Academic literature on the topic 'Land subdivision (N.S.W.)'

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Journal articles on the topic "Land subdivision (N.S.W.)"

1

Javaid, Muhammad, Hafiz Usman Afzal, and Shaohui Wang. "Complexity of Some Generalized Operations on Networks." Complexity 2021 (July 13, 2021): 1–29. http://dx.doi.org/10.1155/2021/9999157.

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The number of spanning trees in a network determines the totality of acyclic and connected components present within. This number is termed as complexity of the network. In this article, we address the closed formulae of the complexity of networks’ operations such as duplication (split, shadow, and vortex networks of S n ), sum ( S n + W 3 , S n + K 2 , and C n ∘ K 2 + K 1 ), product ( S n ⊠ K 2 and W n ∘ K 2 ), semitotal networks ( Q S n and R S n ), and edge subdivision of the wheel. All our findings in this article have been obtained by applying the methods from linear algebra, matrix theory, and Chebyshev polynomials. Our results shall also be summarized with the help of individual plots and relative comparison at the end of this article.
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Zhao, Zi Qi, Li Guang Li, Hong Bo Wang, Xian Li Zhao, and Peng Jiang. "Characteristics of Land Surface Temperture (LST) within the Third Ring Road of Shenyang." Applied Mechanics and Materials 737 (March 2015): 913–16. http://dx.doi.org/10.4028/www.scientific.net/amm.737.913.

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Urban heat island (UHI) effect becomes hot spot in the field of urban climatology in the recent decades. Two sampling belts on the Landsat TM/ETM+ image across the center point of main urban area of shenyang were selected along the E-W and S-N directions in order to analyse the characteristics of UHI effect and discuss the relationships between LST and UHI source or sink. The results indicate that for the E-W direction sampling belt, the maximum and minimum LST values were 37.46 °Cand 33.44 °C in 2001 respectively, while those were 34.61 °C and 33.30 °C in 2010. For the S-N direction sampling belt, the corresponding values were 34.53 °C and 29.27 °C in 2001, 34.47 °C and 29.69 °C in 2010. LST fluctuated significantly in the E-W direction sampling belt in 2010 and the difference value was 4.01 °C, so was in the S-N direction sampling belt in 2010 and the difference value was 4.78 °C. LST of the grid was a positive correlation with LST of the UHI source area of grid in 2001 and 2010, so was with that of UHI sink area in 2001 and 2010. LST of grid was a positive correlation with UHI source area.
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3

Matveyeva, N. V. "(A review) S. S. Kholod. Zonation in the plant cover on the Wrangel Island: syntaxonomical approach. Vegetation of Russia. 2013. N 23. P. 89–121." Vegetation of Russia, no. 25 (2014): 116–23. http://dx.doi.org/10.31111/vegrus/2014.25.116.

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The reviewed paper by S. Kholod (Kholod, 2013) presents the results of detail analysis of a large set of characteristics of 46 syntaxa (associations, subassociations, variants) described on the Wrangel Isl. (Kholod, 2007) according to the of Braun-Blanquet approach. Such parameters as the number and set of syntaxa, their correlations with the elements of landscapes, and the parameters of proper syntaxa composition and structure (number of species, projective cover, horizontal structure type, geographical range of elements, above-ground mass of vascular plants) are included into the analysis. The application of the results of the vegetation classification to the large area with complex geomorphology, geology and meso- and microclimatic conditions is undeniable novelty. The purpose of the S. Kholod paper, reflected in it title, was to assess the zonal position and to conduct a zonal subdivision of the territory of Wrangel Isl. using the syntaxonomical approach. This is undoubtedly should be appreciated having in mind that our knowledge on the syntaxa distribution, both in general and focusing on optimal allocation, their assemblage in different latitudinal stripes strongly increases the objectivity of the zonal division while the use of all mentioned characteristics makes it actually geobotanical (Matveyeva, 2008). However the conclusions and the results of zonal division (shown in the scheme, see Figure), made on the basis of the comprehensive syntaxonomical analysis, induce the numerous questions and even fundamental objections. It is worth to emphasize that islands, in general and in particular those with mountains and situated in the higher latitudes, are not the simplest objects for establishing their zonal status. This fully applies to the Wrangel Isl., where the mountains (albeit low), in most parts of the island, and the cold sea, around a relatively small area of land, leave no opportunity to manifest zonation in its correct (non-changed) form. Searching the zonal positions in the mountains is doomed to fail because this contradicts to the whole system of terms and phenomena taken into consideration when discussing the phenomenon of zonation. However, zonation is reflected not only in zonal but as well in intrazonal landscape elements, and that allows determining the zonal status of a territory when the space of zonal elements is minimized or even in their absence. The last case is not a cause to name as “zonal positions” some others that are widely represented in landscape like, for example, inside mountain valleys or carbonate substrates on Wrangel Isl. Thus, there are no grounds to call the localities, designated on the Fig. 3 in the reviewed paper (see Figure) by the letters «A», «Б», «В» and «Г» as units of zonal subdivision. Three isolated fragments under character A, that are relatively wide mountain valleys, are interpreted as the northern variant of the typical tundra subzone due to the presence the shrub willows that are absent besides this, the most heat favorable, element of landscape. However, it is the presence of the mountains is the main cause for the slightly higher air temperature, due to the specific warm winds (foehn), and optimal snow cover. Without the mountains, this effect would be impossible, and in their absence there would be no shrub thickets. So their existence is a beautiful example of «Alekhin’ feedforward rule» (Alekhin, 1951) when the specific syntaxa represent the extrazonal plant communities but in no case the presence of one zone within another. The analogous example is the location of fragments of polar desert zone and northern variants of arctic tundra subzone both in the north and south of the island. In particular this concerns the south-west island extremity on the Cape Blossom (with mean July temperature 1.1 С°), where the occurrence of polar desert syntaxa is the sequence of the hard ice conditions due to the specific configuration of the coast: long spit and thereby prolonged standing ice, which just is responsible for low summer temperatures. Hence, if the configuration of the coast in the south-west of the island would be different, neither polar desert nor the northern variant of arctic tundra in the southern half of the island would exist. But the configuration of the coastal line of the studied island has nothing to do with zonation. Also debatable is assignment to the zone of polar deserts the narrow strip in the north of the island where low summer temperatures are caused not by the amount of solar radiation / radiation balance, but the cooling effect of the ice cover persisting for most of the growing season, again due to the configuration of coastline with numerous lagoons and specificity of deepwater currents, as well as summer fogs, i. e. not with direct sequence of radiation factors. The occurrence of the communities of ass. Oncophoro wahlenbergii–Deschampsietum borealis, which has some similarities with syntaxa on the Bolshevik Isl. (Matveyeva, 2006), in wet habitats at long gentle macro-slope (mountain trail) of the northern exposure is another classic example of extrazonality, as in the case of communities with high shrub willows in mountain valleys, but with the opposite sighting. When assessing the zonal position of small area it is necessary to link a decision with the existing subdivision of the entire biome. The Wrangel Isl. is situated in relatively low latitudes (between 70° 46′ and 71° 34′ N) where the southern (shrub) tundra subzone is represented in the middle of the Eurasian continent. The southernmost areas of the polar desert zone in circumpolar scale are located north of 75° N in the warmest Atlantic sector and north of 77° N in continental part of the north Eurasia. Similar in size with Wrangel Isl., flat islands of Novosibirskie islands archipelago located between 73° and 76° N, are referred to the arctic tundra subzone. The total cooling effect of the Arctic Ocean affects not only the island territories. The presence of tundras on the vast space of the Eurasian coast (with the exception of the Yamal, Gydan and Taymyr peninsulas) at low latitudes can be explain not only by solar radiation regime, but also by the fact that the large areas of land are cut off by sea. Otherwise, on the territory of the present tundras south of 67–69° N would been the woods. In fact, only on the Taymyr Peninsula, that is mostly extended to the north, the radiation and thermal conditions are proportionate, and therefore all subzonal boundaries are situated there more north than in the European and East Siberian sectors. The oceanic influence appears all over the whole Arctic biome but it does not prevent manifest zonal differentiation from the southern tundras to the polar deserts. This factor, common for the entire Eurasian coast, is strongly enhanced by durable ice cover owing to the specific regional conditions on Wrangel Isl. The mixed pattern of zonal units (the location of polar desert zone south of the arctic tundras and that of typical tundras inside of latter), proposed by S. Kholod, destroys all current ideas of zonation. It is obvious that these are cases of the presence of some community types (by no means zones or subzones) in alien zonal positions (that always has a clear explanation). It is not possible to agree with the author, that all differences of syntaxonomical variability within the studied territory are connected with the zonal position of its various parts. It is rather common situation when the image of zonal subdivision appears based upon for some ideas, and then all identified differences have been linked with this image rather than with the landscape peculiarities. The usage of some terminology, concerning the names of elements of relief, the designation of zonal units, the terms of intra-landscape differentiation are also discussed with a certain amount of criticism.
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4

Loew, Alexander, Jian Peng, and Michael Borsche. "High-resolution land surface fluxes from satellite and reanalysis data (HOLAPS v1.0): evaluation and uncertainty assessment." Geoscientific Model Development 9, no. 7 (July 27, 2016): 2499–532. http://dx.doi.org/10.5194/gmd-9-2499-2016.

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Abstract. Surface water and energy fluxes are essential components of the Earth system. Surface latent heat fluxes provide major energy input to the atmosphere. Despite the importance of these fluxes, state-of-the-art data sets of surface energy and water fluxes largely differ. The present paper introduces a new framework for the estimation of surface energy and water fluxes at the land surface, which allows for temporally and spatially high-resolved flux estimates at the quasi-global scale (50° S, 50° N) (High resOlution Land Atmosphere Parameters from Space – HOLAPS v1.0). The framework makes use of existing long-term satellite and reanalysis data records and ensures internally consistent estimates of the surface radiation and water fluxes. The manuscript introduces the technical details of the developed framework and provides results of a comprehensive sensitivity and evaluation study. Overall the root mean square difference (RMSD) was found to be 51.2 (30.7) W m−2 for hourly (daily) latent heat flux, and 84 (38) W m−2 for sensible heat flux when compared against 48 FLUXNET stations worldwide. The largest uncertainties of latent heat flux and net radiation were found to result from uncertainties in the solar radiation flux obtained from satellite data products.
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5

Scafetta, Nicola. "Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations." Climate Dynamics 56, no. 9-10 (January 17, 2021): 2959–82. http://dx.doi.org/10.1007/s00382-021-05626-x.

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AbstractThe 0.6 °C warming observed in global temperature datasets from 1940 to 1960 to 2000–2020 can be partially due to urban heat island (UHI) and other non-climatic biases in the underlying data, although several previous studies have argued to the contrary. Here we identify land regions where such biases could be present by locally evaluating their diurnal temperature range (DTR = TMax − TMin trends between the decades 1945–1954 and 2005–2014 and between the decades 1951–1960 and 1991–2000 versus their synthetic hindcasts produced by the CMIP5 models. Vast regions of Asia (in particular Russia and China) and North America, a significant part of Europe, part of Oceania, and relatively small parts of South America (in particular Colombia and Venezuela) and Africa show DTR reductions up to 0.5–1.5 °C larger than the hindcasted ones, mostly where fast urbanization has occurred, such as in central-east China. Besides, it is found: (1) from May to October, TMax globally warmed 40% less than the hindcast; (2) in Greenland, which appears nearly free of any non-climatic contamination, TMean warmed about 50% less than the hindcast; (3) the world macro-regions with, on average, the lowest DTR reductions and with low urbanization (60S-30N:120 W–90 E and 60 S–10 N:90 E–180 E: Central and South America, Africa, and Oceania) warmed about 20–30% less than the models’ hindcast. Yet, the world macro-region with, on average, the largest DTR reductions and with high urbanization (30 N–80 N:180 W–180 E: most of North America, Europe, and Central Asia) warmed just a little bit more (5%) than the hindcast, which indicates that the models well agree only with potentially problematic temperature records. Indeed, also tree-based proxy temperature reconstructions covering the 30°N–70°N land area produce significantly less warming than the correspondent instrumentally-based temperature record since 1980. Finally, we compare land and sea surface temperature data versus their CMIP5 simulations and find that 25–45% of the 1 °C land warming from 1940–1960 to 2000–2020 could be due to non-climatic biases. By merging the sea surface temperature record (assumed to be correct) and an adjusted land temperature record based on the model prediction, the global warming during the same period is found to be 15–25% lower than reported. The corrected warming is compatible with that shown by the satellite UAH MSU v6.0 low troposphere global temperature record since 1979. Implications for climate model evaluation and future global warming estimates are briefly addressed.
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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.

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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). 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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.
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Larsen, P. H., and J. C. Escher. "The Silurian turbidite sequence of the Peary Land Group between Newman Bugt and Victoria Fjord, western North Greenland." Rapport Grønlands Geologiske Undersøgelse 126 (December 31, 1985): 47–67. http://dx.doi.org/10.34194/rapggu.v126.7911.

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Several lithological units of the Silurian Peary Land Group show a remarkable continuity along the E-W trending basin axis, but pronounced lateral facies changes occur N-S across the basin. An approximately 4000 m thick sequence of turbidites to the north in the deep-water basin represents the time equivalent of about 1065 m of turbidites, slope sediments and platform carbonates at the basin margin to the south. Ellesmerian regional deformation affected the northern part of the area showing a progressive increase of deformation of the deep-water sequence from south to north. The general strnctural pattern suggests a lithological anisotrophy within the upper part of the crnst with a buried carbonate shelf to the south bounded by an escarpment towards a deep silicic1astic basin to the north. The difference in total thickness of the Llandovery to Lower Ludlow sedimentary sequence between the south and north supports this hypothesis.
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Smith, G. Louis, Pamela E. Mlynczak, David A. Rutan, and Takmeng Wong. "Comparison of the Diurnal Cycle of Outgoing Longwave Radiation from a Climate Model with Results from ERBE." Journal of Applied Meteorology and Climatology 47, no. 12 (December 1, 2008): 3188–201. http://dx.doi.org/10.1175/2008jamc1924.1.

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Abstract The diurnal cycle of outgoing longwave radiation (OLR) computed by a climate model provides a powerful test of the numerical description of various physical processes. Diurnal cycles of OLR computed by version 3 of the Hadley Centre Atmospheric Model (HadAM3) are compared with those observed by the Earth Radiation Budget Satellite (ERBS) for the boreal summer season (June–August). The ERBS observations cover the domain from 55°S to 55°N. To compare the observed and modeled diurnal cycles, the principal component (PC) analysis method is used over this domain. The analysis is performed separately for the land and ocean regions. For land over this domain, the diurnal cycle computed by the model has a root-mean-square (RMS) of 11.4 W m−2, as compared with 13.3 W m−2 for ERBS. PC-1 for ERBS observations and for the model are similar, but the ERBS result has a peak near 1230 LST and decreases very slightly during night, whereas the peak of the model result is an hour later and at night the OLR decreases by 7 W m−2 between 2000 and 0600 LST. Some of the difference between the ERBS and model results is due to the computation of convection too early in the afternoon by the model. PC-2 describes effects of morning/afternoon cloudiness on OLR, depending on the sign. Over ocean in the ERBS domain, the model RMS of the OLR diurnal cycle is 2.8 W m−2, as compared with 5.9 W m−2 for ERBS. Also, for the model, PC-1 accounts for 66% of the variance, while for ERBS, PC-1 accounts for only 16% of the variance. Thus, over ocean, the ERBS results show a greater variety of OLR diurnal cycles than the model does.
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Zanutta, Antonio, Monia Negusini, Luca Vittuari, Leonardo Martelli, Paola Cianfarra, Francesco Salvini, Francesco Mancini, et al. "Victoria Land, Antarctica: An Improved Geodynamic Interpretation Based on the Strain Rate Field of the Current Crustal Motion and Moho Depth Model." Remote Sensing 13, no. 1 (December 29, 2020): 87. http://dx.doi.org/10.3390/rs13010087.

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In Antarctica, the severe climatic conditions and the thick ice sheet that covers the largest and most internal part of the continent make it particularly difficult to systematically carry out geophysical and geodetic observations on a continental scale. It prevents the comprehensive understanding of both the onshore and offshore geology as well as the relationship between the inner part of East Antarctica (EA) and the coastal sector of Victoria Land (VL). With the aim to reduce this gap, in this paper multiple geophysical dataset collected since the 1980s in Antarctica by Programma Nazionale di Ricerche in Antartide (PNRA) were integrated with geodetic observations. In particular, the analyzed data includes: (i) Geodetic time series from Trans Antarctic Mountains DEFormation (TAMDEF), and Victoria Land Network for DEFormation control (VLNDEF) GNSS stations installed in Victoria Land; (ii) the integration of on-shore (ground points data and airborne) gravity measurements in Victoria Land and marine gravity surveys performed in the Ross Sea and the narrow strip of Southern Ocean facing the coasts of northern Victoria Land. Gravity data modelling has improved the knowledge of the Moho depth of VL and surrounding the offshore areas. By the integration of geodetic and gravitational (or gravity) potential results it was possible to better constrain/identify four geodynamic blocks characterized by homogeneous geophysical signature: the Southern Ocean to the N, the Ross Sea to the E, the Wilkes Basin to the W, and VL in between. The last block is characterized by a small but significant clockwise rotation relative to East Antarctica. The presence of a N-S to NNW-SSE 1-km step in the Moho in correspondence of the Rennick Geodynamic Belt confirms the existence of this crustal scale discontinuity, possibly representing the tectonic boundary between East Antarctica and the northern part of VL block, as previously proposed by some geological studies.
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Rahman, Md Naimur. "Urban Expansion Analysis and Land Use Changes in Rangpur City Corporation Area, Bangladesh, using Remote Sensing (RS) and Geographic Information System (GIS) Techniques." Geosfera Indonesia 4, no. 3 (November 25, 2019): 217. http://dx.doi.org/10.19184/geosi.v4i3.13921.

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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. Environmental Earth Sciences, 72(6), 2195-2216. doi:10.1007/s12665-014-3131-y Elbeih, S. F., & El-Zeiny, A. M. (2018). Qualitative assessment of groundwater quality based on land use spectral retrieved indices: Case study sohag governorate, egypt. Remote Sensing Applications: Society and Environment, 10, 82-92. doi:10.1016/j.rsase.2018.03.001 Fasal, S. (2000). Urban expansion and loss of agricultural land – A GIS based study of Saharanpur City, India. Environment and Urbanization, 12(2), 133 – 149 He, S., Wang, X., Dong, J., Wei, B., Duan, H., Jiao, J., & Xie, Y. (2019). Three-dimensional urban expansion analysis of valley-type cities: A case study of chengguan district, lanzhou, china. Sustainability (Switzerland), 11(20) doi:10.3390/su11205663 Heimlich, R.E and W.D. Anderson. (2001). Development at the Urban Fringe and Beyond: Impacts on Agriculture and Rural Land. 803, Economic Research Service, U.S. Department of Agriculture, Washington D.C., pg 80 Im, N., Kawamura, K., Suwandana, E., & Sakuno, Y. (2014). Monitoring land use and land cover effects on water quality in cheung ek lake using ASTER images. American Journal of Environmental Sciences, 11(1), 1-12. doi:10.3844/ajessp.2015.1.12 Kalnay, E., & Cai, M. (2003). Impact of urbanization and land-use change on climate. Nature, 423(6939), 528-531. doi:10.1038/nature01675 Matlhodi, B., Kenabatho, P. K., Parida, B. P., & Maphanyane, J. G. (2019). Evaluating land use and land cover change in the gaborone dam catchment, botswana, from 1984-2015 using GIS and remote sensing. Sustainability (Switzerland), 11(19) doi:10.3390/su11195174 Uddin, M. M. M. (2015). Causal relationship between agriculture, industry and services sector for GDP growth in Bangladesh: An econometric investigation. 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
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Book chapters on the topic "Land subdivision (N.S.W.)"

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". Cha rles M a rte land th e P erip h ery: R e la t io n s w ith Aqu ita in e , B u rg undy , Provence , and th e Reg io n s E ast o f th e Rhine." In The Age of Charles Martel, 93–134. Routledge, 2016. http://dx.doi.org/10.4324/9781315845647-13.

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"T cu im rre e n tl Sycahleeasd ) qu aas rte wreeldlaatst he thLeammounltt -i D na oth io e n rt aylEIaR rt I h , odtrhoeurgm ht ajporrem di ocd ti eolnprw ob il llem re s q . u T ir hee the resolution of hOabvseearnva im to p ry o rt oafntCcooluupm le bdiamoUdneilvecrosm ity p . onTehnet, sea lt ehfo fo urgthsp ex hteernes , io onntaogfloorbeaclasdto in mga , in boatnhdth th eseeaorcee dva saonlnacn es diantcm lu odse ­ m in acn lu ydeodf ( t C he a rs toyn pe 1s9o 98 f ) m . ethods discussed above are uomciesamnatacnhdbaettmwoesepnhtehree . fl Fuo xe rsmaatntyhearbeoaus, n d th atr io ie nsoofftthhee rep F li o ca rtE in NgSaOn , d c , ur in re nstom co eupclaesdesm , oidmep ls roav re in cgapoanb le thoefo of frtehaelsie st iwcillalnrde -q suuirrfeacse ig coupling may be ess eenatd ia dli . tiA on ll tshue cc ecsusrroefnetmgpein ri ecraalt / isotn at i o st ficcaolumpe le th dom ds o . dFeo ls rirnesptlain ca ctee , a model parameterisatio nificant improvements in the SST anomaly patterns in the equatorial Pacific that th ry elraeyqeu rs ir , ecd lo m ud osd , erlad im inasp ti oonf , saun rf dacceonpv ro ecce ti sosn es, bound­ have many characteristics in common with observed to a quick solution, but, ro g v iv eemnetnhtesiam re p o li rktealny . N to onye ie o ld flEeN ss SsO uc cceosm sf puolsiin te tsh . eCm ur orreentdim ffi ocduelltspa ro re blceomnso id ferreapblliy ­ imp Iatcsthoofud ld ronuogthbte , they are worth pursuing. ce of the p ca hteirnigcc th ir ecuslpae ti c o if n ic peav tt oelruntsioinnoafgtihve en SESNTSaOndepaitsm od oes . ­ tehxe prospects for im forgotten, however, that not all of However, it is precisely this problem that must be no ctlufsuilv ly eluynodnersse ta a n so pnraolvteidmde ro sc uag le hst . p A re l dictions reside solved. Just as the ‘average’ daily weather is rarely of climate variabilit d y , th th eem re u l is ti aanmnpulaelteo th doeucgahdawles ca dloeo ce bpsteuravleda , idthteo ‘ ucnadneornsitcaanl’ diEnNgS th Oan id aeauissefm ul orceonastcroun ct ­ e2x .1 is c t ) e nc aend -e th .g e . , sien the time series o vidence for its for prediction. To reach their full potential, coupled distributions of rai cnuflaalrl ( cFhiagnugrees2i . n2ftrhae in f p al rlob (F ab ig il uir ty eim nd oidveildsun al eepdas to t E be N S ab O le etpoisroedpe li scaa te ndt he th eeivroleuv ti ooln vi nogfnoefw co duep velopments in data an ). Very recently, extratropical atmospheric and ocean interactions. There is lesdommeoedveildsehnacveeosftd ar etaeld ys t is oaonpdeinn the accuracy The most optimistic expectation is that once that may have a somewhat c ad d a if lfv er aern ia t t io unpstihnisEN fie S ld O . cEoNuSpO le , d th m ey odw el i s ll bheavaeb le cotnoqhueelrped id etnhtei fy chaanld le npg re edio ct ftmheeasiun red by the ocean s character, as other modes of climate variability. This may include Zhang te ertananl. ua1l99 ti 7 m , eFoslc la al neusr fa ( cKeleteemmapne ra et tures, from links between ENSO and the climate system not yet are now beginning to fin ddeatanlu . m1b9e9r8 ) o . M al. od1e9 ll 9e6 rs , m dis ocdoevlesremdaiyntahiediimnpienrv fe ecsttiogbaste io rv nast io onfaplodsastiab . lIemcplriomvaetdem ab e il cih ty anoin sm th seinde th ca edN al otrothmaun lt d i tropic f potential modes that link ocean basins, such as ENSO-and Barnett 1996). There is adlescoad ev aalltiPm ac eifsiccaf le o r ( vari­ related variations of SST in the tropical North Atlantic, ENSO links to rainfall may come an id dengcoed th ep aetnsLoam ti e f rece In n tl aydddiistc io u n ss etdoboycE ea n n fi -e altdmaonsdphMea re y er c o ( u1p9l9 in 7 g ). , new nointutdheeo se fcE ul N ar S O va riitas bility in the str ding generations of models need to include realistic land-southern Europe (R eolpfe -le wes .g k . i , a in ndneonrg Ha th th lp e e rn an dAfm ri acga/ ­ rae tm ali oss ti pchm er oedeclosuopflitnhge . la Snudch su rifm ac peroavnedmie ts ntvsegientvao ti lovneaThheeadp , r m ed aiyctaalbsio lity of ENS rt 1987). and adequate descriptions based on observed data of in Northern Hevm ar iyspohnerdeecOa sp d , rail on ntgiem ( e to s Ba c a ls a a le fse , w e sp se eacs ia oln ly strheep re isne it nitaal tio ve nge in ta t m io ondesltsa te is . c W ur orrekn tl oynbleainndg -s m ur afiancleym 19 e9a5n ) s . (i I . n e ., additio meda et al. driven by the development of coupled models for over several cdheacnagdenes , sis ) n ec a th u lso e la r ‘ itvnyfpairciaalbio li rty in the climate climate change projection over the next century conditional ENSO probability l u fo ernecceassetsxsi . m pe Fpcolteeds ’ e values (Dickinson et al. 1996). the Gulf Coast of the United States shows reaxaam sonal Significant advances in coupled model-based ENSO signal for both the first and second half s o tro p n le, f th g e." In Droughts, 65. Routledge, 2016. http://dx.doi.org/10.4324/9781315830896-45.

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Conference papers on the topic "Land subdivision (N.S.W.)"

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Sánchez-Murillo, Ricardo. "Tracer hydrology of the data-scarce and heterogeneous Central American Isthmus." In I Congreso Internacional de Ciencias Exactas y Naturales. Universidad Nacional, 2019. http://dx.doi.org/10.15359/cicen.1.36.

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Abstract:
Numerous socio-economic activities depend on the seasonal rainfall and groundwater recharge cycle across the Central American Isthmus. Population growth and unregulated land use changes resulted in extensive surface water pollution and a large dependency on groundwater resources. This chapter uses stable isotope variations in rainfall, surface water, and groundwater of Costa Rica, Nicaragua, El Salvador, and Honduras to develop a regionalized rainfall isoscape, isotopic lapse rates, spatial-temporal isotopic variations, and air mass back trajectories determining potential mean recharge elevations, moisture circulation patterns, and surface water-groundwater interactions. Intra-seasonal rainfall modes resulted in two isotopically depleted incursions (W-shaped isotopic pattern) during the wet season and two enriched pulses during the Mid-Summer Drought and the months of the strongest trade winds. Notable isotopic sub-cloud fractionation and near-surface secondary evaporation were identified as common denominators within the Central American Dry Corridor. Groundwater and surface water isotope ratios depicted the strong orographic separation into the Caribbean and Pacific domains, mainly induced by the governing moisture transport from the Caribbean Sea, complex rainfall producing systems across the N-S mountain range, and the subsequent mixing with local evapotranspiration, and, to a lesser degree, the eastern Pacific Ocean fluxes. Groundwater recharge was characterized by a) depleted recharge in highland areas (72.3%), b) rapid recharge via preferential flow paths (13.1%), and enriched recharge due to near-surface secondary fractionation (14.6%). Median recharge elevation ranged from 1,104 to 1,979 m a.s.l. These results are intended to enhance forest conservation practices, inform water protection regulations, and facilitate water security and sustainability planning in the Central American Isthmus.
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