92–121. Hribar, G. C. (2015). Using Map-Based Investigations with Elementary Students. In ESRI Education GIS Conference (pp. 1–26). Huynh, N. T., & Sharpe, B. (2013). 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Proximate analysis of Dioscorea hispida tubers, collected from five locations around Leuser ecosystem in Aceh Province, showed variations amongst samples. Standard AOAC method for proximate analysis of the fresh weight showed that the water content varied between 15.8 - 37.8%, crude protein 1.13 -6.20%, crude lipid 1.99 - 9.36% and ash 0.29 - 1.24%. The total carbohydrate was high, i.e. between 58.3 -71.9%. The main mineral was phosphorus, with a value of 11.7 - 46.9 mg/100g. These variations could be due to soil, climate and weather factors, as well as postharvest handling. Phytochemical tests showed that all of the samples contained alkaloids and terpenoids. One of the samples (LP) also contained phenol and steroid. The high cyanide content in the tubers (379 - 739 ppm) was easily removed by repeated washing. The cyanide level dropped significantly after the 3rd wash. Information on nutritional content in D. hispida is essential for planning its utilization. Increasing the economic value of D. hispida is expected to attract people around the Leuser ecosystem to cultivate and utilize it, thereby reducing illegal forest encroachment.Keywords: Dioscorea hispida, proximate, Leuser, janeng, gadung, starchREFERENCESBarton H 2014 Yams: Origins and Development, Encyclopaedia of Global Archaeology, p 7943-7947, (Springer. DOI 10.1007/978-1-4419-0465-2_2193).Obidiegwu J E and Akpabio E M 2017 The Geography of Yam Cultivation in Southern Nigeria: Exploring Its Social Meanings and Cultural Functions J. Ethnic Foods 4 28-35.Chandrasekara A and Kumar T J 2016 Roots and Tuber Crops as Functional Foods: A Review on Phytochemical Constituents and Their Potential Health Benefits Intl. J. Food Sci. 2016 1-15.Kumar S, Das G, Shin H-S and Patra J K 2017 Dioscorea spp. (A Wild Edible Tuber): A Study on Its Ethnopharmacological Potential and Traditional Use by the Local People of Similipal Biosphere Reserve India Front. 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The people who live in the Merapi area have been going on for years. Merapi is the most active volcano in Central Java that can threaten the community, but the community still exists today, of course, having local wisdom in responding to the eruption of Merapi. This study aims to determine the local wisdom of Wonolelo Village before, during, and after the Merapi eruption. In addition, to find out the historical relationship of the Merapi eruption to local wisdom and the challenges faced by Wonolelo Village in maintaining the sustainability of local wisdom. This research was used as a descriptive qualitative method. The method of collecting data is done through observation, in-depth interviews, and documentation. Data sources of this study are community leaders, spiritual leaders, and people who are more than 70 years old. Analysis of the data used is sourced triangulation based on the Miles & Huberman model. The results showed that local wisdom in responding to the Merapi eruption in Wonolelo Village still exists today. Local wisdom is divided into three segments, namely before, during, and after the eruption of Merapi. Local wisdom before the Merapi eruption is a notification that Merapi eruption activity will occur. Local wisdom in Wonolelo Village has challenges in the form of modernization and not running the local wisdom relay to young people. Keywords: Disaster, Local wisdom, Merapi volcano. References Andreastuti, S.D., Newhall, C., Dwiyanto, J. (2006). Menelusuri Kebenaran Letusan Gunung Merapi 1006. Jurnal Geologi Indonesia, Vol. 1, No. 4, Hal. 201-207. Andreastuti, S., Paripurno, E., Gunawan, H., Budianto, A., Syahbana, D., & Pallister, J. (2019). Character of community response to volcanic crises at sinabung and kelud volcanoes. Journal of Volcanology and Geothermal Research, 382, 298-310. doi:10.1016/j.jvolgeores.2017.01.022 Atmojo, S. E., Rusilowati, A., Dwiningrum, S. I. 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Local wisdom-based disaster recovery model in indonesia. Disaster Prevention and Management: An International Journal, 21(3), 351-369. doi:10.1108/09653561211234525 Lestari, P., Kusumayudha, S. B., Paripurno, E. T., & Jayadianti, H. (2016). Environmental communication model for disaster mitigation of mount sinabung eruption karo regency of north sumatra. Information (Japan), 19(9B), 4265-4270. Magelang, B. P. S. K. (2018). Kabupaten Magelang dalam Angka. Boyolali : Badan Pusat Statistik Marfai, M.A. (2011). Jakarta flood hazard and community participation on disaster preparedness. Prosiding dalam seminar Community preparedness and disaster management, center for religious and cross-cultural studies, UGMI, no. 2/2011 (december), Hlm, 209-221. Marfai, M.A., & Hizbaron, D.R. (2011). Community’s adaptive capacity due to coastal flooding in semarang coastal city, Indonesia. International Journal of Seria Geografie, Annals of the Univeristy of Oradea. E-ISSN 2065-1619. Year XX. 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This study aim to attempt mapping out the Land Use or Land Cover (LULC) status of Regional Project Coordination Committee (RPCC) between 2009-2019 with a view of detecting the land consumption rate and the changes that has taken place using RS and GIS techniques; serving as a precursor to the further study on urban induced variations or change in weather pattern of the cityn Rangpur City Corporation(RCC) is the main administrative functional area for both of Rangpur City and Rangpur division and experiencing a rapid changes in the field of urban sprawl, cultural and physical landscape,city growth. These agents of Land use or Land cover (LULC) varieties are responsible for multi-dimensional problems such as traffic congestion, waterlogging, and solid waste disposal, loss of agricultural land. In this regard, this study fulfills LULC changes by using Geographical Information Systems (GIS) and Remote Sensing (RS) as well as field survey was conducted for the measurement of change detection. The sources of data were Landsat 7 ETM and landsat 8 OLI/TIRS of both C1 level 1. Then after correcting the data, geometrically and radiometrically change detection and combined classification (supervised & unsupervised) were used. The study finds LULC changes built-up area, water source, agricultural land, bare soil in a change of percentage is 17.23, 2.58, -9.94, -10.19 respectively between 2009 and 2019. Among these changes, bare soil is changed to a great extent, which indicates the expansion of urban areas is utilizing the land to a proper extent. Keywords: Urban expansion; land use; land cover; remote sensing; geographic information system (GIS); Rangpur City Corporation(RCC). References Al Rifat, S. A., & Liu, W. (2019). Quantifying spatiotemporal patterns and major explanatory factors of urban expansion in miami metropolitan area during 1992-2016. Remote Sensing, 11(21) doi:10.3390/rs11212493 Arimoro AO, Fagbeja MA, Eedy W. (2002). The Need and Use of Geographic Information Systems for Environmental Impact Assessment in Africa: With Example from Ten Years Experience in Nigeria. AJEAM/RAGEE, 4(2), 16-27. Belal, A.A. and Moghanm, F.S. (2011).Detecting Urban Growth Using Remote Sensing and GIS Techniques in Al Gharbiya Governorate, Egypt.The Egyptian Journal of Remote Sensing and Space Science, 14, 73-79. http://dx.doi.org/10.1016/j.ejrs.2011.09.001 Dewan, A.M. and Yamaguchi, Y. (2009). Using Remote Sensing and GIS to Detect and Monitor and Use and Land Cover Change in Dhaka Metropolitan of Bangladesh during 1960-2005. Environmental Monitor Assessment, 150, 237- 249. Retrieved from http://dx.doi.org/10.1007/s10661-008-0226-5 Djimadoumngar, K.-N., & Adegoke, J. (2018). Satellite-Based Assessment of Land Use and Land Cover (LULC) Changes around Lake Fitri, Republic of Chad. Journal of Sustainable Development, 11(5), 71. doi:10.5539/jsd.v11n5p71 Edwards, B., Frasch, T., & Jeyacheya, J. (2019). Evaluating the effectiveness of land-use zoning for the protection of built heritage in the bagan archaeological zone, Myanmar—A satellite remote-sensing approach. Land use Policy, 88 doi:10.1016/j.landusepol.2019.104174 Fallati, L., Savini, A., Sterlacchini, S., & Galli, P. (2017). Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data. Environmental Monitoring and Assessment, 189(8). doi:10.1007/s10661-017-6120-2 Fučík, P., Novák, P., & Žížala, D. (2014). A combined statistical approach for evaluation of the effects of land use, agricultural and urban activities on stream water chemistry in small tile-drained catchments of south bohemia, czech republic. 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Journal of Poverty, Investment and Development, 8. Mondal, I., Srivastava, V. K., Roy, P. S., & Talukdar, G. (2014). Using logit model to identify the drivers of landuse landcover change in the lower gangetic basin, india. Paper presented at the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, , XL-8(1) 853-859. doi:10.5194/isprsarchives-XL-8-853-2014 Navale, V. B., & Mhaske, S. Y. (2019). Land use/land cover changes in sangamner city by using remote sensing and GIS. International Journal of Recent Technology and Engineering, 8(2), 4614-4621. doi:10.35940/ijrte.B3386.078219 Nicolson, L.D. (1987). The Greening of the cities; Routledge and Kegan Paul, London Nong, D., Fox, J., Miura, T., & Saksena, S. (2015). Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data. Land, 4(4), 1213–1231. doi:10.3390/land4041213 Park, H., Fan, P., John, R., Ouyang, Z., & Chen, J. (2019). Spatiotemporal changes of informal settlements: Ger districts in ulaanbaatar, mongolia. Landscape and Urban Planning, 191 doi:10.1016/j.landurbplan.2019.103630 Rajeshwari D. (2006). Management of the Urban Environment Using Remote Sensing and Geographic Information Systems.J. Hum. Ecol., 20(4), 269-277. Retrieved from http://www.krepublishers.com/02_journals/JHE/ Rasul, A., Balzter, H., Ibrahim, G., Hameed, H., Wheeler, J., Adamu, B., … Najmaddin, P. (2018). Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates. Land, 7(3), 81. doi:10.3390/land7030081 Risma, Zubair, H., & Paharuddin. (2019). Prediction of land use and land cover (LULC) changes using CA-Markov model in Mamuju Subdistrict. Journal of Physics: Conference Series, 1341, 082033. doi:10.1088/1742-6596/1341/8/082033 Schilling, K. E., Jha, M. K., Zhang, Y.-K., Gassman, P. W., & Wolter, C. F. (2008). Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resources Research, 44(7). doi:10.1029/2007wr006644 Copyright (c) 2019 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

The study aims to improve the learning outcomes in the field of knowledge and inquiry skill in class VIII 5 SMP Negeri 14 Kendari on the subject matter of light in atmosphere as the effect of applying the guided inquiry learning model assisted by science KIT. The method of the study used a classroom action research with research design is cycle model. The research subject is the students of class VIII 5 SMP Negeri 14 Kendari in the academic year 2016/2017 which consist of 26 students. The learning data achievements of the learners' realm were obtained through the learning result test (cycle test), the skill data of the learners were obtained through the inquiry sheet, and then was analyzed used the descriptive statistics. Results of data analysis are: 1) learning outcomes increased from 60,31 in cycle I to 75 in cycle II; 2) the students group inquiry skill increased form average value 2.68 (enough category) in the cycle I to 3.15 (good category) in cycle II; 3) the students mastery learning percentage increase from 42.31% (11 students) in cycle I to 77% (20 students) in cycle II. It could be concluded that the implementation of guided inquiry learning model assisted by science KIT could improve the learning outcomes of knowledge and inquiry skill domain on Class VIII 5 SMP Negeri 14 Kendari in the subject matter of light in atmosphere. Keywords: guided inquiry, inquiry skills, learning outcomes,science KIT. References Ahmadi, L. (2015). Pemanfaatan Alat KIT pada Pembelajaran.Gorontalo: Universitas Negeri Gorontalo. Aksa, F.I., Utaya, S., & Bachri, S. (2019). Geografi dalam Perspektif Filsafat Ilmu. Majalah Geografi Indonesia,33(1), 43-37. Amaliana, I. (2017). Teacher-centered or Student-centered Learning Approach to Promote Learning?. Jurnal Sosial Humaniora, 10(2), 59-70. Anderson, L.W., & Krathwohl, D.R. (2015). Kerangka Landasan untuk Pembelajaran, Pengajaran, dan Asesmen Revisi Taksonomi Pendidikan Bloom (A. Priantoro, Trans.). Yogyakarta: Pustaka Pelajar. Arikunto, S. (2016). Prosedur Penelitian Suatu Pendekatan Praktik. Jakarta: Rineka Cipta. Depdiknas. (2004). Kurikulum Mata PelajaranSains SMP danMTs.Jakarta: Depdiknas. Greenwald, R.R.,&Quitadamo, I.J. (2014). A Mind of Their Own: Using Inquiry-based Teaching to Build Critical Thinking Skills and Intellectual Engagement in an Undergraduate Neuroanatomy Course. The Journal of Undergraduate Neuroscience Education, 12(2), 100-106. Hardianti, T., & Kuswanto, H. (2017). Difference among Levels of Inquiry: Process Skills Improvement at Senior High School in Indonesia. International Journal of Instruction, 10(2), 119-130. Hidayati, D.N., Amaluddin, L.O., & Surdin. (2016). The Effect Guided Inquiry to Critical Thinking Ability to Build Student Character in Geography Subject. Social Science, Education and Humanities Research,9(1), 367-371. Kuhlthau, C.C., Maniotes, L.K., & Caspari, A.K. (2015). Guided Inquiry: Learning In The 21st Century(2nd ed.). California: Libraries Unlimeted. Mulyana, S., Rusdi, & Vivanti, D. (2018). The Effect of Guided Inquiry Learning Model and Scientific Performance on Student Learning Outcome. Indonesian Journal of Science and Education, 2(1), 105-109. Niana, R., Sarwanto, & Ekawati, E.Y. (2016). The Application of Guided Inquiry Model on Physic Learning to Improve Scientific Attitude and Students Analysis Ability. Proceedings of the 2nd International Conference on Teacher Training and Education Sebelas Maret University,2(1), 605-615. Piaget, J. (1970). Science of Education and the Psychology of The Child. New York: Wiley. Putra, M.I.S., Widodo, W., & Jatmiko, B. (2016). 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The Effect of Guided Inquiry Learning Method VS Free Inquiry Against Learning Outcomes. International Conference on EducationUM, 561-568. Zaini, M. (2016). Guided Inquiry Based Learning on the Concept of Ecosystem Toward Learning Outcomes and Critical Thinking Skills of High School Student. IOSR Journal of Research & Method in Education (IOSR-JRME), 6(6), 50-55. Copyright (c) 2019 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Climate change induced sea-level rise (SLR) is on its increase globally. Regionally the lowlands of China, Vietnam, Bangladesh, and islands of the Malaysian, Indonesian and Philippine archipelagos are among the world’s most threatened regions. Sea-level rise has major impacts on the ecosystems and society. It threatens coastal populations, economic activities, and fragile ecosystems as mangroves, coastal salt-marches and wetlands. This paper provides a summary of the current state of knowledge of sea level-rise and its effects on both human and natural ecosystems. The focus is on coastal urban areas and low lying deltas in South-East Asia and Vietnam, as one of the most threatened areas in the world. About 3 mm per year reflects the growing consensus on the average SLR worldwide. The trend speeds up during recent decades. The figures are subject to local, temporal and methodological variation. In Vietnam the average values of 3.3 mm per year during the 1993-2014 period are above the worldwide average. Although a basic conceptual understanding exists that the increasing global frequency of the strongest tropical cyclones is related with the increasing temperature and SLR, this relationship is insufficiently understood. Moreover the precise, complex environmental, economic, social, and health impacts are currently unclear. SLR, storms and changing precipitation patterns increase flood risks, in particular in urban areas. Part of the current scientific debate is on how urban agglomeration can be made more resilient to flood risks. Where originally mainly technical interventions dominated this discussion, it becomes increasingly clear that proactive special planning, flood defense, flood risk mitigation, flood preparation, and flood recovery are important, but costly instruments. Next to the main focus on SLR and its effects on resilience, the paper reviews main SLR associated impacts: Floods and inundation, salinization, shoreline change, and effects on mangroves and wetlands. The hazards of SLR related floods increase fastest in urban areas. This is related with both the increasing surface major cities are expected to occupy during the decades to come and the increasing coastal population. In particular Asia and its megacities in the southern part of the continent are increasingly at risk. The discussion points to complexity, inter-disciplinarity, and the related uncertainty, as core characteristics. An integrated combination of mitigation, adaptation and resilience measures is currently considered as the most indicated way to resist SLR today and in the near future.References Aerts J.C.J.H., Hassan A., Savenije H.H.G., Khan M.F., 2000. Using GIS tools and rapid assessment techniques for determining salt intrusion: Stream a river basin management instrument. 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