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Land
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Time Series Analysis by Focusing on Climate-Land Interactions Variables
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Time Series Analysis by Focusing on Climate-Land Interactions Variables

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land–Climate Interactions".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 21202

Special Issue Editors

Dr. Isa Ebtehaj

E-Mail Website1 Website2
Guest Editor
Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V0A6, Canada
Interests: climate change; deep learning; hydroinfomatics; machine learning; sediment transport; time series; water resource management
Special Issues, Collections and Topics in MDPI journals
Dr. Babak Mohammadi

E-Mail Website
Assistant Guest Editor
Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden
Interests: surface water hydrology; snow hydrology; remote sensing; hydrological modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Time series analysis is a statistical technique that deals with time series data or trend analysis. Accurate results of forecasting in time series modelling can be helpful for classification, regression, prediction, and also numerical computation. Notably, research on time series forecasting has led to advances in many statistical and numerical methods. The machine learning approach is a robust tool for forecasting real-world problems, especially time-series-based problems such as land systems, climate variables, soil temperature, sediment, streamflow, reservoir inflow, etc. The application of machine learning in the time series analysis area has proven very useful in addressing the complexity of computation. The systematic use of machine learning with particular focus on deep learning is receiving much attention, as is time series analysis for modeling, classification, clustering, trend analysis and forecasting solutions in land studies.

In this Special Issue, we would like to encourage people to contribute their latest developments, ideas and review articles on climate–land-based time series forecasting and its applications. This Special Issue will focus on essential climate–land-based applications in the time series analysis sector. Topics include, but are not limited to, the following:

  • Time series forecasting for climate–land interactions;
  • Application of time series analysis in soil, sediment, and water systems;
  • Data mining methods in time series analysis for land management;
  • Climate–land-based time series forecasting and its applications;
  • Application of spatial–temporal statistical analysis in land cover studies;
  • Time series forecasting in renewable energy and its impact on land (wind power, solar radiation, and hydropower);
  • Applied new approaches for time series analysis in land systems science.

Dr. Isa Ebtehaj
Guest Editor
Mr. Babak Mohammadi
Assistant Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Land 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 2600 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

  • climate–land
  • time series forecasting
  • land cover
  • land–energy prediction
  • machine learning
  • data mining in land management

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Published Papers (5 papers)

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Research

21 pages, 6636 KiB  
Article
Regional and Phased Vegetation Responses to Climate Change Are Different in Southwest China
by Meng Wang and Zhengfeng An
Land 2022, 11(8), 1179; https://doi.org/10.3390/land11081179 - 28 Jul 2022
Cited by 5 | Viewed by 1655
Abstract
Southwestern China (SW) is simultaneously affected by the East Asian monsoon, South Asian monsoon and westerly winds, forming a complex and diverse distribution pattern of climate types, resulting in a low interpretation rate of vegetation changes by climate factors in the region. This [...] Read more.
Southwestern China (SW) is simultaneously affected by the East Asian monsoon, South Asian monsoon and westerly winds, forming a complex and diverse distribution pattern of climate types, resulting in a low interpretation rate of vegetation changes by climate factors in the region. This study explored the response characteristics of vegetation to climatic factors in the whole SW and the core area of typical climate type and the phased changes in response, adopting the form of “top-down”, using linear trend method, moving average method and correlation coefficient, and based on the climate data of CRU TS v. 4.02 for the period 1982–2017 and the annual maximum, 3/4 quantile, median, 1/4 quantile, minimum and average (abbreviated as P100, P75, P50, P25, P5 and Mean) of GIMMS NDVI, which were to characterize vegetation growth conditions. Coupling with the trend and variability of climate change, we identified four major types of climate change in the SW, including the significant increase in both temperature and precipitation (T+*-P+*), the only significant increase in temperature and decrease (T+*-P−) or increase (T+*-P+) of precipitation and no significant change (NSC). We then screened out nine typical areas of climate change types (i.e., core areas (CAs)), followed by one T+*-P+* area, which was located in the center of the lake basin of the Qiangtang Plateau. The response of vegetation to climatic factors in T+*-P+* area/T+*-P+ areas and T+*-P− areas/NSC areas were mainly manifested in an increase and a significant decrease, which makes the response characteristics of vegetation to climatic factors in the whole SW have different directionality at different growth stages. Our results may provide new ideas for clearly showing the complexity and heterogeneity of the vegetation response to climate change in the region under the background of global warming. Full article
(This article belongs to the Special Issue Time Series Analysis by Focusing on Climate-Land Interactions Variables)
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28 pages, 10231 KiB  
Article
Linking Spatial–Temporal Changes of Vegetation Cover with Hydroclimatological Variables in Terrestrial Environments with a Focus on the Lake Urmia Basin
by Ehsan Foroumandi, Vahid Nourani, Dominika Dąbrowska and Sameh Ahmed Kantoush
Land 2022, 11(1), 115; https://doi.org/10.3390/land11010115 - 11 Jan 2022
Cited by 20 | Viewed by 3134
Abstract
Investigation of vegetation cover is crucial to the study of terrestrial ecological environments as it has a close relationship with hydroclimatological variables and plays a dominant role in preserving the characteristics of a region. In Iran, the current study selected the watersheds of [...] Read more.
Investigation of vegetation cover is crucial to the study of terrestrial ecological environments as it has a close relationship with hydroclimatological variables and plays a dominant role in preserving the characteristics of a region. In Iran, the current study selected the watersheds of two rivers, Nazloo-Chay and Aji-Chay, to systematically investigate the implications and causes of vegetation cover variations under changing environments. These two rivers are among the essential inflows to Lake Urmia, the second largest saline lake on Earth, and are located on the west and east sides of the lake, respectively. There has been a debate between the people living in the rivers’ watersheds about who is responsible for the decline in the level of Lake Urmia—does responsibility fall with those on the east side or with those on the west side? In this study, the normalized difference vegetation index (NDVI) was used as a remotely sensed index to study spatial–temporal pattern changes in vegetation. Moreover, the temperature, precipitation, and streamflow time series were gathered using ground measurements to explore the causes and implications of changing vegetation cover. Discrete wavelet transform was applied to separate the different components of the time series. The Mann–Kendall (MK) test was applied to the time series on monthly, seasonal, and annual time scales. The connections and relationship between the NDVI time series and temperature, precipitation, and streamflow time series and any underlying causes were investigated using wavelet transform coherence (WTC). Land use maps were generated for different years using a support vector machine (SVM) in the final stage. The results indicated that the most dominant monthly, seasonal, and annual hydrological periodicities across the watersheds are 8 months, 6 months, and 2 years, respectively. The increasing vegetation cover during stable hydro-environmental periods revealed unusual conditions in the Aji-Chay watershed and reflected agricultural expansion. The WTC graphs indicated sudden changes in mutual periodicities and time-lags with different patterns between variables, which indicates the increasing anthropogenic activities in both watersheds. However, this was more dominant in the Aji-Chay watershed. The land use maps and investigation of the averaged NDVI maps also denoted that the areas of cultivated land have increased by 30% in the Aji-Chay watershed, and crop types have been changed to the crops with a higher demand for water in both watersheds. Full article
(This article belongs to the Special Issue Time Series Analysis by Focusing on Climate-Land Interactions Variables)
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17 pages, 2552 KiB  
Article
Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China
by Maomao Zhang, Cheng Zhang, Abdulla-Al Kafy and Shukui Tan
Land 2022, 11(1), 14; https://doi.org/10.3390/land11010014 - 22 Dec 2021
Cited by 75 | Viewed by 6094
Abstract
The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in [...] Read more.
The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show that the built-up area of Wuhan has expanded, reaching 511.51 and 545.28 km2, while the area of vegetation, water bodies and bare land will decrease to varying degrees in 2030 and 2040. If the built-up area continues to expand rapidly, the proportion of 30~35 °C will rise to 52.925% and 55.219%, and the affected area with the temperature >35 °C will expand to 15.264 and 33.612 km2, respectively. The direction of the expansion range of the LST temperature range is obviously similar to the expansion of the built-up area. In order to control and alleviate UHI, the rapid expansion of impervious layers (built-up areas) should be avoided to the greatest extent, and the city’s “green development” strategy should be implemented. Full article
(This article belongs to the Special Issue Time Series Analysis by Focusing on Climate-Land Interactions Variables)
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24 pages, 5031 KiB  
Article
Impacts of Future Sea-Level Rise under Global Warming Assessed from Tide Gauge Records: A Case Study of the East Coast Economic Region of Peninsular Malaysia
by Milad Bagheri, Zelina Z. Ibrahim, Mohd Fadzil Akhir, Bahareh Oryani, Shahabaldin Rezania, Isabelle D. Wolf, Amin Beiranvand Pour and Wan Izatul Asma Wan Talaat
Land 2021, 10(12), 1382; https://doi.org/10.3390/land10121382 - 14 Dec 2021
Cited by 9 | Viewed by 4708
Abstract
The effects of global warming are putting the world’s coasts at risk. Coastal planners need relatively accurate projections of the rate of sea-level rise and its possible consequences, such as extreme sea-level changes, flooding, and coastal erosion. The east coast of Peninsular Malaysia [...] Read more.
The effects of global warming are putting the world’s coasts at risk. Coastal planners need relatively accurate projections of the rate of sea-level rise and its possible consequences, such as extreme sea-level changes, flooding, and coastal erosion. The east coast of Peninsular Malaysia is vulnerable to sea-level change. The purpose of this study is to present an Artificial Neural Network (ANN) model to analyse sea-level change based on observed data of tide gauge, rainfall, sea level pressure, sea surface temperature, and wind. A Feed-forward Neural Network (FNN) approach was used on observed data from 1991 to 2012 to simulate and predict the sea level change until 2020 from five tide gauge stations in Kuala Terengganu along the East Coast of Malaysia. From 1991 to 2020, predictions estimate that sea level would increase at a pace of roughly 4.60 mm/year on average, with a rate of 2.05 ± 7.16 mm on the East Coast of Peninsular Malaysia. This study shows that Peninsular Malaysia’s East Coast is vulnerable to sea-level rise, particularly at Kula Terengganu, Terengganu state, with a rate of 1.38 ± 7.59 mm/year, and Tanjung Gelang, Pahang state, with a rate of 1.87 ± 7.33 mm/year. As a result, strategies and planning for long-term adaptation are needed to control potential consequences. Our research provides crucial information for decision-makers seeking to protect coastal cities from the risks of rising sea levels. Full article
(This article belongs to the Special Issue Time Series Analysis by Focusing on Climate-Land Interactions Variables)
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18 pages, 50627 KiB  
Article
Effects of Meteorological Parameters on Surface Water Loss in Burdur Lake, Turkey over 34 Years Landsat Google Earth Engine Time-Series
by Sohaib K. M. Abujayyab, Khaled H. Almotairi, Mohammed Alswaitti, Salem S. Abu Amr, Abbas F. M. Alkarkhi, Enes Taşoğlu and Ahmad MohdAziz Hussein
Land 2021, 10(12), 1301; https://doi.org/10.3390/land10121301 - 26 Nov 2021
Cited by 12 | Viewed by 3913
Abstract
The current work aims to examine the effect of meteorological parameters as well as the temporal variation on the Burdur Lake surface water body areas in Turkey. The data for the surface area of Burdur Lake was collected over 34 years between 1984 [...] Read more.
The current work aims to examine the effect of meteorological parameters as well as the temporal variation on the Burdur Lake surface water body areas in Turkey. The data for the surface area of Burdur Lake was collected over 34 years between 1984 and 2019 by Google Earth Engine. The seasonal variation in the water bodies area was collected using our proposed extraction method and 570 Landsat images. The reduction in the total area of the lake was observed between 206.6 km2 in 1984 to 125.5 km2 in 2019. The vegetation cover and meteorological parameters collected that affect the observed variation of surface water bodies for the same area include precipitation, evapotranspiration, albedo, radiation, and temperature. The selected meteorological variables influence the reduction in lake area directly during various seasons. Correlations showed a strong positive or negative significant relationship between the reduction and the selected meteorological variables. A factor analysis provided three components that explain 82.15% of the total variation in the data. The data provide valuable references for decision makers to develop sustainable agriculture and industrial water use policies to preserve water resources as well as cope with potential climate changes. Full article
(This article belongs to the Special Issue Time Series Analysis by Focusing on Climate-Land Interactions Variables)
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