scholarly journals Remote Sensing in Predicting Development Patterns of Built-up Land

2021 ◽  
Vol 2 (2) ◽  
pp. 56-64
Author(s):  
Iqbal Eko Noviandi ◽  
Ramadhan Alvien Hanif ◽  
Hasanah Rahma Nur ◽  
Nandi
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Population Growth Rate ◽  
Landsat Imagery ◽  
Temporal Analysis ◽  
West Java ◽  
Development Patterns ◽  
Human Needs ◽  
Multi Temporal ◽  
Total Growth

Indonesia is a developing country whose construction and development are centered on the island of Java, especially in West Java Province. Sukabumi City is one of the areas in West Java. The development of urban areas is expanding due to various human needs to carry out the construction of buildings. Remote sensing that can be used to store developments with multi-temporal analysis with materials is Landsat imagery from 2001 to 2020. The method used is the Normalized Difference Built-up Index (NDBI). The purpose of this study is to map the development of the built-up land from year to year and predict the following years. The results of the research on the significant changes in built-up land occurred between 2013-2020, while from 2001 to 2013 there was not much change. Based on the research results, the total growth of built-up land was 1.539% per year with a population growth rate of 1.4% per year. The results of the analysis show that the area of ​​land built in Sukabumi City in 2028 is 186,7194 km2 or has increased by 21,2808 km2 since 2020.

scholarly journals Watershed Health Changes based on Vegetated Land Cover in the Upper Citarum Watershed, West Java, Province, Indonesia

2021 ◽  
Vol 940 (1) ◽  
pp. 012045
Author(s):  
K Marko ◽  
D Sutjiningsih ◽  
E Kusratmoko
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
West Java ◽  
Health Changes ◽  
The North ◽  
Health Level ◽  
Watershed Health

Abstract The increase in built-up land and the decrease in vegetated land due to human activities have worsened watershed health from time to time. This study aims to assess the watershed’s health and changes every ten years based on the percentage of vegetated land cover except agricultural land in the Upper Citarum watershed, West Java. Land cover information was obtained from the processing of Landsat imagery in 1990, 2000, 2010, and 2020 based on remote sensing using the supervised classification method. The watershed health level is determined by calculating the percentage of vegetated land cover of 173 catchments. The results show that the area of the vegetated land cover decreased from 1990 to 2000, then increased from 2000 to 2010, and decreased again from 2010 to 2020. Changes in the area of vegetated land in each period of the year affect the health level of the watershed in a spatiotemporal manner. Although these changes occur in a fluctuating manner, the number of unhealthy catchments in the Upper Citarum watershed is increasing, especially in the Ci Kapundung sub-watershed in the north and Ci Sangkuy in the south.

Land Planning, Agro-forest Systems, and Implications for Ecosystem Services: Insights from Northern Italy

2021 ◽  
Author(s):  
Antonio Tomao ◽  
Barbara Ermini ◽  
Marcela Prokopov ◽  
Adriano Conte
Keyword(s):  
Land Use ◽  
Environmental Changes ◽  
Land Use Changes ◽  
Value Added ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Development Patterns ◽  
The North ◽  
Multi Temporal ◽  
Potential Impact

Negative environmental changes generally addressed as ‘syndromes’ are evaluated in the context of Soil Degradation (SD) and interpreted by using a ‘Land-Use/Land Cover Changes’ (LULCCs) framework in order to disentangle ‘past trajectories’, ‘present patterns’, and ‘future changes’. This approach allows to discuss the potential impact on SD processes and it represents an informed basis for identifying measurable outcomes of SD. This study focuses on the case of Emilia Romagna, a region located in the North of Italy with high-value added agricultural productions. A multi-temporal analysis of land-use changes between 1954 and 2008 has been proposed, discussing the evolution of associated SD syndromes in Emilia Romagna. The contributing information have been used as a baseline for Sustainable Land Management (SLM) strategies. This framework of analysis provides useful tools to investigate and to monitor the effects of SD in the Mediterranean basin where several regions underwent common development patterns yelding global pathological symptoms of environmental degradation.

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Data Sets ◽  
Temporal Data ◽  
Landsat Images ◽  
Urban Settlements ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Remote sensing of crop residue cover using multi-temporal Landsat imagery

2012 ◽  
Vol 117 ◽  
pp. 177-183 ◽  
Author(s):  
Baojuan Zheng ◽  
James B. Campbell ◽  
Kirsten M. de Beurs
Keyword(s):  
Remote Sensing ◽  
Crop Residue ◽  
Landsat Imagery ◽  
Multi Temporal

scholarly journals Fenômenos geofísicos, captados a partir de sensores orbitais: estimativa de temperatura na superfície de Garanhuns-PE / Geophysical phenomena, obtained from orbital sensors: estimation of temperature on the surface in Garanhuns-PE

2015 ◽  
Vol 26 (45) ◽  
pp. 95
Author(s):  
Felippe Pessoa De Melo ◽  
Rosemeri Melo e Souza ◽  
Maria Betânia Moreira Amador
Keyword(s):  
Remote Sensing ◽  
Thermal Behavior ◽  
Working Conditions ◽  
Temporal Analysis ◽  
Mitigation Measures ◽  
Anthropogenic Effects ◽  
Thematic Mapping ◽  
Multi Temporal ◽  
Vector Base ◽  
Anthropogenic Processes

<p>A presente pesquisa teve como finalidade realizar uma análise multitemporal, da termodinâmica da superfície de Garanhuns-PE. Para tanto, utilizou-se as geotecnologias do Sensoriamento Remoto e Sistemas de Informações Geográficas (SIG). Juntamente com as imagens oriundas do satélite Landsat 5, sensor TM, banda 6, referentes aos anos de 1987, 2001 e 2010. Disponibilizadas pelo Instituto Nacional de Pesquisas Espaciais (INPE). A base vetorial, foi disponibilizada pelo Instituto Brasileiro de Geografia e Estatística (IBGE). Sendo assim, foi possível confeccionar um banco de dados georreferenciados e geocronológicos, o qual, viabilizou o mapeamento temático. Possibilitando, compreender o comportamento térmico dos alvos no transcorrer do tempo. Dados esses que são de suma importância para traçar medidas de mitigação dos efeitos antropogênicos na paisagem. Logo, ficou evidenciado que a maximização das médias termais no transcorrer das décadas, foram reciprocas aos processos antropogênicos</p><p> </p><p><strong>Palavras–chave:</strong> termodinâmica, geotecnologias, mapeamento temático, mitigação.</p><p> </p><p><strong>Abstract </strong></p><p>This research aimed to realize a multi-temporal analysis, the surface of thermodynamics in Garanhuns-PE. For this, it used the geotechnologies of Remote Sensing and Geographic Information Systems (GIS). Along with the images coming from the Landsat 5 satellite, TM sensor, band 6, referring to the years 1987, 2001 and 2010. Disclosed by the National Institute for Space Research (INPE). The vector base, was available by the Brazilian Institute of Geography and Statistics (IBGE). Thus, it was possible to confection a georeferenced database and geochronological, which, enabled the thematic mapping. Making it possible to understand the thermal behavior of the targets in the course of time. These data are of paramount importance to draw mitigation measures of the anthropogenic effects in the landscape. Soon, it was evident that the maximization of the hot medium in the course of decades, were reciprocal to anthropogenic processes.<strong> </strong></p><p><strong>Keywords</strong>: garbage collectors, recycling, working conditions, Association.</p><p> </p>

Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data

2014 ◽  
Vol 5 (2) ◽  
pp. 19-37 ◽  
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Small Towns ◽  
Temporal Data ◽  
Landsat Images ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

scholarly journals Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region Using Landsat Imagery and the Google Earth Engine

2019 ◽  
Vol 12 (1) ◽  
pp. 55 ◽  
Author(s):  
Cong Ou ◽  
Jianyu Yang ◽  
Zhenrong Du ◽  
Yiming Liu ◽  
Quanlong Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landsat Imagery ◽  
Google Earth ◽  
Spatiotemporal Dynamics ◽  
Landscape Patterns ◽  
Agricultural Region ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Production Approach

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon’s entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies.

scholarly journals Highlighting the Sustainability Implications of Urbanisation: A Comparative Analysis of Two Urban Areas in Ghana

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 300 ◽  
Author(s):  
Kwasi Anarfi ◽  
Ross A. Hill ◽  
Chris Shiel
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Tenure ◽  
Urban Areas ◽  
Local Level ◽  
Population Data ◽  
Temporal Analysis ◽  
Resource Extraction ◽  
Market Forces ◽  
Spatio Temporal

Ghana is urbanising rapidly, and over half of the country’s population have lived in urban areas since 2010. Although research has proliferated to explore Ghana’s urbanisation, there is a dearth of research that holistically explores the wider sustainability implications of urbanisation, offers comparative perspectives in the context of large and smaller urban areas, and provides a perspective of local level urbanisation in the context of resource extraction (mining). This study comparatively assesses two urban areas in Ghana (Kumasi and Obuasi), by conducting a spatio-temporal analysis of land cover change through remote sensing and by analysing demographic change through a synthesis of published population data, in order to highlight the sustainability implications of urbanisation. The results show that urbanisation has been rapid, and has resulted in changes in land cover and demography in Kumasi and Obuasi. The sustainability implications of urbanisation are identified to include limited economic opportunities, socio-spatial segregation, and destruction of natural vegetation. The evidence in this study provides insights into urbanisation in Ghana, and suggests that the positive sustainability impacts of urbanisation may be eroded by how factors such as market forces and land tenure interact at the local level.

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