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Статті в журналах з теми "LC (Land cover)"
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Rahaman, S. A., S. Aruchamy, K. Balasubramani, and R. Jegankumar. "LAND USE/LAND COVER CHANGES IN SEMI-ARID MOUNTAIN LANDSCAPE IN SOUTHERN INDIA: A GEOINFORMATICS BASED MARKOV CHAIN APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 231–37. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-231-2017.
Повний текст джерелаАнотація:
Nowadays land use/ land cover in mountain landscape is in critical condition; it leads to high risky and uncertain environments. These areas are facing multiple stresses including degradation of land resources; vagaries of climate and depletion of water resources continuously affect land use practices and livelihoods. To understand the Land use/Land cover (Lu/Lc) changes in a semi-arid mountain landscape, Kallar watershed of Bhavani basin, in southern India has been chosen. Most of the hilly part in the study area covers with forest, plantation, orchards and vegetables and which are highly affected by severe soil erosion, landslide, frequent rainfall failures and associated drought. The foothill regions are mainly utilized for agriculture practices; due to water scarcity and meagre income, the productive agriculture lands are converted into settlement plots and wasteland. Hence, land use/land cover change deduction; a stochastic processed based method is indispensable for future prediction. For identification of land use/land cover, and vegetation changes, Landsat TM, ETM (1995, 2005) and IRS P6- LISS IV (2015) images were used. Through CAMarkov chain analysis, Lu/Lc changes in past three decades (1995, 2005, and 2015) were identified and projected for (2020 and 2025); Normalized Difference Vegetation Index (NDVI) were used to find the vegetation changes. The result shows that, maximum changes occur in the plantation and slight changes found in forest cover in the hilly terrain. In foothill areas, agriculture lands were decreased while wastelands and settlement plots were increased. The outcome of the results helps to farmer and policy makers to draw optimal lands use planning and better management strategies for sustainable development of natural resources.
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López, N., A. Márquez Romance, and E. Guevara Pérez. "Change dynamics of land-use and land-cover for tropical wetland management." Water Practice and Technology 15, no. 3 (June 12, 2020): 632–44. http://dx.doi.org/10.2166/wpt.2020.049.
Повний текст джерелаАнотація:
Abstract In hydrographic basins with wetlands, changes in land use (LU) and land cover (LC) impact the conservation of natural resources, leading to dynamics analysis for integral management. A method is proposed offering greater accuracy in determining the LU and LC bi-temporal and spatial change dynamics in tropical wetlands. LU and LC monitoring is based on Landsat images from 1986 to 2017. ‘Pre-classification’ and ‘post-classification’ methods are applied. In the former, reflectance image differencing and principal component N° 1 image differencing are analyzed to estimate the rate of change/no change area. In the latter, supervised classification is carried out of image pairs from different dates. The principal components method shows that principal component N° 1 collects between 88 and 93% of the reflectance variance in n spectral bands of each satellite image, which improves accuracy in determining LU and LC change dynamics.
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Sertel, Elif, Raziye Topaloğlu, Betül Şallı, Irmak Yay Algan, and Gül Aksu. "Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps." ISPRS International Journal of Geo-Information 7, no. 10 (October 15, 2018): 408. http://dx.doi.org/10.3390/ijgi7100408.
Повний текст джерелаАнотація:
This research aims to investigate how different landscape metrics are affected by the enhancement of the thematic classes in land cover/land use (LC/LU) maps. For this aim, three different LC/LU maps based on three different levels of CORINE (Coordination of Information on The Environment) nomenclature were created for the selected study area using GEOBIA (Geographic Object Based Image Analysis) techniques. First, second and third level LC/LU maps of the study area have five, thirteen and twenty-seven hierarchical thematic classes, respectively. High-resolution Spot 7 images with 1.5 m spatial resolution were used as the main Earth Observation data to create LC/LU maps. Additional geospatial data from open sources (OpenStreetMap and Wikimapia) were also integrated to the classification in order to identify some of the 2nd and 3rd level LC/LU classes. Classification procedure was initially conducted for Level 3 classes in which we developed decision trees to be used in object-based classification. Afterwards, Level 3 classes were merged to create Level 2 LC/LU map and then Level 2 classes were merged to create the Level 1 LC/LU map according to CORINE nomenclature. The accuracy of Level 1, Level 2, Level 3 maps are calculated as; 93.50%, 89.00%, 85.50% respectively. At the last stage, several landscape metrics such as Number of Patch (NP), Edge Density (ED), Largest Patch Index (LPI), Euclidean Nearest Neighbor Distance (ENN), Splitting Index (SPLIT) and Aggregation Index (AI) metrics and others were calculated for different level LC/LU maps and landscape metrics values were compared to analyze the impact of changing thematic details on landscape metrics. Our results show that, increasing the thematic detail allows landscape characteristics to be defined more precisely and ensure comprehensive assessment of cause and effect relationships between classes.
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Bratic, G., M. E. Molinari, and M. A. Brovelli. "VALIDATION OF THE GLOBAL HIGH-RESOLUTION GLOBELAND30 LAND COVER MAP IN EUROPE USING LAND COVER FIELD SURVEY DATABASE - LUCAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 51–59. http://dx.doi.org/10.5194/isprs-archives-xlii-4-51-2018.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> High-resolution land cover maps are one of the technological innovations driving improvements in many fields influenced by Geographic Information Systems (GIS) and Remote Sensing. In particular, the GlobeLand30 (GL30), global LC map with spatial resolution of 30<span class="thinspace"></span>m, is thought to be one of the highest quality high-resolution products. However, these LC maps require validation to determine their suitability for a particular purpose. One of the best ways to provide useful validation reference data is to do a high-level accuracy field survey, but this is time consuming and expensive. Another option is to exploit already available datasets. This study assesses thematic accuracy of GL30 in Europe using LUCAS as a validation reference, because it is a free and open field survey database. The results were generally not good, and very bad for some classes. Analysis was then restricted to a small region (Lombardy, Italy) where LC data of higher resolution than those of GL30 were available. LUCAS was also found to be incoherent with this product. Further comparisons of LUCAS with other independent sources confirmed that the LC attributes of LUCAS are inconsistent with expectations. Although these findings may not be generalized to other regions, the results warn against the suitability of LUCAS as ground truth for LC validation. The paper discusses the process of thematic accuracy assessment of the GL30 and the applicability of LUCAS for high-resolution global LC validation.</p>
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Khyami, Ali. "Impact of land cover change on land surface temperature over Greater Beirut Area – Lebanon." Journal of Geoinformatics & Environmental Research 2, no. 1 (June 23, 2021): 14–27. http://dx.doi.org/10.38094/jgier2121.
Повний текст джерелаАнотація:
Remote sensing (RS) technology has been used together with geographic information systems (GIS) to determine the LC types, retrieve LST, and analyze their relationships. The term Greater Beirut Area (GBA) is used to refer to the city of Beirut and its suburbs which witnessed rapid urban growth, after the end of the civil war, in the last decade of the twentieth century, due to the increase in the number of its inhabitants, and the prosperity and development of sectors such as; industrial, trade, tourism, and construction. These factors led to a wide change in the land cover (LC) types and increased land surface temperature LST. The results showed an increase in built-up areas by 29.1%, and agricultural lands by 6%, while bare land, forests, and seawater decreased by 28.5%, 4.9%, and 1.9%, respectively. These changes caused large differences in the LST between built-up areas and other LC types. The highest LST recorded was in built-up areas (33.03°C in 1985, and 34.01°C in 2020), followed by bare lands (32.61 °C in 1985 and 33.49°C in 2020), cropland (31.23°C in 1985 and 32.17°C in 2020), forest (30.08°C in 1985 and 30.47°C in 2020), and water (24.97°C in 1985 and 28.15°C in 2020). Consequently, converting different LC types into built-up areas led to increases in LST and changed microclimate.
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Giuliani, Gregory, Denisa Rodila, Nathan Külling, Ramona Maggini, and Anthony Lehmann. "Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System." Land 11, no. 5 (April 21, 2022): 615. http://dx.doi.org/10.3390/land11050615.
Повний текст джерелаАнотація:
High spatial and thematic resolution of Land Use/Cover (LU/LC) maps are central for accurate watershed analyses, improved species, and habitat distribution modeling as well as ecosystem services assessment, robust assessments of LU/LC changes, and calculation of indices. Downscaled LU/LC maps for Switzerland were obtained for three time periods by blending two inputs: the Swiss topographic base map at a 1:25,000 scale and the national LU/LC statistics obtained from aerial photointerpretation on a 100 m regular lattice of points. The spatial resolution of the resulting LU/LC map was improved by a factor of 16 to reach a resolution of 25 m, while the thematic resolution was increased from 29 (in the base map) to 62 land use categories. The method combines a simple inverse distance spatial weighting of 36 nearest neighbors’ information and an expert system of correspondence between input base map categories and possible output LU/LC types. The developed algorithm, written in Python, reads and writes gridded layers of more than 64 million pixels. Given the size of the analyzed area, a High-Performance Computing (HPC) cluster was used to parallelize the data and the analysis and to obtain results more efficiently. The method presented in this study is a generalizable approach that can be used to downscale different types of geographic information.
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Shekar N C, Sanjay, and Hemalatha H N. "Land use and Land Cover Characteristics using Bhuvan and MODIS Satellite Data." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 289–94. http://dx.doi.org/10.35940/ijrte.f5322.019521.
Повний текст джерелаАнотація:
Understanding vegetation characteristics is essential for watershed modeling, like in the prediction of streamflow and evapotranspiration (AET) estimation. So, the present study was taken to analyze the Land use/Land cover characteristics in a Sub-humid tropical river basin which is originating in the forested part of Western Ghats mountain ranges using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Bhuvan satellite data as inputs for the algorithm. All the fourteen LU/LC characteristics present in the Hemavathi basin (5427 km2 ) were analyzed in the basin using satellite data which is located in Karnataka, India. Land Surface Reflectance (LSR) and Land Surface Temperature (LST) were the two data products used as input to map the pixel-wise variations in albedo, the fraction of vegetation (FV) and Land Surface Temperature (LST). It was found from the rainfall data that the year 2019 experienced higher rainfall than the average and 2012 very low rainfall than the normal. Parameters considered in this study Albedo, LST and FV are susceptible to wetness and temperature conditions. Variations in albedo and LST were similar in that both values in the summer of 2019 and 2012 are high than winter due to the high temperature and less wetness from all the LU/LC classes. Similarly, FV variations show opposite trends that values in the summer of 2019 and 2012 are low than in winter, which is due to the high temperature and less wetness. The results and discussions show that significant realistic variations in albedo, LST and FV with respect to all LU/LC classes. All the LU/LC classes characteristics in this study show significant variations with respect to wetness and temperature conditions, so the methodology proposed in this study can be used in regional monitoring of LU/LC classes in a convenient and cost-effective manner.
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Shukla, S., M. V. Khire, and S. S. Gedam. "Monitoring Land Use/Land Cover Changes in a River Basin due to Urbanization using Remote Sensing and GIS Approach." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 949–53. http://dx.doi.org/10.5194/isprsarchives-xl-8-949-2014.
Повний текст джерелаАнотація:
Faster pace of urbanization, industrialization, unplanned infrastructure developments and extensive agriculture result in the rapid changes in the Land Use/Land Cover (LU/LC) of the sub-tropical river basins. Study of LU/LC transformations in a river basin is crucial for vulnerability assessment and proper management of the natural resources of a river basin. Remote sensing technology is very promising in mapping the LU/LC distribution of a large region on different spatio-temporal scales. The present study is intended to understand the LU/LC changes in the Upper Bhima river basin due to urbanization using modern geospatial techniques such as remote sensing and GIS. In this study, the Upper Bhima river basin is divided into three adjacent sub-basins: Mula-Mutha sub-basin (ubanized), Bhima sub-basin (semi-urbanized) and Ghod sub-basin (unurbanized). Time series LU/LC maps were prepared for the study area for a period of 1980, 2002 and 2009 using satellite datasets viz. Landsat MSS (October, 1980), Landsat ETM+ (October, 2002) and IRS LISS III (October 2008 and November 2009). All the satellite images were classified into five LU/LC classes viz. built-up lands, agricultural lands, waterbodies, forests and wastelands using supervised classification approach. Post classification change detection method was used to understand the LU/LC changes in the study area. Results reveal that built up lands, waterbodies and agricultural lands are increasing in all the three sub-basins of the study area at the cost of decreasing forests and wastelands. But the change is more drastic in urbanized Mula-Mutha sub-basin compared to the other two sub-basins.
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et al., Abu Haider. "Evaluation of spatiotemporal dynamics of land cover and land surface temperature using spectral indices and supervised classification: A case study of Jobai Beel Area, Bangladesh." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 12 (December 2021): 63–79. http://dx.doi.org/10.21833/ijaas.2021.12.009.
Повний текст джерелаАнотація:
This study aims to evaluate the spatiotemporal change of land cover (LC) and surface temperature of the Jobai Beel area, an exclusive agriculture zone, situated in the far-flung area of northwest Bangladesh using satellite data. Multi-temporal Landsat series of data from 1989 to 2020 and geospatial techniques have been employed to evaluate the LC change and land surface temperature (LST) variation. Different spectral indices such as NDVI, MNDWI, NDBal have been used to retrieve individual LC. Corresponding LST has also been extracted using the thermal bands. Supervised Classification and the post-classification change detection technique were employed to determine the temporal changes and validate the individual LC. The results were employed to assess the LST variation associated with LC changes. The results reveal that the area had undergone a drastic and inconsistent heterogeneous LC transformation during the study period. Water and vegetation areas have expanded at a rate of 0.24km2/year and 0.45km2/year respectively, while bare lands have shrunk at a rate of 0.70km2/year. In general, Bare land exhibits a significant positive correlation, when Vegetation areas show a significant negative correlation with LST. However, the correlation between water areas and LST was found statistically insignificant. Agriculture in the form of vegetation has been found the most dominating land cover character throughout the study period, which has been regulating the LST variation across the area.
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Getu Engida, Tewodros, Tewodros Assefa Nigussie, Abreham Berta Aneseyee, and John Barnabas. "Land Use/Land Cover Change Impact on Hydrological Process in the Upper Baro Basin, Ethiopia." Applied and Environmental Soil Science 2021 (July 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/6617541.
Повний текст джерелаАнотація:
Understanding the hydrological process associated with Land Use/Land Cover (LU/LC) change is vital for decision-makers in improving human wellbeing. LU/LC change significantly affects the hydrology of the landscape, caused by anthropogenic activities. The scope of this study is to investigate the impact of LU/LC change on the hydrological process of Upper Baro Basin for the years 1987, 2002, and 2017. The Soil Water Assessment Tool (SWAT) model was used for the simulation of the streamflow. The required data for the SWAT model are soils obtained from the Food and Agriculture Organization; Digital Elevation Model (DEM) and LU/LC were obtained from the United States Geological Survey (USGS). The meteorological data such as Rainfall, Temperature, Sunshine, Humidity, and Wind Speeds were obtained from the Ethiopian National Meteorological Agency. Data on discharge were obtained from Ministry of Water, Irrigation and Electricity. Ecosystems are deemed vital. Landsat images were used to classify the LU/LC pattern using ERDAS Imagine 2014 software and the LU/LC were classified using the Maximum Likelihood Algorithm of Supervised Classification. The Sequential Uncertainty Fitting (SUFI-2) global sensitivity method within SWAT Calibration and Uncertainty Procedures (SWAT-CUP) was used to identify the most sensitive streamflow parameters. The calibration was carried out using observed streamflow data from 01 January 1990 to 31 December 2002 and a validation period from 01 January 2003 to 31 December 2009. LU/LC analysis shows that there was a drastic decrease of grassland by 15.64% and shrubland by 9.56% while an increase of agricultural land and settlement by 18.01% and 13.01%, respectively, for 30 years. The evaluation of the SWAT model presented that the annual surface runoff increased by 43.53 mm, groundwater flow declined by 27.58 mm, and lateral flow declined by 5.63 mm. The model results showed that the streamflow characteristics changed due to the LU/LC change during the study periods 1987–2017 such as change of flood frequency, increased peak flows, base flow, soil erosion, and annual mean discharge. Curve number, an available water capacity of the soil layer, and soil evaporation composition factor were the most sensitive parameters identified for the streamflow. Both the calibration and validation results disclosed a good agreement between measured and simulated streamflow. The performance of the model statistical test shows the coefficient of determination (R2) and Nash–Sutcliffe (NS) efficiency values 0.87 and 0.81 for calibration periods of 1990–2002 and 0.84 and 0.76 for the validation period of 2003 to 2009, respectively. Overall, LU/LC significantly affected the hydrological condition of the watershed. Therefore, different conservation strategies to maintain the stability and resilience of the ecosystem are vital.
Частини книг з теми "LC (Land cover)"
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García-Álvarez, David, and Javier Lara Hinojosa. "General Land Use Cover Datasets for America and Asia." In Land Use Cover Datasets and Validation Tools, 361–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_18.
Повний текст джерелаАнотація:
AbstractIn this chapter we review some examples of general Land Use Cover (LUC) mapping at a supra-national level in America and Asia. These datasets provide a general overview of the land uses and covers in specific American or Asian regions, without focusing on any particular land use or cover. For Asia, we have only identified one dataset mapping the Himalayan region, whereas for America five different datasets were identified. Only three of these are reviewed here, as the other two (SERENA, South America 30 m) are not available for download. The most ambitious project of all those reviewed is NALCMS, which coordinates the production of a LUC map for the whole of North America (Canada, Mexico, USA) at detailed scales (30–250 m) and using the same classification legend. It is the only dataset of all those reviewed that provides a time series of LUC maps (2005, 2010 and 2015). The Himalaya Regional Land Cover database is a vector-based map that provides information on LUC changes over the period 1970/80–2007 at a scale of 1:350,000. The other two American datasets—LBA-ECO LC-08 (1 km, 1987/91) and MERISAM2009 (300 m, 2008/10)—are raster-based and only available for one date, therefore making change detection impossible.
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García-Álvarez, David, Javier Lara Hinojosa, Francisco José Jurado Pérez, and Jaime Quintero Villaraso. "Global General Land Use Cover Datasets with a Time Series of Maps." In Land Use Cover Datasets and Validation Tools, 287–311. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_15.
Повний текст джерелаАнотація:
AbstractGeneral Land Use Cover (LUC) datasets provide a holistic picture of all the land uses and covers on Earth, without focusing specifically on any individual land use category. As opposed to the LUC maps which are only available for one date or year, reviewed in Chap. “Global General Land Use Cover Datasets with a Single Date”, the maps with time series allow users to study LUC change over time. Time series of general LUC datasets at a global scale is useful for understanding global patterns of LUC change and their relation with global processes such as climate change or the loss of biodiversity. MCD12Q1, also known as MODIS Land Cover, was the first time series of LUC maps to be produced on a global scale. When it was first launched in 2002, there were already many organizations and researchers working on accurate, detailed global LUC maps, although these were all one-off editions for single years. The MCD12Q1 dataset continues to be updated today, providing a series of maps for the period 2001–2018. Since the launch of MCD12Q1, many other historical series of LUC maps have been produced, especially in the last decade. This has resulted in the LUC map series covering a longer time period at higher spatial resolution. Recent efforts have focused on producing consistent time series of maps that can track LUC changes over time with low levels of uncertainty. GLCNMO (500 m), GlobCover (300 m) and GLC250 (250 m) provide time series of LUC maps at similar spatial resolutions to MCD12Q1 (500 m), although for fewer reference years. GLCNMO provides information for the years 2003, 2008 and 2013, GlobCover for 2005 and 2009 and GLC250 for 2001 and 2010. GLASS-GLC is the dataset with the coarsest spatial resolution of all those reviewed in this chapter (5 km), even though it was released very recently, in 2020. Map producers have focused on this dataset’s long timespan (1982–2015) rather than on its spatial detail. LC-CCI and CGLS-LC100 are the recently launched datasets providing a consistent series of LUC maps, which show LUC changes over time with lower levels of uncertainty. LC-CCI provides LUC information for one of the longest timespans reviewed here (1992–2018) at a spatial resolution of 300 m. CGLS-LC100 provides LUC information for a shorter period (2015–2019) but at a higher spatial resolution (100 m). In both cases, updates are scheduled. The datasets with the highest levels of spatial detail are FROM-GLC and GLC30. These were produced using highly detailed Landsat imagery, delivering time series of maps at 30 m. The FROM-GLC project even has a test LUC map at a spatial resolution of 10 m from Sentinel-2 imagery for the year 2017, making it the global dataset with the greatest spatial detail of all those reviewed in this book. Both FROM-GLC and GLC30 provide data for three different dates: the former for 2010, 2015 and 2017 and the latter for 2000, 2010 and 2020.
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García-Álvarez, David, Javier Lara Hinojosa, and Francisco José Jurado Pérez. "Global Thematic Land Use Cover Datasets Characterizing Artificial Covers." In Land Use Cover Datasets and Validation Tools, 419–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_21.
Повний текст джерелаАнотація:
AbstractThe mapping of artificial covers at a global scale has received increasing attention in recent years. Numerous thematic global Land Use Cover (LUC) datasets focusing on artificial surfaces have been produced at increasingly high spatial resolutions and using methods that ensure improved levels of accuracy. In fact, there are several long time series of maps showing the evolution of artificial surfaces from the 1980s to the present. Most of them allow for change detection over time, which is possible, thanks to the high level of accuracy at which artificial surfaces can be mapped and because transitions from artificial to non-artificial covers are very rare. Global thematic LUC datasets characterizing artificial covers usually map the extent or percentage of artificial or urban areas across the world. They do not provide thematic detail on the different uses or covers that make up artificial or urban surfaces. Unlike other general or thematic LUC datasets, those focusing on artificial covers make extensive use of radar data. In several cases, optical and radar imagery have been used together, as each source provides complementary information. Global Urban Expansion 1992–2016 and ISA, which were produced at a spatial resolution of 1 km, are the coarsest of the nine datasets reviewed in this chapter. ISA provides information on the percentage of impervious surface area per pixel. The GHSL edition of 2014 and the GMIS at 30 m also provide sub-pixel information, whereas all the other datasets reviewed here only map the extent of artificial/impervious/urban areas. Most of the datasets reviewed in this chapter were produced at a spatial resolution of 30 m. This is due to the extensive use of Landsat imagery in the production of these datasets. Landsat provides a long, high-resolution series of satellite imagery that enables effective mapping of the evolution of impervious surfaces at detailed scales. Of the datasets produced at 30 m, Global Urban Land maps artificial covers for seven different dates between 1980 and 2015, while GHSL does the same for five different dates between 1987 and 2016, although the map for the last date was produced at 20 m. GUB maps the extent of urban land for seven dates between 1990 and 2018 and was produced together with GAIA, which provides an annual series of maps for the period 1985–2018. HBASE, GMIS and GISM, also at 30 m, are only available for one reference year. The same is true of GUF and WSF, which were produced as part of the same effort to map global artificial surfaces as accurately as possible. They provide the most detailed datasets up to date, with spatial resolutions of 12 m (GUF) and 10 m (WSF). Future updates of WSF will produce a consistent time series of global LC maps of artificial areas from the 1980s to the present. It aims to be the longest, most detailed, most accurate dataset ever produced on this subject.
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García-Álvarez, David, Javier Lara Hinojosa, and Jaime Quintero Villaraso. "Global General Land Use Cover Datasets with a Single Date." In Land Use Cover Datasets and Validation Tools, 269–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_14.
Повний текст джерелаАнотація:
AbstractGlobal general Land Use and Land Cover (LUC) datasets map all land uses and covers across the globe, without focusing on any specific use or cover. This chapter only reviews those datasets available for one single date, which have not been updated over time. Seven different datasets are described in detail. Two other ones were identified, but are not included in this review, because of its coarsens, which limits their utility: Mathews Global Vegetation/Land Use and GMRCA LULC. The first experiences in global LUC mapping date back to the 1990s, when leading research groups in the field produced the first global LUC maps at fine scales of 1 km spatial resolution: the UMD LC Classification and the Global Land Cover Characterization. Not long afterwards, in an attempt to build on these experiences and take them a stage further, an international partnership produced GLC2000 for the reference year 2000. These initial LUC mapping projects produced maps for just one reference year and were not continued or updated over time. Subsequent projects have mostly focused on the production of timeseries of global LUC maps, which allow us to study LUC change over time (see Chapter “Global General Land Use Cover Datasets with a Time Series of Maps”). As a result, there are relatively few single-date global LUC maps for recent years of reference. The latest projects and initiatives producing global LUC maps for single dates have focused on improving the accuracy of global LUC mapping and the use of crowdsourcing production strategies. The Geo-Wiki Hybrid and GLC-SHARE datasets built on the previous research in a bid to obtain more accurate global LUC maps by merging the data from existing datasets. OSM LULC is an ongoing test project that is trying to produce a global LUC map cheaply, using crowdsourced information provided by the Open Street Maps community. The other dataset reviewed here is the LADA LUC Map, which was developed for a specific thematic project (Land Degradation Assessment in Dryland). This dataset is not comparable to the others reviewed in this chapter in terms of its purpose and nature, as is clear from its coarse spatial resolution (5 arc minutes). We therefore believe that this dataset should not be considered part of initiatives to produce more accurate, more detailed land use maps at a global level.
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Sertel, Elif, Raziye Hale Topaloğlu, Kübra Bahşi, Beril Varol, and Nebiye Musaoğlu. "Production of a Land Cover/Land Use (LC/LU) Map of Izmir Metropolitan City by Using High-Resolution Images." In Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions (2nd Edition), 1837–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51210-1_290.
Повний текст джерела
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Joshi, P. K., and Neena Priyanka. "Geo-Informatics for Land Use and Biodiversity Studies." In Geographic Information Systems, 1913–39. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch114.
Повний текст джерелаАнотація:
The dynamics of land use/land cover (LU/LC) is a manifestation of the cyclic correlation among the kind and magnitude of causes, impacts, responses and resulting ecological processes of the ecosystem. Thus, the holistic understanding of the complex mechanisms that control LU/LC requires synergetic adoption of measurement approaches, addressing issues, and identifying drivers of change and state of art technologies for mitigation measures. As the spatio-temporal heterogeneity of the LU/LC increases, its impact on biodiversity becomes even more difficult to anticipate. Thus, in order to understand the spatio-temporal dynamics of change in landscape and its relationship to biodiversity, it is necessary to reliably identify and quantify the indicators of change. In addition, it is also important to have better understanding of the technologies and techniques that serve as complimentary tool for land mitigation and conservation planning. Against this background, the chapter aims to synthesize LU/LC studies worldwide and their impacts on biodiversity. This chapter explores identification and analysis of key natural, socio-economic and regulatory drivers for LU/LC. Finally, it attempts to collate some LU/LC studies involving usage of geospatial tools, such as satellite remote sensing, Geographic Information System (GIS), Global Positioning System (GPS), and integrative tools, besides conventional approaches that could assist decision makers, land managers, stakeholders and researchers in better management and formulation of conservation strategies based on scientific grounds.
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Rahman, Munshi K., Thomas W. Schmidlin, Mandy J. Munro-Stasiuk, and Andrew Curtis. "Geospatial Analysis of Land Loss, Land Cover Change, and Landuse Patterns of Kutubdia Island, Bangladesh." In Environmental Information Systems, 1080–97. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7033-2.ch048.
Повний текст джерелаАнотація:
This study utilizes geospatial tools of remote sensing, geographical information systems (GIS), and global positioning system (GPS) to examine the land loss, land cover (LC) change, landuse of Kutubdia Island, Bangladesh. Multi-spectral Scanner (MSS), Thematic Mapper (TM), and Landsat8 OLI imageries were used for land cover change. For assessing the landuse patterns of 2012, spatial video data were collected by using contour GPS camera. Using remote sensing analysis three different land cover classes (water, trees and forest, and agriculture) were identified and land cover changes were detected from 1972 to 2013. The results show from 1972 to 2013, an estimated 9 km2 of land has been lost and significant changes have taken place from 1972 to 2013. Only an estimated .35 km2 area of accretion has taken place during the study period. Using GIS eight different landuse patterns were identified based on spatial video data.
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Rahman, Munshi K., Thomas W. Schmidlin, Mandy J. Munro-Stasiuk, and Andrew Curtis. "Geospatial Analysis of Land Loss, Land Cover Change, and Landuse Patterns of Kutubdia Island, Bangladesh." In Oceanography and Coastal Informatics, 448–65. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7308-1.ch021.
Повний текст джерелаАнотація:
This study utilizes geospatial tools of remote sensing, geographical information systems (GIS), and global positioning system (GPS) to examine the land loss, land cover (LC) change, landuse of Kutubdia Island, Bangladesh. Multi-spectral Scanner (MSS), Thematic Mapper (TM), and Landsat8 OLI imageries were used for land cover change. For assessing the landuse patterns of 2012, spatial video data were collected by using contour GPS camera. Using remote sensing analysis three different land cover classes (water, trees and forest, and agriculture) were identified and land cover changes were detected from 1972 to 2013. The results show from 1972 to 2013, an estimated 9 km2 of land has been lost and significant changes have taken place from 1972 to 2013. Only an estimated .35 km2 area of accretion has taken place during the study period. Using GIS eight different landuse patterns were identified based on spatial video data.
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Fichera, Carmelo Riccardo, Giuseppe Modica, and Maurizio Pollino. "Integration of Satellite Remote Sensing Techniques and Landscape Metrics to Characterize Land Cover Change and Dynamics." In Geographic Information Analysis for Sustainable Development and Economic Planning, 228–44. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1924-1.ch016.
Повний текст джерелаАнотація:
One of the most relevant applications of Remote Sensing (RS) techniques is related to the analysis and the characterization of Land Cover (LC) and its change, very useful to efficiently undertake land planning and management policies. Here, a case study is described, conducted in the area of Avellino (Southern Italy) by means of RS in combination with GIS and landscape metrics. A multi-temporal dataset of RS imagery has been used: aerial photos (1954, 1974, 1990), Landsat images (MSS 1975, TM 1985 and 1993, ETM+ 2004), and digital orthophotos (1994 and 2006). To characterize the dynamics of changes during a fifty year period (1954-2004), the approach has integrated temporal trend analysis and landscape metrics, focusing on the urban-rural gradient. Aerial photos and satellite images have been classified to obtain maps of LC changes, for fixed intervals: 1954-1985 and 1985-2004. LC pattern and its change are linked to both natural and social processes, whose driving role has been clearly demonstrated in the case analysed. In fact, after the disastrous Irpinia earthquake (1980), the local specific zoning laws and urban plans have significantly addressed landscape changes.
Тези доповідей конференцій з теми "LC (Land cover)"
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Sahu, Smruti Ranjan, Kishan Singh Rawat, Sudhir Kumar Singh, and Anoop Bahuguna. "Land use land cover (LU/LC) change analysis using earth observation data sets over Jharsuguda districts of Odisha." In INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “TECHNOLOGY IN AGRICULTURE, ENERGY AND ECOLOGY” (TAEE2022). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0117977.
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Ahmed, Usman Iqbal, Arturo Velasco, and Bernhard Rabus. "Semantic Segmentation of Land Use / Land Cover (LU/LC) Types Using F-CNNS on Multi-Sensor (Radar-Ir-Optical) Image Data." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554051.
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