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Global Biospheric Monitoring with Remote Sensing

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 51312

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Dr. Alicia Palacios-Orueta

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Guest Editor

Departamento de Sistemas y Recursos Naturales, ETSIMFMN, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Interests: indexes development; time series analysis; agricultural and forest monitoring; fire risk
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaolu Tang

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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 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. 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 2700 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 (8 papers)

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Research

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20 pages, 44109 KiB

Open AccessArticle

Medium- (MR) and Very-High-Resolution (VHR) Image Integration through Collect Earth for Monitoring Forests and Land-Use Changes: Global Forest Survey (GFS) in the Temperate FAO Ecozone in Europe (2000–2015)

by Luis Gonzaga García-Montero, Cristina Pascual, Susana Martín-Fernández, Alfonso Sanchez-Paus Díaz, Chiara Patriarca, Pablo Martín-Ortega and Danilo Mollicone

Remote Sens. 2021, 13(21), 4344; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214344 - 28 Oct 2021

Cited by 4 | Viewed by 1998

Abstract

Monitoring of land use, land-use changes, and forestry (LULUCF) plays a crucial role in biodiversity and global environmental challenges. In 2015, the Food and Agriculture Organization of the United Nations (FAO) launched the Global Forest Survey (GFS) integrating medium- (MR) and very-high-resolution (VHR) images through the FAO’s Collect Earth platform. More than 11,150 plots were inventoried in the Temperate FAO ecozone in Europe to monitor LULUCF from 2000 to 2015. As a result, 2.19% (VHR) to 2.77% (MR/VHR) of the study area underwent LULUCF, including a 0.37% (VHR) to 0.43% (MR/VHR) net increase in forest lands. Collect Earth and VHR images have also (i) allowed for shaping a preliminary structure of the land-use network, showing that cropland was the land type that changed most and that cropland and grassland were the more frequent land uses that generated new forest land, (ii) shown that, in 2015, mixed and monospecific forests represented 44.3% and 46.5% of the forest land, respectively, unlike other forest sources, and (iii) shown that 14.9% of the area had been affected by disturbances, particularly wood harvesting (67.47% of the disturbed forests). According to other authors, the area showed a strong correlation between canopy mortality and reported wood removals due to the transition from past clear-cut systems to “close-to-nature” silviculture. Full article

(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)

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23 pages, 14052 KiB

Open AccessArticle

Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010

by Julie Wolf, Min Chen and Ghassem R. Asrar

Remote Sens. 2021, 13(17), 3430; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173430 - 29 Aug 2021

Cited by 7 | Viewed by 2625

Abstract

Livestock grazing occupies ca. 25% of global ice-free land, removing large quantities of carbon (C) from global rangelands (here, including grass- and shrublands). The proportion of total livestock intake that is supplied by grazing (GP) is estimated at >50%, larger than the proportion from crop- and byproduct-derived fodders. Both rangeland productivity and its consumption through grazing are difficult to quantify, as is grazing intensity (GI), the proportion of annual aboveground net primary productivity (ANPP) removed from rangelands by grazing livestock. We develop national or sub-national level estimates of GI and GP for 2000–2010, using remote sensing products, inventory data, and model simulations, and accounting for recent changes in livestock intake, fodder losses and waste, and national cropland use intensities. Over the 11 study years, multi-model average global rangeland ANPP varied between the values of 13.0 Pg C in 2002 and 13.96 Pg C in 2000. The global requirement for grazing intake increased monotonically by 18%, from 1.54 in 2000 to 1.82 Pg C in 2010. Although total global rangeland ANPP is roughly an order of magnitude larger than grazing demand, much of this total ANPP is unavailable for grazing, and national or sub-national deficits between intake requirements and available rangeland ANPP occurred in each year, totaling 36.6 Tg C (2.4% of total grazing intake requirement) in 2000, and an unprecedented 77.8 Tg C (4.3% of global grazing intake requirement) in 2010. After accounting for these deficits, global average GI ranged from 10.7% in 2000 to 12.6% in 2009 and 2010. The annually increasing grazing deficits suggest that rangelands are under significant pressure to accommodate rising grazing demand. Greater focus on observing, understanding, and managing the role of rangelands in feeding livestock, providing ecosystem services, and as part of the global C cycle, is warranted. Full article

(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)

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33 pages, 16780 KiB

Open AccessArticle

Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

by Daniel Cunningham, Paul Cunningham and Matthew E. Fagan

Remote Sens. 2020, 12(19), 3226; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193226 - 3 Oct 2020

Cited by 2 | Viewed by 6061

Abstract

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R² rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production. Full article

(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)

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20 pages, 13166 KiB

Open AccessArticle

Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology

by Maridee Weber, Dalei Hao, Ghassem R. Asrar, Yuyu Zhou, Xuecao Li and Min Chen

Remote Sens. 2020, 12(15), 2384; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152384 - 24 Jul 2020

Cited by 12 | Viewed by 3999

Abstract

Vegetation phenology plays a pivotal role in regulating several ecological processes and has profound impacts on global carbon exchange. Large-scale vegetation phenology monitoring mostly relies on Low-Earth-Orbit satellite observations with low temporal resolutions, leaving gaps in data that are important for monitoring seasonal vegetation phenology. 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 explored the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard the 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 h, to observe vegetation phenology cycles in North America. We assessed the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Enhanced Thematic Mapper (ETM+) onboard Landsat 7, and PhenoCam ground-based observations across six different plant functional types. Our results indicated that EPIC could capture and characterize seasonal changes of vegetation across different plant functional types and is particularly consistent in the estimated growing season length. Our results also provided new insights into 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 and the impact of phenology changes on ecosystem productivity and global carbon exchange. Full article

(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)

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23 pages, 2722 KiB

Open AccessArticle

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

by Xiaowei Jia, Ankush Khandelwal, Kimberly M. Carlson, James S. Gerber, Paul C. West, Leah H. Samberg and Vipin Kumar

Remote Sens. 2020, 12(4), 636; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040636 - 14 Feb 2020

Cited by 4 | Viewed by 4407

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 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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21 pages, 2084 KiB

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

by Ruonan Qiu, Ge Han, Xin Ma, Hao Xu, Tianqi Shi and Miao Zhang

Remote Sens. 2020, 12(2), 258; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020258 - 11 Jan 2020

Cited by 53 | Viewed by 6892

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, 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 (GPP_EC). 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 GPP_EC 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 GPP_EC 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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21 pages, 3941 KiB

Open AccessArticle

Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

by Laura Recuero, Javier Litago, Jorge E. Pinzón, Margarita Huesca, Maria C. Moyano and Alicia Palacios-Orueta

Remote Sens. 2019, 11(21), 2497; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11212497 - 25 Oct 2019

Cited by 13 | Viewed by 4147

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 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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Review

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31 pages, 16601 KiB

Open AccessReview

Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review

by Sung Wook Paek, Sivagaminathan Balasubramanian, Sangtae Kim and Olivier de Weck

Remote Sens. 2020, 12(16), 2546; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162546 - 7 Aug 2020

Cited by 42 | Viewed by 20209

Abstract

Space-based radar sensors have transformed Earth observation since their first use by Seasat in 1978. Radar instruments are less affected by daylight or weather conditions than optical counterparts, suitable for continually monitoring the global biosphere. The current trends in synthetic aperture radar (SAR) platform design are distinct from traditional approaches in that miniaturized satellites carrying SAR are launched in multiples to form a SAR constellation. A systems engineering perspective is presented in this paper to track the transitioning of space-based SAR platforms from large satellites to small satellites. Technological advances therein are analyzed in terms of subsystem components, standalone satellites, and satellite constellations. The availability of commercial satellite constellations, ground stations, and launch services together enable real-time SAR observations with unprecedented details, which will help reveal the global biomass and their changes owing to anthropogenic drivers. The possible roles of small satellites in global biospheric monitoring and the subsequent research areas are also discussed. Full article

(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)

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