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Earth/Community Observations for Climate Change Research
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Earth/Community Observations for Climate Change Research

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (15 December 2017) | Viewed by 44446

Special Issue Editor

Prof. Dr. Jamal Jokar Arsanjani

E-Mail Website
Guest Editor
Geoinformatics and Earth Observation Research Group, Department of Planning, Aalborg University Copenhagen, A.C. Meyers Vænge 15, DK-2450 Copenhagen, Denmark
Interests: citizen observations; earth observation; geocomputation; GEO-artificial intelligence; data quality; environmental monitoring and assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Numerous climate change extremes have devastating impacts on human societies, societal and physical infrastructure and the natural environment.

This Special Issue aims to explore, analyze and discuss various impacts of climate change on social communities, public health, environmental and ecological systems, and/or infrastructures from scientific perspectives, through earth and community observation data. We invite you to contribute to this issue by submitting comprehensive reviews, case studies, or research articles that focus on scientific methods and innovative statistical analyses. Please feel free to inform us about your tentative paper title upon your interest and prior to your final submission.

Dr. Jamal Jokar Arsanjani
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big data
  • Climate change and public health
  • Earth observations
  • Citizen/Community observations
  • Remote sensing
  • Climate change extremes
  • Societal infrastructure
  • Impacts of climate changes on communities
  • Sustainable environments
  • Climate change issues in developing countries
  • Sustainability

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Published Papers (6 papers)

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Research

19 pages, 14311 KiB  
Article
ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data
by Sanjiwana Arjasakusuma, Yasushi Yamaguchi, Yasuhiro Hirano and Xiang Zhou
ISPRS Int. J. Geo-Inf. 2018, 7(3), 103; https://doi.org/10.3390/ijgi7030103 - 14 Mar 2018
Cited by 15 | Viewed by 7450
Abstract
Ongoing global warming has triggered extreme climate events of increasing magnitude and frequency. Under this effect, a series of extreme climate events such as drought and increased rainfall during the El Nino Southern Oscillation (ENSO) are expected to be amplified in the coming [...] Read more.
Ongoing global warming has triggered extreme climate events of increasing magnitude and frequency. Under this effect, a series of extreme climate events such as drought and increased rainfall during the El Nino Southern Oscillation (ENSO) are expected to be amplified in the coming years. Adequate mapping of regions with climate-sensitive vegetation and its associated time lag is required for appropriate mitigation planning to avoid potential negative ecological impacts towards vegetation. In this study, ENSO and climate indicator time series data, for example, Multivariate ENSO Index (MEI) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data for rainfall were linked with long-term time series vegetation proxies from remote sensing (RS proxies). ENSO- and rainfall-sensitive areas were identified from each RS proxy using the bivariate Granger test, and the areas identified by multiple RS proxies were taken to identify climate-sensitive regions in Indonesia. Of the biome types in Indonesia, savanna was the most sensitive, with approximately 53% of the total savanna area in Indonesia shown to be sensitive to ENSO and rainfall by two or more RS proxies. Rolling correlation analysis also found that the ENSO effect on the vegetation region after rainfall was positively correlated with the RS proxies with a time lag of +5 months. Therefore, rainfall can be taken as a proxy of the effects of ENSO on the temporal dynamics of sensitive vegetation regions in Indonesia. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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4439 KiB  
Article
A Content-Based Remote Sensing Image Change Information Retrieval Model
by Caihong Ma, Wei Xia, Fu Chen, Jianbo Liu, Qin Dai, Liyuan Jiang, Jianbo Duan and Wei Liu
ISPRS Int. J. Geo-Inf. 2017, 6(10), 310; https://doi.org/10.3390/ijgi6100310 - 18 Oct 2017
Cited by 16 | Viewed by 5358
Abstract
With the rapid development of satellite remote sensing technology, the size of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and [...] Read more.
With the rapid development of satellite remote sensing technology, the size of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection, and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval from a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature, integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing and also deal with problems related to seasonal changes, as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval. The experiment results obtained using a Landsat data set show that the use of the new model can produce promising results. A coverage rate and mean average precision of 71% and 89%, respectively, were achieved for the top 20 returned pairs of images. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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5116 KiB  
Article
An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran
by Roya Habibi, Ali Asghar Alesheikh, Ali Mohammadinia and Mohammad Sharif
ISPRS Int. J. Geo-Inf. 2017, 6(9), 270; https://doi.org/10.3390/ijgi6090270 - 31 Aug 2017
Cited by 51 | Viewed by 7877
Abstract
Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, [...] Read more.
Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran’s I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM2.5) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord’s PCI suggested a more polluted air quality than the Moran’s I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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7272 KiB  
Article
Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014
by Patcha Huntra and Tim C. Keener
ISPRS Int. J. Geo-Inf. 2017, 6(8), 249; https://doi.org/10.3390/ijgi6080249 - 14 Aug 2017
Cited by 21 | Viewed by 8595
Abstract
Many factors impact a city’s water consumption, including population distribution, average household income, water prices, water conservation programs, and climate. Of these, however, meteorological effects are considered to be the primary determinants of water consumption. In this study, the effects of climate on [...] Read more.
Many factors impact a city’s water consumption, including population distribution, average household income, water prices, water conservation programs, and climate. Of these, however, meteorological effects are considered to be the primary determinants of water consumption. In this study, the effects of climate on residential water consumption in Las Vegas, Nevada, were examined during the period from 1990 to 2014. The investigations found that climatic variables, including maximum temperature, minimum temperature, average temperature, precipitation, diurnal temperature, dew point depression, wind speed, wind direction, and percent of calm wind influenced water use. The multivariate autoregressive integrated moving average (ARIMAX) model found that the historical data of water consumption and dew point depression explain the highest percentage of variance (98.88%) in water use when dew point depression is used as an explanatory variable. Our results indicate that the ARIMAX model with dew point depression input, and average temperature, play a significant role in predicting long-term water consumption rates in Las Vegas. The sensitivity analysis results also show that the changes in average temperature impacted water demand three times more than dew point depression. The accuracy performance, specifically the mean average percentage error (MAPE), of the model’s forecasting is found to be about 2–3% from five years out. This study can be adapted and utilized for the long-term forecasting of water demand in other regions. By using one significant climate factor and historical water demand for the forecasting, the ARIMAX model gives a forecast with high accuracy and provides an effective technique for monitoring the effects of climate change on water demand in the area. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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10391 KiB  
Article
Exploring the Relationship between the Arid Valley Boundary’s Displacement and Climate Change during 1999–2013 in the Upper Reaches of the Min River, China
by Yalin Guo, Qing Wang and Min Fan
ISPRS Int. J. Geo-Inf. 2017, 6(5), 146; https://doi.org/10.3390/ijgi6050146 - 6 May 2017
Cited by 9 | Viewed by 5299
Abstract
The arid valley is a unique type of ecological fragile landscape in the Hengduan Mountain Area, China. The boundary of the arid valley is one of the response indicators to mountainous climate change. Based on the meteorological data from 1999 to 2013 and [...] Read more.
The arid valley is a unique type of ecological fragile landscape in the Hengduan Mountain Area, China. The boundary of the arid valley is one of the response indicators to mountainous climate change. Based on the meteorological data from 1999 to 2013 and the SPOT remote sensing images in 1999 and 2013 this study explored the response characteristics of the arid valley boundary to regional climate change in the upper reaches of the Min River in the Hengduan Mountains. The results are as follows: (1) During 1999–2013, the temperature, precipitation, and evaporation increased, and the sunshine duration and relative humidity showed decreasing trends at the rates of 0.008 °C/a, 2.25 mm/a, 5.51 mm/a, −8.72 h/a, and −0.19%/a, respectively. Meanwhile, the climate showed the warm-dry tendency in the southern region and the warm-humid tendency in the central and northern areas. (2) On the whole, the arid valley boundary mainly distributed between 1601–3200 m and moved downward to 2428 m at the speed of −0.76 ± 0.26 m/a along with global warming. The descent speeds in different regions showed the same decreasing order as the regional distributions of precipitation and sunshine duration. (3) The arid valley boundary’s displacement in the whole basin had significant negative correlations with current climate change (p < 0.05), as well as with variations of moisture factors. Additionally, with the enhancements of the drought degree and humidity tendency, the variations of temperature, evaporation, and relative humidity, respectively, became the main factors that had significant correlations with the arid valley boundary’s displacement. Therefore, climate change during 1999–2013 shows beneficial effects on the improvement of the arid valley habitat in the upper reaches of the Min River. The study provides a new method and gives basic data for research on climate change. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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11552 KiB  
Article
Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015
by Xia Wang, Yaolin Liu, Feng Ling, Yanfang Liu and Feiguo Fang
ISPRS Int. J. Geo-Inf. 2017, 6(3), 68; https://doi.org/10.3390/ijgi6030068 - 2 Mar 2017
Cited by 69 | Viewed by 8452
Abstract
Ningbo City in Zhejiang Province is one of the largest port cities in China and has achieved high economic development during the past decades. The port construction, land reclamation, urban development and silt deposition in the Ningbo coastal zone have resulted in extensive [...] Read more.
Ningbo City in Zhejiang Province is one of the largest port cities in China and has achieved high economic development during the past decades. The port construction, land reclamation, urban development and silt deposition in the Ningbo coastal zone have resulted in extensive coastline change. In this study, the spatio-temporal change of the Ningbo coastlines during 1976–2015 was detected and analysed using Landsat time-series images from different sensors, including Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI). Fourteen individual scenes (covering seven phases) of cloud-free Landsat images within the required tidal range of ±63 cm were collected. The ZiYuan-3 (ZY-3) image of 2015 was used to extract the reference coastline for the accuracy assessment. The normalised difference water index (NDWI) and the modified normalized difference water index (MNDWI) were applied to discriminate surface water and land features, respectively. The on-screen digitising approach was then used to further refine the extracted time-series coastlines in the period from 1976 to 2015. Six relevant indices, length, length change, annual length change, fractal dimension (FD), average net shoreline movement (NSM) and average annual NSM, were calculated to analyse and explore the spatio-temporal change features of Ningbo coastlines. Results show that the length of the Ningbo coastlines increased from 910 km to 986 km, and the value of FD increased from 1.09 to 1.12, and the coastline morphology changed from sinuous to straight. The average NSM increased from 187 m to 298 m and the average annual NSM reached 85 m/year, indicating the advance of coastlines towards the sea at a high level. The spatio-temporal change patterns also varied in different areas. In Hangzhou Bay, significant advancement along the coastlines was experienced since 2001 mainly because of urban construction and land reclamation. In Xiangshan Bay, the forces of nature played a major role in coastline dynamics before 2008, whilst port construction, urban construction and island link projections moved the coastlines towards the sea. The coastline changes of Sanmen Bay were affected by the interaction of nature and human activities. All these observations indicate that forces of nature and human activities were the two important influential factors for the observed coastline change. In this case, the coastline complexity variation was considered responsible for various coastline patterns change of the Ningbo coast. In addition, erosion and accretion occurred in turn because of forces of nature and human activities, such as urban development and agricultural exploitation. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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