Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Remote Sensing of Climate Change

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 20995

Share This Special Issue

Special Issue Editors

Prof. Dr. Ralf Ludwig

E-Mail Website
Guest Editor

Department of Geography, Ludwig-Maximilians-Universität München (LMU), 80333 Munich, Germany
Interests: climate change; land use change; hydrology; water resources management; spatial environmental modeling; multi-sensor remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Arturo Sanchez-Azofeifa

E-Mail Website
Guest Editor

Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: multi and hyper-spectral remote sensing; ecosystem succession; time series trend-analysis; geostatistics; spatial modeling; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has provided significant advances in understanding the climate system and its changes, by quantifying processes and spatio-temporal states of land surface–atmosphere interactions on various scales. Yet, the ever-growing possibilities stemming from new, high-resolution sensor technologies and advanced monitoring systems also pose challenges in making the best use of the information. New concepts and tools are needed to efficiently combine long-term (coarse resolution) data sets with these novel high-resolution observations, such as data mining, data fusion, and data-assimilation of remote sensing observations into environmental models and monitoring networks, to explore the capabilities for climate change research to its best potential.

This Special Issue invites contributions from studies that focus on understanding how climate change may impact the human and natural environment and evaluating its impacts and threats using remote sensing observations from multi-scale platforms, e.g., in situ, airborne and various satellite platforms. Contributions possibly spanning long time periods are especially welcome; these should preferably rely on the integration between earth observation imagery and data collected in the field.

Prof. Dr. Ralf Ludwig
Prof. Dr. Arturo Sanchez-Azofeifa
Guest Editors

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. Sensors is an international peer-reviewed open access semimonthly 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 variability
Climate change
Land cover dynamics
Water resources
Extreme events
Ecosystem functioning
Biodiversity
Spatio-temporal heterogeneity
Data assimilation
Sensor fusion
Time series analysis

Published Papers (5 papers)

Download All Papers

Research

20 pages, 4551 KiB

Open AccessArticle

Prediction of Sea Level Nonlinear Trends around Shandong Peninsula from Satellite Altimetry

by Jian Zhao, Ruiyang Cai and Yanguo Fan

Sensors 2019, 19(21), 4770; https://0-doi-org.brum.beds.ac.uk/10.3390/s19214770 - 02 Nov 2019

Cited by 6 | Viewed by 2592

Abstract

Sea level change is a key indicator of climate change, and the prediction of sea level rise is one of most important scientific issues. In this paper, the gridded sea level anomaly (SLA) data from satellite altimetry are used to analyze the sea level variations around Shandong Peninsula from 1993 to 2016. Based on the Complete Ensemble Empirical Mode Decomposition (CEEMD) method and Radial Basis Function (RBF) network, the paper proposes an improved sea level multi-scale prediction approach, namely, CEEMD-RBF combined model. Firstly, the multi-scale frequency oscillatory modes (intrinsic mode functions (IMFs)) representing different oceanic processes are extracted by CEEMD from the highest frequency to the lowest frequency oscillating mode. Secondly, RBF network is used to establish prediction models for various IMF components to predict their future trends, and each IMF is used as an input factor of the RBF network separately. Finally, the prediction results of each IMF component with RBF network are reconstructed to obtain the final predictions of sea level anomalies. The results shows that CEEMD is particularly suitable for analyzing nonlinear and non-stationary time series and RBF network is applicable for regional sea level prediction at different scales. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change)

► Show Figures

Figure 1

11 pages, 2833 KiB

Open AccessArticle

Remote Sensing Greenness and Urbanization in Ecohydrological Model Analysis: Asia and Australasia (1982–2015)

by Danlu Cai, Klaus Fraedrich, Yanning Guan, Shan Guo, Chunyan Zhang, Rui Sun and Zhixiang Wu

Sensors 2019, 19(21), 4693; https://0-doi-org.brum.beds.ac.uk/10.3390/s19214693 - 29 Oct 2019

Cited by 1 | Viewed by 3601

Abstract

Linking remote sensing information and ecohydrological models to improve understanding of terrestrial biosphere responses to climate and land use change has become the subject of increased interest due to the impacts of current global changes and the effect on the sustainability of human lifestyles. An application to Asia and Australasia (1982–2015) is presented, revealing the following results: (i) The broad distribution of regions with the enhanced vegetation greenness only follows the general pattern as for the whole, without obvious dependence on regional or climate fluxes ratios. That indicates a prevailing increasing greenness over land due to both the impacts of current global changes and the sustainability of human lifestyles; (ii) regions with vegetation greenness reduction reveal a unique distribution, concentrating in the water-limited domain due to the impacts of external (climatically “dry gets drier and wet gets wetter”) and internal (anthropogenically increased evaporation) changes; (iii) the external changes of dryness diverge at the boundary separating energy from water-limited regimes, and the internal changes indicate large-scale afforestation and deforestation) that occur mainly in China and Russia due to a conservation program and illegal logging, respectively, and a massive conversion of tropical forest to industrial tree plantations in Southeast Asia, leading to an increased evaporation. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change)

► Show Figures

Figure 1

14 pages, 7841 KiB

Open AccessArticle

Causality of Biodiversity Loss: Climate, Vegetation, and Urbanization in China and America

by Danlu Cai, Klaus Fraedrich, Yanning Guan, Shan Guo, Chunyan Zhang, Leila M.V. Carvalho and Xiuhua Zhu

Sensors 2019, 19(20), 4499; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204499 - 17 Oct 2019

Cited by 6 | Viewed by 3011

Abstract

Essential for directing conservation resources is to identify threatened vertebrate regions and diagnose the underlying causalities. Through relating vertebrates and threatened vertebrates to the rainfall-runoff chain, to the food chain, and to the human impact of urbanization, the following relationships are noticed: (i) The Earth’s vertebrates generally show increasing abundance and decreasing threatened species indicator (threatened species number/species abundance) for a higher Normalized Difference Vegetation Index (NDVI) or larger city-size. (ii) Regional vertebrates reveal a notable ‘U-shape profile’ (‘step-like jump’) of threatened species indicator occurs in the moderate (high) NDVI regions in China (America). (iii) Positive/green city states emerge in China and are characterized by the lowest threatened species indicators in areas of low to moderate greenness, where the greenness trend of change during the last 30 years is about three times higher in the urbanized areas than over land. (iv) Negative/brown city states emerge in America revealing high threatened species indicators for greenness exceeding NDVI > 0.2, where similar greenness trends are of both urbanized and land areas. The occurrence of green and brown city states suggests a biodiversity change pattern characterized by the threatened species indicator declining from city regimes with high to those with low indicator values for increasing ratio of the city-over-land NDVI trends. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change)

► Show Figures

Figure 1

20 pages, 6897 KiB

Open AccessArticle

The Impact of the Land Cover Dynamics on Surface Urban Heat Island Variations in Semi-Arid Cities: A Case Study in Ahmedabad City, India, Using Multi-Sensor/Source Data

by Pir Mohammad, Ajanta Goswami and Stefania Bonafoni

Sensors 2019, 19(17), 3701; https://0-doi-org.brum.beds.ac.uk/10.3390/s19173701 - 26 Aug 2019

Cited by 58 | Viewed by 6473

Abstract

This study examines the behavior of land surface temperature (LST) and surface urban heat island (SUHI) from MODIS data over Ahmedabad city, Gujarat state (India), from 2003 to 2018. Summer and winter LST patterns were analyzed, both daytime and nighttime. Ahmedabad, one of the fastest growing metropolitan cities in India, is characterized by a semi-arid climate. The investigation focuses on the SUHI variations due to warming or cooling trends of both urban and rural areas, providing quantitative interpretations by means of multi-sensor/source data. Land cover maps, normalized differential vegetation index, surface albedo, evapotranspiration, urban population, and groundwater level were analyzed across the years to assess their impact on SUHI variations. Moreover, a field campaign was carried out in summer 2018 to measure LST in several rural and urban sites. During summer daytime, the rural zone exhibits a higher average LST than the urban area, resulting in a mean negative SUHI, typical of arid cities, while a slight positive SUHI (mean intensity of 0.4 °C) during winter daytime is present. An evident positive SUHI is found only during summer (1.8 °C) and winter nighttime (3.2 °C). The negative SUHI intensity is due to the low vegetation presence in the rural area, dominated by croplands turning into bare land surfaces during the pre-monsoon summer season. Higher LST values in the rural area than in the urban area are also confirmed by the field campaign, with an average difference of about 5 °C. Therefore, the impact of the rural LST in biasing the SUHI is evident, and a careful biophysical interpretation is needed. For instance, within the urban area, the yearly intensity of the summer daytime SUHI is not correlated with the evapotranspiration, while the correspondent summer daytime LST exhibits a significant negative correlation (−0.73) with evapotranspiration. Furthermore, despite the city growth across the years, the urban area does not generally reveal a temporal increase of the magnitude of the heat island but an enlargement of its spatial footprint. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change)

► Show Figures

Figure 1

13 pages, 2502 KiB

Open AccessArticle

High Spatial Resolution Simulation of Sunshine Duration over the Complex Terrain of Ghana

by Mustapha Adamu, Xinfa Qiu, Guoping Shi, Isaac Kwesi Nooni, Dandan Wang, Xiaochen Zhu, Daniel Fiifi T. Hagan and Kenny T.C. Lim Kam Sian

Sensors 2019, 19(7), 1743; https://0-doi-org.brum.beds.ac.uk/10.3390/s19071743 - 11 Apr 2019

Cited by 6 | Viewed by 3692

Abstract

In this paper, we propose a remote sensing model based on a 1 × 1 km spatial resolution to estimate the spatio-temporal distribution of sunshine percentage (SSP) and sunshine duration (SD), taking into account terrain features and atmospheric factors. To account for the influence of topography and atmospheric conditions in the model, a digital elevation model (DEM) and cloud products from the moderate-resolution imaging spectroradiometer (MODIS) for 2010 were incorporated into the model and subsequently validated against in situ observation data. The annual and monthly average daily total SSP and SD have been estimated based on the proposed model. The error analysis results indicate that the proposed modelled SD is in good agreement with ground-based observations. The model performance is evaluated against two classical interpolation techniques (kriging and inverse distance weighting (IDW)) based on the mean absolute error (MAE), the mean relative error (MRE) and the root-mean-square error (RMSE). The results reveal that the SD obtained from the proposed model performs better than those obtained from the two classical interpolators. This results indicate that the proposed model can reliably reflect the contribution of terrain and cloud cover in SD estimation in Ghana, and the model performance is expected to perform well in similar environmental conditions. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sensing of Climate Change

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI