scholarly journals Comparison of variance estimation methods for use with two-dimensional systematic sampling of land use/land cover data

2014 ◽  
Vol 61 ◽  
pp. 87-97 ◽  
Author(s):  
Linda Aune-Lundberg ◽  
Geir-Harald Strand
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Variance Estimation ◽  
Estimation Methods ◽  
Systematic Sampling ◽  
Land Use Land Cover ◽  
Two Dimensional ◽  
Land Cover Data

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

scholarly journals Land Use/Land Cover Data of the Urban Atlas and the Cadastre of Real Estate: An Evaluation Study in the Prague Metropolitan Region

Land ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 153 ◽  
Author(s):  
Ondrej Micek ◽  
Jan Feranec ◽  
Premysl Stych
Keyword(s):  
Land Use ◽  
Real Estate ◽  
Land Cover ◽  
Relevant Information ◽  
Evaluation Study ◽  
Metropolitan Region ◽  
Land Use Land Cover ◽  
Land Cover Data ◽  
Scale Levels ◽  
The Difference

Landscape research involves a large number of scientific disciplines. Different disciplinary and scale approaches have led to the creation of numerous land use/land cover databases with different classification nomenclature. It is very important for end-users of databases to know the capabilities and limits of land use/land cover data to avoid potential mistakes resulting from inappropriate combinations and interpretations. In this context, the aim of this study was to evaluate the thematic content of the Urban Atlas database and data from the Czech cadastre of real estate in the Prague metropolitan region between the years 2006 and 2012 with a focus on the meaning of the nomenclature used by both datasets. The data were processed using approaches with different levels of thematic harmonisation and statistical tools to quantify the similarities and differences among the researched data. The methods of comparison used for land use/land cover data with different nomenclature were based on an aggregation approach or modified difference indices (the overall difference index and the sub-index of the difference). The areas with high degrees of dissimilarity and similarity were found and further examined and interpreted. These intentions were documented precisely on the Czech cadastre of real estate and the Urban Atlas databases at two scale levels: 1) an analysis of the whole area of the Prague metropolitan region and 2) a detailed analysis of the selected cadastral units. It was proven that the differences between both datasets are significant and they share certain characteristics. Most of the differences are distributed in the classes of the built-up areas, gardens, and other areas. Smaller differences are characteristic for waterways, agricultural lands, and forests. This study provides relevant information on the evaluated databases with the intention of raising awareness of their limits, strengths, and weaknesses. The results enhance the scientific knowledge about the Urban Atlas and Czech cadastre of real estate databases, thereby facilitating decision-making about the options of their use.

scholarly journals GLOBAL LAND-USE AND LAND-COVER DATA FOR ECOLOGISTS: HISTORICAL, CURRENT AND FUTURE SCENARIOS

2021 ◽  
Author(s):  
Mariana Moncassim Vale ◽  
Taina Carreira da Rocha ◽  
Matheus de Souza Lima Ribeiro
Keyword(s):  
Land Use ◽  
Global Change ◽  
Land Cover ◽  
Ecological Niche Modeling ◽  
Climatic Conditions ◽  
Land Use Land Cover ◽  
Distribution Models ◽  
Change Analysis ◽  
Land Cover Data ◽  
Global Change Scenarios

Land-use land-cover (LULC) data are important predictors of species occurrence and biodiversity threat. Although there are LULC datasets available for ecologists under current conditions, there is a lack of such data under historical and future climatic conditions. This hinders, for example, projecting niche and distribution models under global change scenarios at different times. The Land Use Harmonization Project (LUH2) is a global terrestrial dataset at 0.25° spatial resolution that provides LULC data from 850 to 2300 for 12 LULC state classes. The dataset, however, is compressed in a file format (NetCDF) that is incompatible with most ecological analysis and intractable for most ecologists. Here we selected and transformed the LUH2 data in order to make it more useful for ecological studies. We provide LULC for every year from 850 to 2100, with data from 2015 on provided under two Shared Socioeconomic Pathways (SSP2 and SSP5). We provide two types of file for each year: separate files with continuous values for each of the 12 LULC state classes, and a single categorical file with all state classes combined. To create the categorical layer, we assigned the state with the highest value in a given pixel among the 12 continuous data. The final dataset provides LULC data for 1251 years that will be of interest for macroecology, ecological niche modeling, global change analysis, and other applications in ecology and conservation. We also provide a description of LUH2 prediction of future LULC change through time.

scholarly journals Global land-use and land-cover data for ecologists: Historical, current, and future scenarios

2021 ◽  
Vol 16 (1) ◽  
pp. 28-38
Author(s):  
Tainá Rocha ◽  
Mariana M Vale ◽  
Matheus S. Lima-Ribeiro
Keyword(s):  
Land Use ◽  
Global Change ◽  
Land Cover ◽  
Ecological Niche Modeling ◽  
Climatic Conditions ◽  
Land Use Land Cover ◽  
Distribution Models ◽  
Change Analysis ◽  
Land Cover Data ◽  
Global Change Scenarios

Land-use land-cover (LULC) data are important predictors of species occurrence and biodiversity threat. Although there are LULC datasets available for ecologists under current conditions, there is a lack of such data under historical and future climatic conditions. This hinders, for example, projecting niche and distribution models under global change scenarios at different times. The Land Use Harmonization Project (LUH2) is a global terrestrial dataset at 0.25o spatial resolution that provides LULC data from 850 to 2300 for 12 LULC state classes. The dataset, however, is compressed in a file format (NetCDF) that is incompatible with most ecological analysis and intractable for most ecologists. Here we selected and transformed the LUH2 data in order to make it more useful for ecological studies. We provide LULC for every year from 850 to 2100, with data from 2015 on provided under two Shared Socioeconomic Pathways (SSP2 and SSP5). We provide two types of file for each year: separate files with continuous values for each of the 12 LULC state classes, and a single categorical file with all state classes combined. To create the categorical layer, we assigned the state with the highest value in a given pixel among the 12 continuous data. The final dataset provides LULC data for 1251 years that will be of interest for macroecology, ecological niche modeling, global change analysis, and other applications in ecology and conservation. We also provide a description of LUH2 prediction of future LULC change through time.

scholarly journals Influence of Land Use Land Cover on Cyclone Track Prediction – A Study During Aila Cyclone

2012 ◽  
Vol 6 (1) ◽  
pp. 33-41 ◽  
Author(s):  
K. V.S. Badarinath ◽  
D. V. Mahalakshmi ◽  
Satyaban Bishoyi Ratna
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Mesoscale Model ◽  
Surface Processes ◽  
Land Use Land Cover ◽  
Land Surface Processes ◽  
Mm5 Model ◽  
Land Cover Data ◽  
The Impact

Land-surface processes are one of the important drivers for weather and climate systems over the tropics. Realistic representation of land surface processes in mesoscale models over the region will help accurate simulation of numerical forecasts. The present study examines the influence of Land Use/ Land Cover Change (LULC) on the forecasting of cyclone intensity and track prediction using Mesoscale Model (MM5). Gridded land use/land cover data set over the Indian region compatible with the MM5 model were generated from Indian Remote Sensing Satellite (IRS-P6) Advanced Wide Field Sensor (AWiFS) for the year 2007-2008. A case study of simulation of ‘Aila’ cyclone has been considered to see the impact of these two sets of LULC data with the use of MM5 model. Results of the study indicated that incorporation of current land use/land cover data sets in mesoscale model provides better forecasting of cyclonic track.

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