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Special Issue "Global Biospheric Monitoring with Remote Sensing"
Deadline for manuscript submissions: 26 June 2020.
Tel. 34 91 336 70 80
Interests: Indexes development; time series analysis; agricultural and forest monitoring; fire risk
Interests: soil/vegetation carbon cycling of terrestrial ecosystems; remote sensing of vegetation; model-data integration—mainly machine learning approaches
The biosphere as the interface between lithosphere and atmosphere modulates most of the Earth processes, enabling the cycling of energy, water, and chemical elements. As the living part of the Earth, it maintains a delicate equilibrium, highly dependent on climate dynamics and anthropic impacts. On a yearly basis, the biosphere is always changing in response to annual climate variation; in addition, large-scale climatic variability can have a strong impact on biosphere functioning at longer time scales.
The role of the biosphere on the functioning of biogeochemical cycles results in substantial local or regional alterations that can impact the conditions of the entire planet, including the climate. In addition, climate change occurring at a global scale has an effect on atmosphere–land surface interactions in all regions of the planet.
Already hundreds of years ago geographers and naturalists were exploring the Earth trying to discover the underlying processes that drive biosphere functioning and structure. Important findings were made when these scientists gathered and analyzed huge amounts of local information, during long trips along the hemispheres. Nowadays, our biosphere and landscapes are so fragmented that it would be difficult to derive general patterns from local observations.
Anthropogenic impacts interplay with natural gradients providing a high level of complexity to biosphere functioning. Thus, monitoring must be framed both in the spatial and temporal dimensions in order to assess the spatial distribution of the biosphere temporal patterns and the temporal characteristics of the biosphere spatial patterns.
At present, technical advances enable the exploration and monitoring of the biosphere. Remote sensing is potentially the most powerful tool to explore the Earth, making it possible to assess biosphere dynamics at several scales. More recently increases in computing capabilities have opened new possibilities to manage and analyze the large amounts of land surface information acquired by satellites.
This Special Issue intends to disseminate advanced research on biosphere monitoring based on remote sensing data at the regional and global scales. It represents an opportunity to bring together new methodologies/paradigms to advance efficient biosphere monitoring. All topics related to biosphere functioning are considered, for example, biodiversity, phenology, land use change, burning dynamics, energy balance, and soil resources. We are inviting papers including, but not limited to the following research lines:
- Assessing patterns of biosphere dynamics at short, medium, and long terms such as early warning methodologies and identification of anomalies and trends among others.
- Assessing the impact of climate change and anthropogenic drivers on the biosphere.
- Assessing the impact of vegetation dynamics and land use change on climatic patterns.
- Developing forecasting models for biosphere dynamics
- Developing and use of novel spectral indexes to better understand biosphere functioning.
Dr. Alicia Palacios-Orueta
Dr. Xiaolu Tang
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 papers will be 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. Remote Sensing 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 2000 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.
- Climate change
- Land use and land cover dynamics
- Spectral indices
- Time series analysis
- Vegetation anomalies and trends
- Vegetation modeling
- Biogeochemical cycles
- Energy balance
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
1. Quantifying Global Rangeland Primary Production, Its Consumption by Livestock, and Net Contributions to the Global Carbon Cycle
Authors: Julie Wolf 1,*, Min Chen 2, Ghassem Asrar 2 and Bem Bond-Lamberty 2
Abstract: Livestock grazing occupies an estimated 22-26% or more of global ice-free land and supplies ~ 58% of total livestock intake globally, representing the annual removal of ca. 1.65 Pg of carbon (C) from global rangelands. Grazing area and intensity (the proportion of annual net primary productivity (NPP) grazed) are difficult to quantify, owing to i) the paucity and difficulty of direct measurements, ii) the incremental nature of both plant growth and grazing, iii) variability over multiple temporal and spatial scales, and iv) grazing may occur on land used for crops or other uses at other times. High uncertainty as well as very high or impossible grazing intensities (e.g. >100%) are often found in some regions.
Because good estimates of both grazed area and amounts of C removed are needed, we develop total and downscaled estimates of global rangeland grazing intensity. We derive available rangeland area and distribution by harmonizing MODIS land cover product (i.e., MCD12Q1 V006), accounting for recent cropland area extent and cropland use intensities. Multimodel ensemble estimates of NPP on the resulting rangeland areas from the MODIS NPP product and the results of the The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are then combined with annual grazing intake requirements at the state/province or national level, and downscaled by placing grazing on available rangeland closest to cropland gridcells until intake requirements are met. Grazing intensity was kept <= 15% where possible. We use the most up-to-date estimates of livestock intake, which, along with our recent estimates of cropland area and multicropping, is unique.
2. Exploring the Use of DSCOVR/EPIC Satellite Observations to Study Seasonal Dynamics in Earth’s Vegetation Phenology
Authors: Maridee Weber 1, Dalei Hao1, Ghassem Asrar1, Min Chen 1, Yuyu Zhou 2 and Xuecao Li 2
Abstract: Seasonal dynamics of vegetation play a pivotal role in ecosystem productivity and global carbon exchange. Current remote sensing of the Earth from space for the purpose of observing vegetation phenology sensitive to climate and other disturbances, lacks high temporal resolution, leaving gaps in data that is important for environmental, health, and agricultural purposes. High temporal resolution satellite observations have the potential to fill this gap by frequently collecting observations on a global scale, making it easier to study change over time. This study explores the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR) satellite, which captures images of the entire sunlit face of the Earth at a temporal resolution of once every 1-2 hours, to observe vegetation phenology cycles at sites and regions (e.g. North America) worldwide. We set out to assess the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Landsat and PhenoCam ground-based observations across different plant functional types, including agriculture, deciduous broadleaf, evergreen broadleaf, evergreen needleleaf, grassland, mixed forest, shrub, and wetland. Our preliminary results indicate that EPIC, which has significantly improved temporal resolution, can detect and characterize seasonal vegetation changes across different plant functional types more accurately than MODIS and Landsat, especially at the relatively homogeneous sites. Our results also provide new insights about the complementary features and benefits of the four datasets, which is valuable for improving our understanding of the complex response of vegetation to global climate variability and other disturbances.
3. Title: Inversion of aquaculture water quality elements based on UAVs-WSN spectral images
Authors: Linhui Wang1, Huihui Wang2, Houbing Song3, Xuejun Yue1,*, Yongxin Liu3, Jian Wang3, Kangjie Lin1
1 College of Electronic Engineering, South China Agricultural University, Guangzhou 510642 China
2 Department of Engineering at Jacksonville University, Jacksonville, FL, 32211, USA
3 Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Abstract: Water quality is a key factor which is closely related to the economic benefits of aquaculture as well as the quality of aquatic products. In view of the problems of detecting water quality with satellite remote sensing, such as complex process, low efficiency of data acquisition and susceptible to interference, as well as weak generalization ability and low accuracy of traditional inversion model, this paper combined the Internet of things with the technology unmanned aerial vehicles to design a set of water quality parameter acquisition methods which are suitable for freshwater aquaculture, and then put forward a dynamic network clipping-depth learning model based on multi-feature fusion (DNS - DNNs), so as to predict the distribution of water quality parameters. Based on GPRS, the ground WSN network built a real water quality parameter acquisition system on the ground, and combining with the UAVs platform, a spectral imager was equipped to obtain spectral remote sensing data. The correlation analysis was carried out to the measured DO and TUB and the water spectral reflectance after normalization and first-order differential treatment, so as to obtain the sensitive bands of DO and TUB, and then the characteristic spectral information was enriched through various spectral parameter combinations. Gray co-occurrence matrix GLCM and CNN were used to extract texture features of feature spectral images. Different feature combinations such as feature spectrum, GLCM texture and CNN texture are taken as input items to import DNS-DNNs, so as to explore the best feature fusion parameters which are suitable for DO and TUB. The results indicated that the characteristic spectrum + CNN texture fusion features had the best prediction effect on DO, and characteristic spectrum + GLCM texture feature + CNN texture feature had the best prediction effect on TUB. Compared with modeling only using characteristic spectrum, on the basis of integrating a variety of data information, including characteristic spectrum, image texture features and so on, DNS - DNNs’ stability was enhanced and generalization ability was also promoted to some extent. Compared with a variety of mainstream models, DNS-DNNs model had a better performance than other models in the inversion prediction of dissolved oxygen and turbidity, and it fitted well with the measured values, for example,