Special Issue "Global Biospheric Monitoring with Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 26 June 2020.

Special Issue Editors

Dr. Alicia Palacios-Orueta
E-Mail Website
Guest Editor
Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, Madrid, Spain
Tel. 34 91 336 70 80
Interests: Indexes development; time series analysis; agricultural and forest monitoring; fire risk
Dr. Xiaolu Tang
E-Mail Website
Guest Editor
1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, China
2. State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu 610059, China
Interests: soil/vegetation carbon cycling of terrestrial ecosystems; remote sensing of vegetation; model-data integration—mainly machine learning approaches

Special Issue Information

Dear Colleagues,

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
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 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.

Keywords

  • Climate change
  • Land use and land cover dynamics
  • Spectral indices
  • Time series analysis
  • Vegetation anomalies and trends
  • Vegetation modeling
  • Biogeochemical cycles
  • Energy balance

Published Papers (3 papers)

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Research

Open AccessArticle
Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations
Remote Sens. 2020, 12(4), 636; https://doi.org/10.3390/rs12040636 - 14 Feb 2020
Abstract
Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is [...] Read more.
Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is technically challenging. Thus, most available plantation maps rely on visual inspection of imagery, and many of them are limited to small areas for specific years. Here, we present an automated approach, which we call Plantation Analysis by Learning from Multiple Classes (PALM), for mapping plantations on an annual basis using satellite remote sensing data. Due to the heterogeneity of land cover classes, PALM utilizes ensemble learning to simultaneously incorporate training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. We implement the proposed automated approach using MODIS data in Sumatra and Indonesian Borneo (Kalimantan). To validate the classification, we compare plantations detected using our approach with existing datasets developed through visual interpretation. Based on random sampling and comparison with high-resolution images, the user’s accuracy and producer’s accuracy of our generated map are around 85% and 80% in our study region. Full article
(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)
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Open AccessArticle
A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America
Remote Sens. 2020, 12(2), 258; https://doi.org/10.3390/rs12020258 - 11 Jan 2020
Abstract
Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, [...] Read more.
Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP. Full article
(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)
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Open AccessArticle
Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series
Remote Sens. 2019, 11(21), 2497; https://doi.org/10.3390/rs11212497 - 25 Oct 2019
Abstract
Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely [...] Read more.
Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change. Full article
(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)
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Planned Papers

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,

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