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Journal articles on the topic 'Land cover'
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Azevedo, Thiago, and Lindon Fonseca Matias. "Urban Land Use and Land Cover Mapping." Agua y Territorio / Water and Landscape, no. 23 (September 7, 2023): e7251. http://dx.doi.org/10.17561/at.23.7251.
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The Brazilian urbanization process produced a complex urban space, with a variety of urban land use and cover as a result. The study of these forms through a classification system is essential, but most current systems don’t capture this complexity, condensing it. Urban forms are difficult to distinguish and classify, resulting in the need for a system with a high degree of detail, for a more accurate urban planning. The objective of this work is to propose a classification system for urban land use and cover, which can demonstrate the multiplicities through remote sensing, using data from CBERS 04A satellite. The methodology surveys the visual elements of remote sensing images, through visual interpretation, relating them to each proposed use and cover class. With this, a classification system was developed that covers the urban space in 17 classes, being an effective way to raise information about the different urban forms.
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TOWNSHEND, J. R. G. "Land cover." International Journal of Remote Sensing 13, no. 6-7 (April 1992): 1319–28. http://dx.doi.org/10.1080/01431169208904193.
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Strode, Georgianna, Victor Mesev, Benjamin Thornton, Marjorie Jerez, Thomas Tricarico, and Tyler McAlear. "Geovisualization of land use and land cover using bivariate maps and Sankey flow diagrams." Proceedings of the ICA 1 (May 16, 2018): 1–5. http://dx.doi.org/10.5194/ica-proc-1-106-2018.
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The terms ‘land use’ and ‘land cover’ typically describe categories that convey information about the landscape. Despite the major difference of land use implying some degree of anthropogenic disturbance, the two terms are commonly used interchangeably, especially when anthropogenic disturbance is ambiguous, say managed forestland or abandoned agricultural fields. Cartographically, land use and land cover are also sometimes represented interchangeably within common legends, giving with the impression that the landscape is a seamless continuum of land use parcels spatially adjacent to land cover tracts. We believe this is misleading, and feel we need to reiterate the well-established symbiosis of land uses as amalgams of land covers; in other words land covers are subsets of land use. Our paper addresses this spatially complex, and frequently ambiguous relationship, and posits that bivariate cartographic techniques are an ideal vehicle for representing both land use and land cover simultaneously. In more specific terms, we explore the use of nested symbology as ways to represent graphically land use and land cover, where land cover are circles nested with land use squares. We also investigate bivariate legends for representing statistical covariance as a means for visualizing the combinations of land use and cover. Lastly, we apply Sankey flow diagrams to further illustrate the complex, multifaceted relationships between land use and land cover. Our work is demonstrated on data representing land use and cover data for the US state of Florida.
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Morales, Narkis S., and Ignacio C. Fernández. "Land-Cover Classification Using MaxEnt: Can We Trust in Model Quality Metrics for Estimating Classification Accuracy?" Entropy 22, no. 3 (March 17, 2020): 342. http://dx.doi.org/10.3390/e22030342.
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MaxEnt is a popular maximum entropy-based algorithm originally developed for modelling species distribution, but increasingly used for land-cover classification. In this article, we used MaxEnt as a single-class land-cover classification and explored if recommended procedures for generating high-quality species distribution models also apply for generating high-accuracy land-cover classification. We used remote sensing imagery and randomly selected ground-true points for four types of land covers (built, grass, deciduous, evergreen) to generate 1980 classification maps using MaxEnt. We calculated different accuracy discrimination and quality model metrics to determine if these metrics were suitable proxies for estimating the accuracy of land-cover classification outcomes. Correlation analysis between model quality metrics showed consistent patterns for the relationships between metrics, but not for all land-covers. Relationship between model quality metrics and land-cover classification accuracy were land-cover-dependent. While for built cover there was no consistent patterns of correlations for any quality metrics; for grass, evergreen and deciduous, there was a consistent association between quality metrics and classification accuracy. We recommend evaluating the accuracy of land-cover classification results by using proper discrimination accuracy coefficients (e.g., Kappa, Overall Accuracy), and not placing all the confidence in model’s quality metrics as a reliable indicator of land-cover classification results.
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Oliveira, J. C., R. A. C. Lamparelli, G. K. D. A. Figueiredo, E. E. Campbell, J. R. Soares, L. A. Monteiro, M. Vianna, et al. "PASTURE LAND COVER CHANGE IN SÃO PAULO STATE, BRAZIL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 321–26. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-321-2020.
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Abstract. Pastures are complex land covers with a variety of land use systems. This land cover occupies large areas in the globe and is mainly used for livestock production. Brazil is one of the largest livestock producers and has extensive pasture areas. We analyzed the pasture land cover change of the São Paulo State between the years 2000 to 2015. São Paulo was chosen as study case due to its large industrial and agricultural importance and its expressive land cover changes over past decades. It was analyzed land covers databases generated by the Brazilian Annual Land Use and Land Cover Mapping Project (MapBiomas Project) – Collection 4. Transition matrix was generated to analyze the land cover change during the period. Gain, loss, total change, net change and swap were calculated in terms of area. Total pasture area decreased but continues the largest land cover of the São Paulo State; with 79.5% of persistence in the area. Main changes were from losses of pastures and gains in agriculture. Most of the changes to pasture came from other non vegetated areas and grassland categories. These results demonstrated the relevance of pastures areas in land cover change dynamics to address land use policy and plan future land use scenarios.
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Comber, A. J. "Land use or land cover?" Journal of Land Use Science 3, no. 4 (November 22, 2008): 199–201. http://dx.doi.org/10.1080/17474230802465140.
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Nganro, Sudirman, Slamet Trisutomo, Roland Barkey, Mukti Ali, Hidefumi Imura, Akio Onishi, Pei-I. Tsai, and Mohd Amirul Mahamud. "Prediction of Future Land Use and Land Cover (LULC) in Makassar City." TATALOKA 23, no. 2 (May 31, 2021): 183–89. http://dx.doi.org/10.14710/tataloka.23.2.183-189.
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Migration from rural area to urban area increases urban population. It increases and needs for settlements, leading to conversion of agricultural lands into settlement areas. Inconsistent land use compared with spatial planning causes change in land use. Spatial land use expansion can be monitored and predicted by modeling. NetLogo application is a software integrated with Agent-Based Modeling (ABM), which can be used to predict change of land use with various complex parameters. The present study used population growth as a parameter to predict change of land use of Makassar in 2050 based on 2017 land use classification map as the start of the prediction. The analysis result showed that the biggest change of land use happens to Settlement class which is 594.74 hectares and the smallest is Water Body class which is 8.76 hectares.
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Šetka, Josip, Petra Radeljak Kaufmann, and Luka Valožić. "Modelling land use and land cover changes in the Lower Neretva Region." Hrvatski geografski glasnik/Croatian Geographical Bulletin 85, no. 1 (2023): 41–63. http://dx.doi.org/10.21861/hgg.2023.85.01.02.
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It has been shown that simulation models are reliable tools for predicting land changes, which contributes to better understanding and management of human impact on the environment. Land use and land cover changes in the Lower Neretva Region between 1990 and 2035 have been analysed and modelled in this study. The final simulation model of future changes was created based on cellular automata and artificial neural networks, implemented in the MOLUSCE plugin for QGIS. In addition, a test simulation model for 2020 was created, which showed high accuracy. Input variables for the final simulation model included a digital elevation model (DEM), slope, distance from water bodies, distance from built-up areas, and population density by settlement in 2011 and 2021. According to the results, forests and grasslands will expand and occupy almost 45% of the area. A slight increase in built-up and agricultural areas is expected, while swamps, water bodies, and sparse vegetation areas will decrease.
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Deshpande, Srinivas, and Rohan S. Gurav. "Change Detection of Land Use and Land Cover Over Ghataprabha River Basin." International Journal of Science and Research (IJSR) 12, no. 10 (October 5, 2023): 71–78. http://dx.doi.org/10.21275/sr23930164256.
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Anua, S. N., and W. V. C. Wong. "Utilizing Landsat 8 OLI for land cover classification in plantations area." IOP Conference Series: Earth and Environmental Science 1053, no. 1 (June 1, 2022): 012027. http://dx.doi.org/10.1088/1755-1315/1053/1/012027.
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Abstract Identifying the land cover in plantations is crucial to assist the management of an area. Today, land cover classification can be achieved using free satellite data. The objective of this study is to perform a supervised classification using LANDSAT 8 OLI to differentiate the land cover in Brumas Camp which consists of non-vegetation, oil palm, forest, and forest plantations. The overall accuracy and Kappa’s coefficients were 71.64% and 0.62, respectively. We found out that the accuracy of classification for non-vegetation is relatively higher compared to vegetation land cover types. The non-vegetation land cover has distinct spectral reflectance which is useful to differentiate between non-vegetation and vegetation land covers.
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Guliyeva, S. H. "LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 61–65. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-61-2020.
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Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.
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Isola, Federica, Sabrina Lai, Federica Leone, and Corrado Zoppi. "Land Take and Landslide Hazard: Spatial Assessment and Policy Implications from a Study Concerning Sardinia." Land 12, no. 2 (January 28, 2023): 359. http://dx.doi.org/10.3390/land12020359.
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Land take and soil sealing imply land cover transitions that may possibly result in decreased capacity to resist landslides; hence, this study focuses on the relations between land-taking processes and landslide hazard by addressing the following research question: “To what extent do land-taking processes increase landslide hazard?” The impact of land take is assessed through a regression model which relates the level of landslide hazard to a set of land cover variables which include artificialized land; that is, land taken up through urbanization processes, and a set of covariates that represent land cover types grouped in accordance with the LEAC (land and ecosystem accounting) classification. This methodological approach is implemented into the spatial context of Sardinia, an insular Italian region, and shows that not only the amount of taken up artificialized land, but also other types of land covers, are likely to increase the magnitude of landslide hazard. A set of implications concerning planning policies related to land cover and land cover transitions are discussed in the concluding section, where policy recommendations are identified in order to mitigate the impacts of land cover transitions on landslide hazards.
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Kolar, Jan. "Land cover accounting." International Journal of Environment and Pollution 15, no. 6 (2001): 695. http://dx.doi.org/10.1504/ijep.2001.004988.
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Wulder, Michael A., Nicholas C. Coops, David P. Roy, Joanne C. White, and Txomin Hermosilla. "Land cover 2.0." International Journal of Remote Sensing 39, no. 12 (March 27, 2018): 4254–84. http://dx.doi.org/10.1080/01431161.2018.1452075.
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Mahesh, R., R. Neelakantan, R. Anbalagan, P. Parthiban, and A. Das. "Spatio-Temporal Assessment of Land use / Land cover using Normalized Difference Vegetation Index (NDVI) of Satellite Imageries and its relationship with Landslide-prone Zones in Kodaikanal Hill Station of Tamil Nadu, India." Disaster Advances 15, no. 10 (September 25, 2022): 35–41. http://dx.doi.org/10.25303/1510da035041.
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The land use exercises at last influence the land cover briefly as well as spatially. The primary consideration liable for the adjustment in land cover is to satisfy the developing requests of expanding populace through horticultural increase for nourishment and clearing of typical land covers like the forests and sparsely inhabited regions for settlement and business activities. The adjustment in land cover additionally upsets the other characteristic parts like soil ripeness, soil disintegration, environment, biodiversity, air quality and water system of the upset area. Remote sensing and GIS have been deployed to examine the changes in land use and land cover of the zone at spatial and transient scales. In the present study, the NDVI-based grouping explored a significant change in land use- land cover between 2009 and 2016. A significant change has been found in the forest cover area where around (3.34%) of the forest was occupied between 2009 and 2016. A comparison between the land use/land cover and landslide hazard zones was carried out which indicated the excessive anthropogenic activities (of construction and mechanized agriculture) as identified by built-up land and crop-land by NDVI studies need to be monitored and planned to maintain the safety, stability and popularity of this precious and geo-climatically strategic region.
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Wahyuni, Nurlita, Abdul Hasyim, and Soemarno Soemarno. "Dynamic of the Land Use and Land Cover Change in Banyuwangi Regency From 1995-2019." Jurnal Wasian 8, no. 2 (December 30, 2021): 121–32. http://dx.doi.org/10.20886/jwas.v8i2.6707.
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The land use and land cover change phenomenon has become one concern over many regions worldwide, including Indonesia. Land use and land cover change due to human activities triggered alteration terrestrial ecosystems and its services including climate control functions. The study aimed to analyze land use and land cover change in Banyuwangi regency during 1995 – 2019. Four satellite images from acquisition year 1995, 2000, 2014 and 2019 were used to analyze the spatial and temporal changes along with field observations. The classification processes of land use and land cover included determination of training areas, supervised classification, and accuracy assessment. There are 12 land use and land cover based on supervised classification as follow primary forest, secondary forest, plantation forest, mangrove forest, plantation, settlement, cropland, paddy field, shrubs, water, fishpond and barren land. The result showed during observation period of 1995 until 2019 land use and land cover which tends to decrease are secondary forest, mangrove forest, and rice fields. On the other hand, the area of settlements, shrubs and fishponds were increased significantly.
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Nagarajan, N., and S. Poongothai. "Identification of Land use and Land cover Changes using Remote Sensing and GIS." International Journal of Engineering and Technology 3, no. 5 (2011): 570–76. http://dx.doi.org/10.7763/ijet.2011.v3.287.
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Kobayashi, Toshiyuki, Ryutaro Tateishi, Bayan Alsaaideh, Ram C. Sharma, Takuma Wakaizumi, Daichi Miyamoto, Xiulian Bai, et al. "Production of Global Land Cover Data – GLCNMO2013." Journal of Geography and Geology 9, no. 3 (June 25, 2017): 1. http://dx.doi.org/10.5539/jgg.v9n3p1.
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Global land cover products have been created for global environmental studies by several institutions and organizations. The Global Mapping Project coordinated by the International Steering Committee for Global Mapping (ISCGM) has been periodically producing global land cover datasets asone of the eight basic global datasets. It has produced a new fifteen-second (approximately 500 m resolution at the equator) global land cover dataset – GLCNMO2013 (or GLCNMO version 3). This paper describes the method of producing GLCNMO2013. GLCNMO2013 has 20 land cover classes, and they were mapped by improved methods from GLCNMO version 2. In GLCNMO2013, five classes,which are urban, mangrove, wetland, snow/ice, and waterwere independently classified. The remaining 15 classes were divided into 4 groups and mapped individually by supervised classification. 2006 polygons of training data collected for GLCNMO2008 were used for supervised classification. In addition, about 3000 polygons of new training data were collected globally using Google Earth, MODIS Normalized Difference Vegetation Index (NDVI) seasonal change patterns, existing regional land cover maps, and existing four global land cover products. The primary data of this product were Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013. GLCNMO2013 was validated at 1006 sampled points. The overall accuracy of GLCNMO2013 was 74.8%, and the overall accuracy for eight aggregated classes was 90.2%. The accuracy of the GLCNMO2013 was not improved compared with the GLCNMO2008 at heterogeneous land covers. It is necessary to prepare the training data for mosaic classes and heterogeneous land covers for improving the accuracy.
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Sewnet, Amare. "Land Use/Cover Change at Infraz Watershed, Northwestren Ethiopia." Journal of Landscape Ecology 8, no. 1 (January 1, 2015): 69–83. http://dx.doi.org/10.1515/jlecol-2015-0005.
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Abstract Land cover is the physical and biological cover of the surface whereas land use covers the results of human activities for the exploitation of it. The land cover and landuse change is caused by both, natural and anthropogenic factors. The objective of this study was to detect land cover/use changes in Infraz Watershed. The study has used ArcGIS10 and ERDAS IMAGINE10, landsat images of 1973, 1986, 1995 and 2011 and socio-economic data to analyze land cover and landuse changes of Infraz watershed. The study has found that due to the population increase and improper agricultural activity bush and wetlands have declined where as farm and settlement lands expanded between the study years. About 1044 wetlands and 6338.7 ha of bush lands were lost and converted to cultivated and farm lands, grass lands and forest covers which were increased by 6685.3, 357.7 and 338.3 ha between the study periods respectively. There is an urgent need to limit the population growth rate and implementing land use policy in the Infraz watershed.
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Samsuri, A. Zaitunah, AG Ahmad, and I. Pasaribu. "Lands Cover Dynamics of Siais Lake Catchment Area, Tapanuli Selatan District, Indonesia." IOP Conference Series: Earth and Environmental Science 1116, no. 1 (December 1, 2022): 012084. http://dx.doi.org/10.1088/1755-1315/1116/1/012084.
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Abstract The Siais Lake Catchment Area (SLCA) is a water resource that includes various types of land cover and land use in Rianiate village, Angkola Sangkunur sub-district, and Tapanuli Selatan district. The SLCA lake catchment area has altered the land cover and increased the surface area of Siais Lake. The purpose of this study was to determine the type of land cover that changed in the Siais lake catchment area between 1990 and 2018. The land cover classification was obtained using Landsat 5 and Landsat 8 OLI images. Make use of supervised classification. Forest, plantation, bare land, paddy field, settlement, waterbody, shrub, and swamp were the land covers identified in the Lake Siais catchment area. Plantations had the highest percentage of land cover increase from 1990 to 2018, with an increase of 3,995.87%, while paddy fields had the highest decrease, with a decrease of 93.77%. .
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Svenningsen, Cecilie S., Birte Peters, Diana E. Bowler, Robert R. Dunn, Aletta Bonn, and Anders P. Tøttrup. "Insect biomass shows a stronger decrease than species richness along urban gradients." Insect Conservation and Diversity 17, no. 2 (March 2024): 182–88. http://dx.doi.org/10.1111/icad.12694.
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Abstract Anthropogenic land cover change is a major driver of biodiversity loss, with urbanisation and farmland practices responsible for some of the most drastic modifications of natural habitats. The relative importance of different land covers for shaping insect communities, however, is unclear. This study examines the effect of urban and farmland covers, along with land cover heterogeneity, at a landscape scale on species richness, evenness and biomass of flying insects using citizen science carnet sampling across Denmark. Increasing urban cover had a negative effect on insect richness but an even stronger negative effect on biomass. Increased land cover heterogeneity did not mitigate the negative effect of urban cover. Insect assemblages also became more even with increased urban cover. Farmland cover had no significant effect on insect richness, evenness or biomass. Based on our findings, the urban cover has a strong negative impact on insect communities, indicating that urbanisation could contribute to insect declines. Moreover, our findings indicate that insect loss occurs more through loss of biomass than loss of species, which may affect the ecosystem‐level consequences of urbanisation.
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Zhu, Linye, Mingming Shi, Deqin Fan, Kun Tu, and Wenbin Sun. "Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China." Sustainability 15, no. 6 (March 7, 2023): 4735. http://dx.doi.org/10.3390/su15064735.
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Exploring the influence of vegetation carbon stocks over land-cover change and the relationship between land-cover change and net primary production (NPP) is of great practical importance for achieving China’s carbon neutrality target. Therefore, this paper analyses the spatio-temporal distribution of land-cover change and NPP change in Inner Mongolia from 2000 to 2020 and explores the vegetation carbon stock change caused by land-cover change, the relationship between land cover and NPP, and the relationship between land-cover change and NPP change. The results show that the main land-cover change in Inner Mongolia during 2000–2020 was the conversion from other land-cover types to grassland, with the conversion of bare land to grassland from 2000 to 2010 covering an area of 20,634.87 km2. During the period of 2000–2020, the high values of NPP were mainly located in northeastern Inner Mongolia, and the low values were mainly distributed in southwestern Inner Mongolia. The total carbon storage changes in vegetation affected by land-cover change during 2000–2010 and 2010–2020 were 10,736,986.11 t and 3,894,272.41 t. The mean values of NPP for different land covers were, in descending order, forest > cultivated land > wetland > grassland > artificial surfaces > shrubland > water bodies > bare land. Between 2000 and 2010, the change in land-cover type to water bodies was the main cause of the decrease in NPP, and the increase in NPP was mainly due to the conversion of other land-cover types to cultivated land, forest, and grassland. The implementation of appropriate conservation and management measures (e.g., the construction of forest and grass ecological protection), planting, and the protection of high-vegetation carbon storage plants and other means can improve the level of vegetation carbon sequestration and protect the ecological environment of Inner Mongolia.
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Gaillard, M. J., S. Sugita, F. Mazier, A. K. Trondman, A. Broström, T. Hickler, J. O. Kaplan, et al. "Holocene land-cover reconstructions for studies on land cover-climate feedbacks." Climate of the Past 6, no. 4 (July 26, 2010): 483–99. http://dx.doi.org/10.5194/cp-6-483-2010.
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Abstract. The major objectives of this paper are: (1) to review the pros and cons of the scenarios of past anthropogenic land cover change (ALCC) developed during the last ten years, (2) to discuss issues related to pollen-based reconstruction of the past land-cover and introduce a new method, REVEALS (Regional Estimates of VEgetation Abundance from Large Sites), to infer long-term records of past land-cover from pollen data, (3) to present a new project (LANDCLIM: LAND cover – CLIMate interactions in NW Europe during the Holocene) currently underway, and show preliminary results of REVEALS reconstructions of the regional land-cover in the Czech Republic for five selected time windows of the Holocene, and (4) to discuss the implications and future directions in climate and vegetation/land-cover modeling, and in the assessment of the effects of human-induced changes in land-cover on the regional climate through altered feedbacks. The existing ALCC scenarios show large discrepancies between them, and few cover time periods older than AD 800. When these scenarios are used to assess the impact of human land-use on climate, contrasting results are obtained. It emphasizes the need for methods such as the REVEALS model-based land-cover reconstructions. They might help to fine-tune descriptions of past land-cover and lead to a better understanding of how long-term changes in ALCC might have influenced climate. The REVEALS model is demonstrated to provide better estimates of the regional vegetation/land-cover changes than the traditional use of pollen percentages. This will achieve a robust assessment of land cover at regional- to continental-spatial scale throughout the Holocene. We present maps of REVEALS estimates for the percentage cover of 10 plant functional types (PFTs) at 200 BP and 6000 BP, and of the two open-land PFTs "grassland" and "agricultural land" at five time-windows from 6000 BP to recent time. The LANDCLIM results are expected to provide crucial data to reassess ALCC estimates for a better understanding of the land suface-atmosphere interactions.
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Gaillard, M. J., S. Sugita, F. Mazier, J. O. Kaplan, A. K. Trondman, A. Broström, T. Hickler, et al. "Holocene land-cover reconstructions for studies on land cover-climate feedbacks." Climate of the Past Discussions 6, no. 2 (March 11, 2010): 307–46. http://dx.doi.org/10.5194/cpd-6-307-2010.
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Abstract. The major objectives of this paper are: (1) to review the pros and cons of the scenarios of past anthropogenic land cover change (ALCC) developed during the last ten years, (2) to discuss issues related to pollen-based reconstruction of the past land-cover and introduce a new method, REVEALS (Regional Estimates of VEgetation Abundance from Large Sites), to infer long-term records of past land-cover from pollen data, (3) to present a new project (LANDCLIM: LAND cover – CLIMate interactions in NW Europe during the Holocene) currently underway, and show preliminary results of REVEALS reconstructions of the regional land-cover in the Czech Republic for five selected time windows of the Holocene, and (4) to discuss the implications and future directions in climate and vegetation/land-cover modeling, and in the assessment of the effects of human-induced changes in land-cover on the regional climate through altered feedbacks. The existing ALCC scenarios show large discrepancies between them, and few cover time periods older than AD 800. When these scenarios are used to assess the impact of human land-use on climate, contrasting results are obtained. It emphasizes the need of REVEALS model-based land-cover reconstructions. They might help to fine-tune descriptions of past land-cover and lead to a better understanding of how long-term changes in ALCC might have influenced climate. The REVEALS model is proved to provide better estimates of the regional vegetation/land-cover changes than the traditional use of pollen percentages. Thus, the application of REVEALS opens up the possibility of achieving a more robust assessment of land cover at regional- to continental-spatial scale throughout the Holocene. We present maps of REVEALS estimates for the percentage cover of 10 plant functional types (PFTs) at 200 BP and 6000 BP, and of the two open-land PFTs "grassland" and "agricultural land" at five time-windows from 6000 BP to recent time. The LANDCLIM results are expected to provide crucial data to reassess ALCC estimates for a better understanding of the land suface-atmosphere interactions.
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Zeng, Tian, Lei Wang, Zengxiang Zhang, Qingke Wen, Xiao Wang, and Le Yu. "An Integrated Land Cover Mapping Method Suitable for Low-Accuracy Areas in Global Land Cover Maps." Remote Sensing 11, no. 15 (July 29, 2019): 1777. http://dx.doi.org/10.3390/rs11151777.
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In land cover mapping, an area with complex topography or heterogeneous land covers is usually poorly classified and therefore defined as a low-accuracy area. The low-accuracy areas are important because they restrict the overall accuracy (OA) of global land cover classification (LCC) data generated. In this paper, low-accuracy areas in China (extracted from the MODIS global LCC maps) were taken as examples, identified as the regions having lower accuracy than the average OA of China. An integrated land cover mapping method targeting low-accuracy regions was developed and tested in eight representative low-accuracy regions of China. The method optimized procedures of image choosing and sample selection based on an existent visually-interpreted regional LCC dataset with high accuracies. Five algorithms and 16 groups of classification features were compared to achieve the highest OA. The support vector machine (SVM) achieved the highest mean OA (81.5%) when only spectral bands were classified. Aspect tended to attenuate OA as a classification feature. The optimal classification features for different regions largely depends on the topographic feature of vegetation. The mean OA for eight low-accuracy regions was 84.4% by the proposed method in this study, which exceeded the mean OA of most precedent global land cover datasets. The new method can be applied worldwide to improve land cover mapping of low-accuracy areas in global land cover maps.
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Phi, Dinh Cao, and Nguyen Hai Hoa. "USING ATMOSPHERICALLY RESISTANT VEGETATION INDEX TO DETECT FOREST COVER CHANGE IN LAC DUONG DISTRICT, LAM DONG PROVINCE." Journal of Forestry Science and Technology, no. 14 (2022): 129–40. http://dx.doi.org/10.55250/jo.vnuf.2022.14.129-140.
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This study tested the potential of using Sentinel-2-derived ARVI for land covers mapping compared to NDVI in Lac Duong district, Lam Dong province. Overall accuracy assessments of ARVI-based land covers mapping are calculated from 88.9% (in 2016) to 92.0% (in 2022) with Kappa coefficients of 0.79 and 0.86, respectively. The comparison between Sentinel-2-derived NDVI and ARVI for land covers mapping indicated that ARVI has performed well in land covers mapping in Lac Duong district. The Sentinel-2 time series were therefore used to generate spectral-temporal changes in land covers based on ARVI thresholds. Our estimation of land covers and forest cover change shows that the forest cover in Lac Duong increased by 1718.4 ha from 2016 to 2022, while non-forest covers reduced by 1662.1 ha at the same period. Conversely, during the segmented period of 2018-2020, the extent of forest cover reduced by 1156.1 ha, whereas non-forest covers increased by 1032.8 ha. The study also identified the main drivers of LULC change, including forest cover change, these changes are due to the expansion of coffee production, effectiveness of PES policy implementation, and a lack of agricultural production and sustained likelihoods. Our study suggests that ARVI can be used to monitor and detect changes in land covers, especially in deforestation and forest degradation detection in the tropical and mountainous regions of Vietnam. More focus of comprehensive understanding of LULC change and its management should be expanded beyond the poverty-environmental relationships in Lac Duong district.
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Ahmad and R. Utomowati. "Dynamics types of land use and land cover to determine time of the value erosion coefficient criticism in Keduang Watershed." IOP Conference Series: Earth and Environmental Science 986, no. 1 (February 1, 2022): 012056. http://dx.doi.org/10.1088/1755-1315/986/1/012056.
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Abstract This research aims to: (1) Determine the trends in change of the type of plants planted by the community within a year for each type of land use in Keduang Watershed; (2) Understand the dynamics of land cover in each land-use unit in Keduang Watershed; (3) Determine the influential factors of the dynamics land use and land cover in Keduang Watershed; (4) Understand in what circumstances the criticality of erosion coefficient (C value) occurs in Keduang Watershed. The methods used are: (1) analysis of trends in changes of crop types are determined based on the rotation of the plant species planted by farmers in each land use unit; (2) The dynamics of land cover determined by the percentage (%) of the plant canopy area that covers each land use unit; (3) The affecting factor of the dynamics of land use and land cover are determined based on the causative factor of land-use change and land cover dynamics; (4) the time of occurrence of the critical value of the erosion coefficient is determined based on comparing the time of high rainfall and the lowest decrease of land cover. The result shows that: (1) In Keduang Watershed, crop rotation occurs in rainfed-rice fields and moorland; (2) Seasonal change is the main factor for change of land use and land cover; (3) The critical period of the erosion coefficient value in Keduang Watershed occurred in November.
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Ngcofe, Luncedo, and Kgaugelo Nkoana. "Sentinel-2 land cover product comparison: South African National land cover 2020 vs ESRI Global land cover 2020." Abstracts of the ICA 6 (August 11, 2023): 1–2. http://dx.doi.org/10.5194/ica-abs-6-186-2023.
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Šetka, Josip, Petra Radeljak Kaufmann, and Luka Valožić. "Land use and land cover changes in the Lower Neretva Region from 1990 to 2020." Hrvatski geografski glasnik/Croatian Geographical Bulletin 83, no. 2 (December 23, 2021): 7–31. http://dx.doi.org/10.21861/hgg.2021.83.02.01.
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Changes in land use and land cover are the result of complex interactions between humans and their environment. This study examines land use and land cover changes in the Lower Neretva Region between 1990 and 2020. Political and economic changes in the early 1990s resulted in changes in the landscape, both directly and indirectly. Multispectral image processing was used to create thematic maps of land use and land cover for 1990, 2005, and 2020. Satellite images from Landsat 5, Landsat 7 and Landsat 8 were the main source of data. Land use and land cover structure was assessed using a hybrid approach, combining unsupervised and manual (visual) classification methods. An assessment of classification accuracy was carried out using a confusion matrix and kappa coefficient. According to the results of the study, the percentage of built-up areas increased by almost 33%. Agricultural land and forests and grasslands also increased, while the proportion of swamps and sparse vegetation areas decreased.
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Adugna, Alemayehu, Assefa Abegaz, Asmamaw Legass, and Diogenes L. Antille. "RANDOM AND SYSTEMATIC LAND-COVER TRANSITIONS IN NORTH-EASTERN WOLLEGA, ETHIOPIA." BOIS & FORETS DES TROPIQUES 332 (September 18, 2017): 3–15. http://dx.doi.org/10.19182/bft2017.332.a31329.
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Africa has seen significant changes in land cover at different spatial scales. Changes in Land Use and Land Cover (LULC) include deforestation and subse- quent use of the land for arable cropping, conversion to grassland or urbanization. The work reported in this article was conducted to examine land cover transi- tions in north-eastern Wollega (Ethiopia) between 2005 and 2015. The analysis focused on land cover transitions that occurred systematically or randomly, and identified the main drivers for these changes. Landsat data from 2005 and 2015 were examined to better unders- tand the various dimensions of land cover transitions, namely: swaps, losses, gains, persistency and vulnerability. Results showed that shrubland exhibited the largest gain (22%), with a 63% gain- to-loss ratio, a 47% gain-to-persistence ratio and a positive net change-to-persis- tence ratio of 46%. Cropland showed the largest loss (19%) while grassland was the most stable type of land cover des- pite some fluctuation (»10%) observed during the 10-year period. The land cover transition was dominated by systematic processes, with few random processes of change. Systematic land cover transitions such as agricultural abandonment and vegetation re-growth were attributed to regular or common processes of change. This study suggests that the implementa- tion of practices conducive to sustainable intensification of existing agricultural land, supported by policies that promote increased diversification of Ethiopian agriculture, would mitigate pressure on forests by avoiding their future conver- sion to cropland.
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Hanafi, Fahrudin, Dinda Putri Rahmadewi, and Fajar Setiawan. "Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency." Geosfera Indonesia 6, no. 3 (December 20, 2021): 301. http://dx.doi.org/10.19184/geosi.v6i3.23471.
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Land cover changes based on cellular automata for surface temperature in Semarang Regency has increased significantly due to the continuous rise in its population. Therefore, this study aims to identify, analyze and predict multitemporal land cover changes and surface temperature distribution in 2028. Data on the land cover map were obtained from Landsat 7 and 8 based on supervised classification, while Land Surface Temperature (LST) was calculated from its thermal bands. The collected data were analyzed for accuracy through observation, while Cellular Automata - Markov Chain was used to predict the associated changes in 2028. The result showed that there are 4 land cover maps with 5-year intervals from 2003 to 2018 at an accuracy of more than 85%. Furthermore, the existing land covers were dominated by forest with decreasing trend, while the built-up area continuously increased. The existing Land surface temperature range from 20.6°C to 36.6°C, at an average of 28.2°C and a yearly increase of 0.07°C. The temperature changes are positively correlated with the occurrence of land conversion. Land cover predictions for 2028 show similar forest dominance, with a 23,4% built-up area at a surface temperature of 28.9°C.
Keywords: Land cover change; Cellular Automata-Markov Chain; Land Surface Temperature
Copyright (c) 2021 Geosfera Indonesia 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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Nguyen, Lan H., and Geoffrey M. Henebry. "Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology." Remote Sensing 11, no. 14 (July 15, 2019): 1677. http://dx.doi.org/10.3390/rs11141677.
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Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.
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Dewi, R. A., A. Triyogo, and E. Faridah. "Diversity of Collembola on Various Post-Rehabilitation Land Covers." Jurnal Manajemen Hutan Tropika (Journal of Tropical Forest Management) 30, no. 1 (April 4, 2024): 40–50. http://dx.doi.org/10.7226/jtfm.30.1.40.
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The success of rehabilitation has altered environmental conditions from critical land to new habitats for living organisms. One of the representative and sensitive mesofauna to environmental changes is Collembola. Various vegetation cover almost all of the rehabilitation area, which has impacts on microclimate and soil quality as important factors to Collembola existence. This study investigated the diversity and abundance of Collembola and its relation to environmental factors on various land covers, including teak, grass, and mixed stand land cover. Data were collected in an observation plot of 20 m × 20 m using purposive samples, including environmental measurements, Collembola collection, and soil samples for physical and chemical analysis. The results showed that different land covers affected the abundance and diversity of Collembola, even though the statistical analyses of TLC, GLC, and MLC were not significantly different. However, TLC has a higher litter thickness and organic material than the other land cover types. A few families of Collembola, such as Cyphoderidae, Brachystomellidae, Katiannidae, Isotomidae, Oncopoduridae, and Isotogastruridae, show their correlation to climatic and edaphic factors in a certain land cover.
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Kundapura Usha Aswathaiah, Suman. "An Insight to the Decadal Land Use Land Cover Changes of Dakshina Kannada District." International Journal of Science and Research (IJSR) 12, no. 3 (March 5, 2023): 694–702. http://dx.doi.org/10.21275/sr23313162026.
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Manika, Mallick, and Krishnaiah Y. V. "Spatio-Temporal Detection of Land Use Land Cover Changes in Jalpaiguri District; Geospatial Analysis." International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 1945–53. http://dx.doi.org/10.21275/sr231227231047.
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Turner, B. L., Eric F. Lambin, and Peter H. Verburg. "From land-use/land-cover to land system science." Ambio 50, no. 7 (March 13, 2021): 1291–94. http://dx.doi.org/10.1007/s13280-021-01510-4.
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Abuzar, Mohammad, Andy McAllister, Des Whitfield, and Kathryn Sheffield. "Remotely-Sensed Surface Temperature and Vegetation Status for the Assessment of Decadal Change in the Irrigated Land Cover of North-Central Victoria, Australia." Land 9, no. 9 (September 2, 2020): 308. http://dx.doi.org/10.3390/land9090308.
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Monitoring of irrigated land cover is important for both resource managers and farmers. An operational approach is presented to use the satellite-derived surface temperature and vegetation cover in order to distinguish between irrigated and non-irrigated land. Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of surface temperature and vegetation cover was achieved for each irrigation period (Spring, Summer and Autumn). The three periodic profiles were used to define irrigation land covers from 2008–2009 to 2018–2019 in a key agricultural region of Australia. The overall accuracy of identifying farms with irrigated land cover amounted to 95.7%. Total irrigated land cover was the lowest (approximately 200,000 ha) in the 2008–2009 crop year which increased more than three-fold in 2012–2013, followed by a gradual decline in the following years. Satellite images from Landsat series (L-5, L-7 and L-8), Sentinel-2 and ASTER were found suitable for land cover classification, which is scalable from farm to regional levels. For this reason, the results are desirable for a range of stakeholders.
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Venter, Zander S., David N. Barton, Tirthankar Chakraborty, Trond Simensen, and Geethen Singh. "Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover." Remote Sensing 14, no. 16 (August 21, 2022): 4101. http://dx.doi.org/10.3390/rs14164101.
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The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m2, we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m2, we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area.
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Achmad, Eva, Fazriyas, Bambang Irawan, and Adi Karta Kusuma. "Forest cover changes analysis using Landsat Imageries over Pengabuan Watershed in Jambi Province." IOP Conference Series: Earth and Environmental Science 1095, no. 1 (October 1, 2022): 012024. http://dx.doi.org/10.1088/1755-1315/1095/1/012024.
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Abstract The availability of land cover in the form of forest is very important in the watershed system (DAS). The decrease in forest cover and the carbon content stored in a watershed also shows a decline in the quality of the watershed. The research aims to examine changes in land cover that occurred during the last twenty years in the Pengabuan Watershed. Changes in land cover were obtained by classifying Landsat imagery from 1996 to 2017. Classification was carried out using the supervised classification method and visual interpretation and then followed by change detection analysis. The result showed that from 1996 to 2017, secondary dryland forest cover and bare land in the Pengabuan watershed has been declining, the rate of secondary dryland forest decreased by 53.38% (11,186.47 ha / year), the rate of decline in bare land by 1, 26% (264.29 ha / year). Land cover with an increasing area such as settlements at 2.29% (479.24 ha / year), plantation forests at 17.31% (3,626.75 ha / year), dry land agriculture at 1.63% (341, 41 ha / year), plantations at 33.12% (6,940.13 ha / year) and water bodies at 0.30% (62.94 ha / year). Changes in land cover are caused by increased land requirements for various uses and implementation of government policies. Forest cover in Pengabuan Watershed in 2017 only covers 14.1% of the total area of the Pengabuan Watershed (419,114.49 ha). This shows that the minimum coverage of existing forest in this watershed area has not reached 30%. Changes in land use patterns are one factor in causing the decrease of carrying capacity of watersheds. Conversion of forest cover, especially forest areas into plantations, agriculture, bare land and others, has led DAS area vulnerable to the occurrence of floods, erosion, the threat of drought in the dry season, decreased water quality, land productivity and the threat of siltation of the estuary due to sedimentation.
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Yihe Lü, Liding Chen, and Bojie Fu. "Land-cover effects on red soil rehabilitation in China: a meta-analysis." Progress in Physical Geography: Earth and Environment 32, no. 5 (October 2008): 491–502. http://dx.doi.org/10.1177/0309133308098942.
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To reverse the soil degradation problems resulting from overexploitation, ecological soil rehabilitation has already been an important pursuit in ecological and environmental research and applications. Based on journal publications and a meta-analysis approach, this paper investigates the effects, and their spatiotemporal variations, of different land covers on red soil rehabilitation in China. Results indicate that all the land covers used in red soil rehabilitation had been effective in improving degraded red soils but the degrees of effectiveness varied. Soil organic carbon was the most sensitive of the three major soil nutrients considered with regard to land-cover change during ecological rehabilitation. Coniferous and broadleaf mixed forest was detected as the most effective land cover in red soil rehabilitation. Some spatiotemporal trends of land-cover effectiveness on red soil rehabilitation exist in a statistically significant sense. These findings are important for land use and ecological rehabilitation decision-making such as the selection of a soil rehabilitation pathway and the determination of priority areas for ecological rehabilitation.
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Ge, Jianjun, Nathan Torbick, and Jiaguo Qi. "Biophysical Evaluation of Land-Cover Products for Land–Climate Modeling." Earth Interactions 13, no. 6 (June 1, 2009): 1–16. http://dx.doi.org/10.1175/2009ei276.1.
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Abstract The need for accurate characterization of the land surface as boundary conditions in climate models has been recognized widely in the climate modeling community. A large number of land-cover datasets are currently used in climate models either to better represent surface conditions or to study the impacts of surface changes. Deciding upon land-cover datasets can be challenging because the datasets are made with different sensors, ranging methodologies, and varying classification objectives. A new statistical measure Q was developed to evaluate land-cover datasets in land–climate interaction research. This measure calculates biophysical precision of land-cover datasets using 1-km monthly Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product. This method aggregates within-class biophysical consistency, calculated as LAI variation, across a study domain and over multiple years into a single statistic. A smaller mean Q value for a land-cover product indicates more precise biophysical characterization within the classes. As an illustration, four land-cover products were assessed in the East Africa region: Global Land Cover 2000 (GLC2000), MODIS land cover, Olson Global Ecosystems (OGE), and Land Ecosystem–Atmosphere Feedback (LEAF) model. The evaluation was conducted at three different spatial scales corresponding to 30 × 30, 50 × 50, and 100 × 100 km quadrates. The Q measure found that GLC2000 ranked higher compared to the other three land-cover products for every quadrate size. For the 30 × 30 km quadrate size GLC2000 was significantly better than LEAF, which is currently used in the Regional Atmospheric Modeling System. The statistic ranks MODIS land cover above OGE, which is above LEAF. As quadrate size increases, differences between Q decrease indicating greater uncertainty at coarser resolution. The utility of the measure is that it can be applied to any continuous parameter over any scale (space or time) to evaluate the biophysical precision of any land-cover dataset.
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Erickson, Donna L. "Rural land use and land cover change." Land Use Policy 12, no. 3 (July 1995): 223–36. http://dx.doi.org/10.1016/0264-8377(95)00005-x.
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Mwangi, P. W., F. N. Karanja, P. K. Kamau, and S. C. Letema. "CONTRIBUTION INDEX OF LAND COVER AND LAND SURFACE TEMPERATURE CHANGES IN UPPER HILL NAIROBI, KENYA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 141–49. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-141-2021.
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Abstract. Urban heat island is the difference in thermal temperature between rural and urban areas. The urbanization process alters the material type with impervious surfaces being absorbers of incoming radiation during the day and emitting it at night. The research involved the use of time-series satellite imagery from Sentinel, Landsat, ASTER and MODIS for the period 1986, 1995, 2000, 2005, 2011, 2015 and 2017 over the Upper Hill, Nairobi. Morning, afternoon and night land surface temperatures (LST) were calculated for each of these years and analyzed together with the land cover. The mean albedo was calculated to determine the relationship between each land cover and mean LST. The contribution index was calculated to determine whether a land contributed positively or negatively to the mean LST in Upper Hill. Results indicated that built-up land cover had increased from 1986 to 2017 by 0.86% per annum while forest land cover had decreased by 0.99% per annum. Sparse grassland had higher albedo and LST values of 0.81 and 27.9 °C respectively, whereas water had lower albedo and LST values of 0.09 and 25.1 °C. Water had the lowest mean LST during the day but highest mean LST in the afternoon and night in each of the years due to its high thermal capacity. Bare ground tends to have a higher contribution index compared to other land covers, while forest land cover has a negative contribution index, indicating the impact land cover types have on LST and the urban heat island effect.
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Stonewall, Adam J., Gregory E. Granato, and Tana L. Haluska. "Assessing Roadway Contributions to Stormwater Flows, Concentrations, and Loads with the StreamStats Application." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 39 (April 11, 2018): 79–87. http://dx.doi.org/10.1177/0361198118758679.
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The Oregon Department of Transportation (ODOT) and other state departments of transportation need quantitative information about the percentages of different land cover categories above any given stream crossing in the state to assess and address roadway contributions to water-quality impairments and resulting total maximum daily loads. The U.S. Geological Survey, in cooperation with ODOT and the FHWA, added roadway and land cover information to the online StreamStats application to facilitate analysis of stormwater runoff contributions from different land covers. Analysis of 25 delineated basins with drainage areas of about 100 mi2 indicates the diversity of land covers in the Willamette Valley, Oregon. On average, agricultural, developed, and undeveloped land covers comprise 15%, 2.3%, and 82% of these basin areas. On average, these basins contained about 10 mi of state highways and 222 mi of non-state roads. The Stochastic Empirical Loading and Dilution Model was used with available water-quality data to simulate long-term yields of total phosphorus from highways, non-highway roadways, and agricultural, developed, and undeveloped areas. These yields were applied to land cover areas obtained from StreamStats for the Willamette River above Wilsonville, Oregon. This analysis indicated that highway yields were larger than yields from other land covers because highway runoff concentrations were higher than other land covers and the highway is fully impervious. However, the total highway area was a fraction of the other land covers. Accordingly, highway runoff mitigation measures can be effective for managing water quality locally, they may have limited effect on achieving basin-wide stormwater reduction goals.
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Egbom, Sylvia, Florence Onyemachi Nduka, Sidney Obidimma Nzeako, Chigozie C. Ikechukwu, Joseph I. Nwachukwu, Chinonye O. Ezenwaka, Charity O. Ihejirika, et al. "SPATIAL DISTRIBUTION OF MALARIA AND LAND COVER PATTERNS IN OGONI LAND, RIVERS STATE, NIGERIA." Malaysian Journal of Science 42, no. 3 (October 31, 2023): 91–100. http://dx.doi.org/10.22452/mjs.vol42no3.12.
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The distribution of malaria is characterised by microgeographic variations determined by a range of factors, including the local environment. A study on the spatial distribution of malaria about land cover patterns was carried out by sampling Primary Health Centres in Ogoni Land. Nine Primary Healthcare Centres (PHCs) were selected across four local government areas (LGA) using Systematic Grid Point Sampling. Human blood samples were obtained from 318 consented individuals, and questionnaires were administered to obtain demographic data. Plasmodium species were identified through microscopy using thick and thin blood films. A geodatabase was created and imported into ArcGIS 10.7 to produce a thematic map of the study area. A cloud-free Landsat-8 Operational Land Imager (OLI) was employed for land cover analysis. Both supervised and unsupervised classifications of land cover were performed to generate the land cover classes. Pearson correlation was carried out to determine the significance between malaria distribution and land cover. Of the 318 individuals, 169 were infected with an overall prevalence of 53.1%.Only P. falciparum was identified and malaria distribution showed spatial variations. Across the PHCs sampled, the highest point prevalence was recorded in Model Primary Health Centre Koroma in Tai LGA whereas the lowest was recorded in MPHC Okwale in Khana LGA. Cumulatively, Kwawa PHC recorded the highest malaria prevalence whereas MPHC Bunu in Tai recorded the lowest prevalence. The highest prevalence was recorded in Khana LGA while the lowest was recorded in Eleme LGA. Land cover analysis revealed that Ogoni Land has a total land cover mass of 982.97km.2 Sparse vegetation dominated the study area (471.06km2) while dense vegetation covers a total mass of 213.1km2. Bivariate analysis showed a significant correlation between malaria prevalence and dense vegetation (p<0.05, 0.952). Dense vegetation played a significant role in malaria transmission in Ogoni Land. The study concludes that the presence of dense vegetation is associated with high malaria prevalence in the study area.
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Luo, H., B. He, X. Kuai, Y. Li, and R. Z. Guo. "LAND COVER EXTRACTION OF COASTAL AREA FROM GF-1 WFV IMAGERY USING ONTOLOGICAL METHOD." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 53–58. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-53-2020.
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Abstract. As a knowledge organization and representation method, ontology that can store land cover spectral, texture, shape attributes and relationships derived from image analysis. With the knowledge organized in ontology, the efficiency of automatic or semi-automatic land cover information extraction for the large coastal area is supposed to be improved. Together with the help of GF-1 Wide Field of View (WFV) data, which covers almost 200 km width area, the more frequent monitoring and change detection for coastal area of Guangxi province are available. This study makes attempt to monitor the land cover of Guangxi coastal area using GF-1 WFV data with ontological method. The land cover ontology for this area is established first via image feature analysis. Using this ontology, automatic image extraction from GF-1 WFV data of subsequent monitoring time is realized. The results of this study reveal that, using ontology, land cover extraction can be completed in acceptable accuracy but with higher efficiency.
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Akbar, Sandi, Sukartono Sukartono, and Bambang Hari Kusumo. "Study of Soil Carbon Reserves in Conservation Area in Gili Meno, North Lombok." Jurnal Biologi Tropis 23, no. 2 (December 23, 2023): 414–19. http://dx.doi.org/10.29303/jbt.v23i2.6307.
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Gili Meno is a small island located to the north of Lombok Island which is included in a conservation area with high potential as a tourist destination. As a tourist destination, there has been a conversion of green land into hotels and other tourist facilities which has led to changes in land use patterns. In order to reduce the increase in earth's surface temperature due to GHG emissions, especially CO2, CO2 mitigation efforts are needed by squesting carbon into the soil. Research on soil carbon stocks on Gili Meno has been carried out using descriptive methods with survey techniques, namely by taking soil samples in several types of land cover at two depths, namely 0-10 cm and 10-20 cm. The soil samples taken were used for analysis of c-organic and some physical characteristics of the soil. Based on the data obtained, mangrove land cover has the largest potential carbon reserves, namely 89.36 tons.ha-1, then followed by shrub land cover, then coconut plantations, and finally open land and settlements with values respectively 57, 50 ton.ha-1, 44.11 ton.ha-1, and 36.15 ton.ha-1. The high soil carbon reserves in the mangrove ecosystem compared to other land covers reflect that the carbon storage potential of mangrove land covers is better compared to other land cover types. Thus, for long-term land management, the sustainability of mengrove plants must be maintained.
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Ibrahim, I., A. Abu Samah, R. Fauzi, and N. M. Noor. "THE LAND SURFACE TEMPERATURE IMPACT TO LAND COVER TYPES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 871–76. http://dx.doi.org/10.5194/isprs-archives-xli-b3-871-2016.
Full textAbstract:
Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R<sup>2</sup> = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.
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Ibrahim, I., A. Abu Samah, R. Fauzi, and N. M. Noor. "THE LAND SURFACE TEMPERATURE IMPACT TO LAND COVER TYPES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 871–76. http://dx.doi.org/10.5194/isprsarchives-xli-b3-871-2016.
Full textAbstract:
Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R<sup>2</sup> = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.
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Liu, Qingsheng, Chong Huang, and He Li. "Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery." Applied Sciences 10, no. 11 (May 26, 2020): 3673. http://dx.doi.org/10.3390/app10113673.
Full textAbstract:
The existing pansharpening methods applied to recently obtained satellite data can produce spectral distortion. Therefore, quality assessments should be performed to address this. However, quality assessment of the whole image may not be sufficient, because major differences in a given region or land cover can be minimized by small differences in another region or land cover in the image. Thus, it is necessary to evaluate the performance of the pansharpening process for different regions and land covers. In this study, the widely used modified intensity-hue-saturation (mIHS), Gram–Schmidt spectral sharpening (GS), color spectral sharpening (CN), and principal component analysis (PCA) pansharpening methods were applied to Gaofen 2 (GF-2) imagery and evaluated according to region and land-cover type, which was determined via an object-oriented image analysis technique with a support vector machine-supervised method based on several reliable quality indices at the native spatial scale without reference. Both visual and quantitative analyses based on region and land cover indicated that all four approaches satisfied the demands for improving the spatial resolution of the original GF-2 multispectral (MS) image, and mIHS produced results superior to those of the GS, CN, and PC methods by preserving image colors. The results indicated differences in the pansharpening quality among different land covers. Generally, for most land-cover types, the mIHS method better preserved the spectral information and spatial autocorrelation compared with the other methods.