scholarly journals DASYMETRIC METHODS APPLIED TO JACAREPAGUÁ WATERSHED

2017 ◽  
Vol 23 (4) ◽  
pp. 606-622
Author(s):  
Otto Marques dos Santos Neves ◽  
Julia Celia Mercedes Strauch ◽  
Cesar Ajara
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Population Data ◽  
Likelihood Method ◽  
Dasymetric Mapping ◽  
Auxiliary Data ◽  
Original Population ◽  
Different Types ◽  
High Resolution Images

Abstract: This paper aimed to use the dasymetric mapping methods proposed by Mennis and Hultgreen (2006) and Strauch and Ajara (2015) to estimate the variation of the distribution in the population in the Jacarepaguá Watershed. For this, population data from the census tracts of 2010 and, as auxiliary data, the map of land use and land cover obtained from the supervised classification, were used - the auxiliary data were obtained using a maximum likelihood method with high resolution images. The method proposed by Mennis and Hultgreen (2006) preserved the pycnophylactic capacity of the dasymetric mapping; however, it resulted in a dasymetric map that distributes the population among the pixels, in accordance with the population variables, and in a more homogeneous way, since it considers only two classes of urban use and occupation. In the Strauch and Ajara (2015) method, there was a loss of 0.04% of the original population, but it emphasized the density differences, by distributing the population heterogeneously, because it allows the specialist to include other classes of land use and land cover and attribute different types of weights to these classes.

scholarly journals Estimating Per Capita Land Use/Land Cover Change (LULCC) in Makurdi, Northcentral Nigeria

2021 ◽  
Vol 4 (1) ◽  
pp. p97
Author(s):  
Bernard Tarza Tyubee
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Supervised Classification ◽  
Population Data ◽  
Land Use Land Cover ◽  
Cultivated Land ◽  
Landsat Images ◽  
Per Capita

The study estimated annual and temporal variation in per capita Land Use/Land Cover Change (LULCC) in Makurdi, Northcentral Nigeria. A total of four Landsat TM/ETM+ images were acquired in April of 1991, 1996, 2001 and 2006 for the study. A total of five LULC types namely water, forest, undergrowth/wetland, cultivated land and built-up land were derived from the Landsat images using supervised classification method. The per capita LULCC was derived by dividing the areas of LULC types by the actual population data. The result showed that built-up land recorded the highest long-term gain in area by 179km2 (130%), with an increment of 8.7% per anum, and undergrowth/wetland lost 119km2 (32%) in area with a decrease of 2.1% per annum from 1991 to 2006. The per capita LULCC of built-up land has increased from 575m2/person (1991) to 1059m2/person (2006), representing an increment of 481m2/person (83%). The undergrowth/wetland recorded the highest decrease in per capita LULCC from 1542m2/person (1991) to 836m2/person (2006), representing a decline by 706m2/person (46%). The study concludes that undergrowth/wetland is the most vulnerable LULC type due to urbanisation, and sustainable urban planning should be practised to conserve the natural cover materials in the study area.

scholarly journals Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia

2019 ◽  
Vol 41 (1) ◽  
pp. 146-153 ◽  
Author(s):  
Megersa Olumana Dinka ◽  
Degefa Dhuga Chaka
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Landsat Imagery ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Post Processing ◽  
Ancillary Data ◽  
The Stability ◽  
Land Cover Maps

Abstract Land use/land cover changes (LULCC) at Adei watershed (Ethiopia) over a period of 23 years (1986–2009) has been analysed from LANDSAT imagery and ancillary data. The patterns (magnitude and direction) of LULCC were quantified and the final land use/land cover maps were produced after a supervised classification with appropriate post-processing. Image analysis results revealed that the study area has undergone substantial LULCC, primarily a shift from natural cover into managed agro-systems, which is apparently attributed to the increasing both human and livestock pressure. Over the 23 years, the aerial coverage of forest and grass lands declined by 8.5% and 4.3%, respectively. On the other hand, agricultural and shrub lands expanded by 9.1% and 3.7%, respectively. This shows that most of the previously covered by forest and grass lands are mostly shifted to the rapidly expanding farm land use classes. The findings of this study suggested that the rate of LULCC over the study period, particularly deforestation due to the expansion of farmland need to be given due attention to maintain the stability and sustainability of the ecosystem.

scholarly journals Regionalisation of asset values for risk analyses

2006 ◽  
Vol 6 (2) ◽  
pp. 167-178 ◽  
Author(s):  
A. H. Thieken ◽  
M. Müller ◽  
L. Kleist ◽  
I. Seifert ◽  
D. Borst ◽  
...  
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Large Scale ◽  
Census Data ◽  
Population Data ◽  
Dasymetric Mapping ◽  
Unit Value ◽  
Land Cover Data ◽  
Mapping Techniques ◽  
Asset Data

Abstract. In risk analysis there is a spatial mismatch of hazard data that are commonly modelled on an explicit raster level and exposure data that are often only available for aggregated units, e.g. communities. Dasymetric mapping techniques that use ancillary information to disaggregate data within a spatial unit help to bridge this gap. This paper presents dasymetric maps showing the population density and a unit value of residential assets for whole Germany. A dasymetric mapping approach, which uses land cover data (CORINE Land Cover) as ancillary variable, was adapted and applied to regionalize aggregated census data that are provided for all communities in Germany. The results were validated by two approaches. First, it was ascertained whether population data disaggregated at the community level can be used to estimate population in postcodes. Secondly, disaggregated population and asset data were used for a loss evaluation of two flood events that occurred in 1999 and 2002, respectively. It must be concluded that the algorithm tends to underestimate the population in urban areas and to overestimate population in other land cover classes. Nevertheless, flood loss evaluations demonstrate that the approach is capable of providing realistic estimates of the number of exposed people and assets. Thus, the maps are sufficient for applications in large-scale risk assessments such as the estimation of population and assets exposed to natural and man-made hazards.

scholarly journals APLIKASI CITRA SATELIT PENGINDERAAN JAUH UNTUK PERCEPATAN IDENTIFIKASI TANAH TERLANTAR

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Westi Utami ◽  
I Gede Kusuma Artika ◽  
Aziz Arisanto
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Planning ◽  
Supervised Classification ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Gis Analysis ◽  
Maximum Likelihood Algorithm ◽  
Abandoned Land ◽  
And Control

Abstract: Identification and regulation of abandoned land needs to be intensified, to contribute identification of Objects of Agrarian Reform (TORA). Mapping of potential abandoned land carried out by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN) was considered not optimally implemented if compared between the setting targets with the achievements each year. Utilization of google earth imagery and Geographic Information System (GE and GIS) is expected accelerate mapping of potentialabandoned land. Google earth image was used to interpret land cover as the basis to identify land use. Land cover classification was done using supervised classification with maximum likelihood algorithm. The results showed that google earth image and GIS were able to present existing land use, and able to identifyland that has not been used as the permit rights granted. The result of interpretation and GIS analysis was expected to be used as tool to identify potential abandoned land, as the basis to regulate, accelerate and control abandoned land in Indonesia.Intisari: Identifikasi dan penertiban tanah terlantar perlu dilakukan secara intensif, salah satunya untuk memberikan sumbangan bagi Tanah Obyek Reforma Agraria (TORA). Pemetaan potensi tanah terlantar yang dilakukan Kementerian Agraria dan Tata Ruang/Badan Pertanahan Nasional (ATR/BPN) selama ini dirasa belum optimal apabila dibandingkan antara target yang ditetapkan dengan capaian setiap tahunnya. Pemanfaatan citra google earth dan Sistem Informasi Geografi diharapkan dapat membantu pekerjaanpemetaan potensi dan identifikasi tanah terlantar. Data yang digunakan adalah citra google earth untuk interpretasi tutupan tanah sebagai dasar untuk menentukan penggunaan tanah. Klasifikasi tutupan tanah pada penelitian ini menggunakan klasifikasi terselia (supervised) dengan algoritma maxsimum likelihood. Hasil penelitian menunjukkan bahwa pemanfaatan citra google earth dan SIG mampu menyajikan data penggunaan tanah eksisting terbaru, dan mampu mengidentifikasi tanah-tanah yang tidak dimanfaatkan sesuai arahan dalam izin hak yang diberikan. Hasil interpretasi dan analisis dengan SIG ini diharapkan dapat digunakan sebagai identifikasi obyek potensi tanah terlantar untuk kemudian dijadikan sebagai dasar dalam kegiatan penertiban tanah terlantar sehingga dapat membantu percepatan penertiban tanah terlantar di Indonesia.  

scholarly journals Remote Sensing and GIS based Land Use Land Cover Analysis in Chandel District, Manipur, India

2021 ◽  
Vol 889 (1) ◽  
pp. 012046
Author(s):  
Ashangbam Inaoba Singh ◽  
Kanwarpreet Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Geographical Area ◽  
Temporal Trends ◽  
Water Bodies ◽  
Landsat 8 ◽  
Rapid Urbanization ◽  
Barren Area ◽  
Landsat 7

Abstract Rapid urbanization has dramatically altered land use and land cover (LULC). The focus of this research is on the examination of the last two decades. The research was conducted in the Chandel district of Manipur, India. The LULC of Chandel (encompassing a 3313 km2 geographical area) was mapped using remotely sensed images from LANDSAT4-5, LANDSAT 7 ETM+, and LANDSAT 8 (OLI) to focus on spatial and temporal trends between years 2000 and 2021. The LULC maps with six major classifications viz., Thickly Vegetated Area (TVA), Sparsely Vegetated Area (SVA), Agriculture Area (AA), Population Area (PA), Water Bodies (WB), and Barren Area (BA) of the were generated using supervised classification approach. For the image classification procedure, interactive supervised classification is adopted to calculate the area percentage. The results interpreted that the TVA covers approximately 65% of the total mapped area in year 2002, which has been decreased up to 60% in 2007, 56% in 2011, 55 % in 2017, and 52% in 2021. The populated area also increases significantly in these two decades. The change and increase in the PA has been observed from year 2000 (8%) to 2021 (11%). Water Bodies remain same throughout the study period. Deforestation occurs as a result of the rapid rise of the population and the extension of the territory.

scholarly journals A DEFINITION OF VISUAL INTERPRETATION CRITERIAS TO MAPPING LAND-USE AND LAND-COVER IN THE BRAZILIAN BIOMES

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 83-86
Author(s):  
E. B. Silva ◽  
S. H. M. Nogueira ◽  
A. P. S. Matos ◽  
L. L. Parente ◽  
L. G. Ferreira ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Undergraduate Students ◽  
Visual Interpretation ◽  
Landsat Images ◽  
Master's Students ◽  
Sample Data ◽  
Class Definition ◽  
High Resolution Images ◽  
Definition Of

Abstract. The present work aims to establish of Visual Interpretation Criterias of the land-use and land-cover (LULC) classes of the Brazilian biomes. The process relies on the efforts of experts from each biome, Ph.D. and Master's students, and undergraduate students in research. Due to the particularities, the criterias were elaborated individually for each biome. The classes correspond to MapBiomas collection 04 legend. In each LULC class, the user has the following information: class definition, patterns (e.g., color, texture, roughness), and historical Landsat images (RGB 564) from the dry and rainy periods, as well as high-resolution images and field photos of the class. These visual interpretation criterias was used to generate data of samples for MapBiomas mapping validation. With the help of Visual Interpretation Criterias, experienced and inexperienced interpreters were able to produce high-quality sample data without visual inspection. This initiative, a pioneer in Brazil, is a tool to support future interpretations of Brazilian biomes. The results can be found on Lapig website.

scholarly journals Comparação entre diferentes algoritmos de classificação supervisionada em imagens Landsat-8 no mapeamento temático da Bacia Hidrográfica do rio Caldas, Goiás

2019 ◽  
Vol 12 (3) ◽  
pp. 961
Author(s):  
Leovigildo Aparecido Costa Santos ◽  
Paulo Eliardo Morais de Lima
Keyword(s):  
Land Use ◽  
Maximum Likelihood ◽  
River Basin ◽  
Supervised Classification ◽  
Likelihood Method ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Entire Area ◽  
Area Of Interest

Diferentes métodos são empregados para a classificação digital de imagens, porém, podem apresentar desempenhos diferentes, sendo importante testá-los para verificar suas eficácias no mapeamento de uso e cobertura da terra com intuito de se selecionar o classificador que apresente os melhores resultados e maior veracidade em relação à verdade de campo. O objetivo deste estudo foi avaliar e comparar os desempenhos de quatro algoritmos de classificação supervisionada para o mapeamento do uso e cobertura da terra da bacia hidrográfica do Rio Caldas – GO, utilizando imagens Landsat-8. Para tanto, foram utilizadas as cenas de órbita/ponto 222/71 e 222/72, com datas de passagem em 24/10/2017 e 22/10/2017, mosaicadas para formar uma única imagem de dimensões que abrangesse toda a área de interesse. A composição RGB utilizada foi das bandas 6, 5 e 4 (R=6, G=5, B=4). Para a realização do processamento digital da imagem foi empregado o software ENVI versão 5.0 e à elaboração de mapas temáticos o QGIS 2.18. Os algoritmos testados foram: Paralelepípedo, Distância de Mahalanobis, Distância Mínima e Máxima-verossimilhança. Como parâmetros de comparação foram utilizados os coeficientes de Kappa, acurácias global e matrizes de confusão. Os melhores resultados para a classificação de uso e cobertura foram obtidos pelo método da Máxima-verossimilhança (MaxVer), os piores pelo método do Paralelepípedo, os outros classificadores apresentaram resultados intermediários entre o melhor e o pior. Com os resultados obtidos pela classificação por MaxVer, constatou-se que atualmente a maior parte do solo da bacia é ocupada pelas classes Pastagem (63,14%) e Vegetação nativa (22,07%). Comparison between different supervised classification algorithms in Landsat-8 images in the thematic mapping of the caldas river basin, GoiásA B S T R A C TDifferent methods are used for a digital classification of images, however, they can present different performances, being important to test them to verify their efficiencies in the mapping of land use and coverage in order to select the classifier that presents the best results and greater truthfulness In relation to the truth of the field. The objective of this study was to evaluate and compare the performance of four supervised classification algorithms for the mapping of the land use and land cover of the Caldas river basin - GO, using Landsat-8 images. To do so, they were like the orbit / dot scenes 222/71 and 222/72, with passing date on 10/24/2017 and 10/22/2017, mosaicked to form a single image of dimensions covering an entire area of interest . An RGB composition used for bands 6, 5 and 4 (R = 6, G = 5, B = 4). For the realization of digital image processing and the use of ENVI version 5.0 software and the development of thematic maps, QGIS 2.18. The algorithms tested were: Parallelepiped, Mahalanobis Distance, Minimum Distance and Maximum Likelihood. As the comparison parameter is used by Kappa coefficients, global accuracy and matrices of confusion. The best results for a classification of use and coverage are obtained by the Maximum-likelihood method (MaxVer), the most common methods, the other classifiers presented the intermediates between the best and the worst. With the results obtained by classification by MaxVer, it was verified that at the moment it is part of the soil of the basin is occupied by classes Pasture (63.14%) and native vegetation (22.07%).Keywords: Use and coverage; remote sensing; geoprocessing; Landsat.

scholarly journals Terminology as a key uncertainty in net land use and land cover change carbon flux estimates

2014 ◽  
Vol 5 (1) ◽  
pp. 177-195 ◽  
Author(s):  
J. Pongratz ◽  
C. H. Reick ◽  
R. A. Houghton ◽  
J. I. House
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Flux ◽  
Scientific Application ◽  
The Past ◽  
Different Types ◽  
Definition Of ◽  
Specific Definition ◽  
Terminological Confusion

Abstract. Reasons for the large uncertainty in land use and land cover change (LULCC) emissions go beyond recognized issues related to the available data on land cover change and the fact that model simulations rely on a simplified and incomplete description of the complexity of biological and LULCC processes. The large range across published LULCC emission estimates is also fundamentally driven by the fact that the net LULCC flux is defined and calculated in different ways across models. We introduce a conceptual framework that allows us to compare the different types of models and simulation setups used to derive land use fluxes. We find that published studies are based on at least nine different definitions of the net LULCC flux. Many multi-model syntheses lack a clear agreement on definition. Our analysis reveals three key processes that are accounted for in different ways: the land use feedback, the loss of additional sink capacity, and legacy (regrowth and decomposition) fluxes. We show that these terminological differences, alone, explain differences between published net LULCC flux estimates that are of the same order as the published estimates themselves. This has consequences for quantifications of the residual terrestrial sink: the spread in estimates caused by terminological differences is conveyed to those of the residual sink. Furthermore, the application of inconsistent definitions of net LULCC flux and residual sink has led to double-counting of fluxes in the past. While the decision to use a specific definition of the net LULCC flux will depend on the scientific application and potential political considerations, our analysis shows that the uncertainty of the net LULCC flux can be substantially reduced when the existing terminological confusion is resolved.

scholarly journals Terminology as a key uncertainty in net land use flux estimates

2013 ◽  
Vol 4 (2) ◽  
pp. 677-716 ◽  
Author(s):  
J. Pongratz ◽  
C. H. Reick ◽  
R. A. Houghton ◽  
J. House
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Large Range ◽  
Scientific Application ◽  
Different Types ◽  
Definition Of ◽  
High Uncertainty ◽  
Specific Definition ◽  
Terminological Confusion

Abstract. Reasons for the high uncertainty in land use and land cover change (LULCC) emissions go beyond recognized issues to do with available data on land cover change and the fact that model simulations rely on a simplified and incomplete description of the complexity of biological and LULCC processes. The large range across published LULCC emission estimates is also fundamentally to do with the exact definition of the net land use flux with respect to the way it is calculated by models. We introduce a conceptual framework that allows us to compare the different types of models and simulation setups used to derive land use fluxes. We find that published studies are based on at least 9 different definitions of the net land use flux. Our analysis reveals three key processes that are accounted for in different ways: the land use feedback, the loss of additional sink capacity, and legacy (regrowth and decomposition) fluxes. We show that these terminological differences, alone, explain differences between published net land use flux estimates that are of the order of published estimates. While the decision to use a specific definition will depend on the scientific application and potential political considerations, our analysis shows that the uncertainty of the net land use flux can be substantially reduced when the existing terminological confusion is resolved.

scholarly journals Analysis of Spatiotemporal Land Use and Land Cover Changes using Remote Sensing and GIS: A Review

2018 ◽  
Vol 7 (4.34) ◽  
pp. 159
Author(s):  
Kabir Abdulkadir Gidado ◽  
Mohd Khairul Amri Kamarudin ◽  
Nik Ahmad Firdausaq ◽  
Aliyu Muhammad Nalado ◽  
Ahmad Shakir Mohd Saudi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Lulc Changes ◽  
Detection Analysis ◽  
Socio Economic Factors

The land-use and land-cover (LULC) pattern of an area is an outcome of natural and socio-economic factors and their use spatially by man; this LULC varies from the forest, water body, agricultural land and so on. Remote Sensing (RS) and Geographical Information System (GIS) studies have predominantly focused on providing the technical knowledge of, where, and the type of LULC change that has occurred and its impacts on man and the environment. Knowledge about LULC changes is essential for understanding the relationships and interfaces between humans and the natural environment. The purpose of this article is to review the previous studies of the spatiotemporal LULC changes. However, thirty (30) articles were reviewed from 2011 to 2017. However, these articles studied the LULC, classification, changes and change detection analysis, using different methods and software of RS and G.I.S. The finding shows that these articles have overall accuracy assessment ranges from 75% to 95% validations. Also, supervised classification in Maximum Likelihood Algorithm method was mostly employed for the LULC classification. Moreover, these reviewed articles confirmed that LULC changes are imminent as a result of both natural and human factors which lead to increase and decrease of one LULC cover to another. Therefore proper monitoring of LULC changes when applied help the relevant government bodies, agencies and environmental managers utilise the environment to the fullest.  

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