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Earth Observation for Biodiversity and Ecosystem Services: Remote Sensing Data Handling and Analysis

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

Deadline for manuscript submissions: closed (30 June 2017) | Viewed by 38148

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Special Issue Editors

Prof. Dr. Gherardo Chirici

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Guest Editor

GeoLAB—Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura 13, 50145 Firenze, Italy
Interests: application of geomatics to forestry; remote sensing; forest inventories and monitoring; sustainable forest management; land planning; landscape ecology; biodiversity; forest fires and climate change; bio-geo-chemical models; decision support systems; forest ecology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Duccio Rocchini

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Guest Editor

Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy
Interests: biodiversity estimate; computational ecology; ecological informatics; remote sensing; spatial ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is an increasing awareness that several approaches and indicators should be considered for evaluating the multiple dimensions of biodiversity. For example, the EU Biodiversity Strategy aims to halt the loss of biodiversity and ecosystem services in Europe, contributing to the UNECE goal of stopping future global biodiversity loss. To meet such an ambitious goal, consistent and operational monitoring systems to acquire the necessary information at a variety of spatial and temporal resolutions are urgently needed. Remote sensing technologies are essential for studying biodiversity change over space and time, thus making it a crucial tool for supporting proper management and conservation of ecosystems.

In this framework, we are glad to edit this special issue on “Earth Observation for Biodiversity and Ecosystem Services: Remote Sensing Data Handling and Analysis".

The special issue is dedicated to presenting robust remote sensing data gathering and statistically sound analysis procedures for deriving meaningful ecological proxies that can be related to biodiversity and ecosystem services at different spatial and temporal scales. Contributions possibly spanning long time periods, and/or large geographical extents are especially welcome. Studies should rely on the integration between earth observation imagery and data collected in the field. Studies based on remote sensing active or passive sensors from satellite, aerial or drone platforms are welcome.

Prof. Dr. Gherardo Chirici
Dr. Duccio Rocchini
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

Algorithms
Biodiversity proxies by remote sensing
Ecosystem services
Temporal trends
Spatial estimations
Spatial modeling
Forest ecology
Forest inventory
Free and open source software
Global change
Plant community ecology
Productivity proxies
Remote sensing data analysis
Remote sensing data handling
Spatial heterogeneity
Species distribution modelling

Published Papers (6 papers)

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Research

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38 pages, 6763 KiB

Open AccessFeature PaperArticle

Challenges in Complementing Data from Ground-Based Sensors with Satellite-Derived Products to Measure Ecological Changes in Relation to Climate—Lessons from Temperate Wetland-Upland Landscapes

by Alisa L. Gallant, Walt Sadinski, Jesslyn F. Brown, Gabriel B. Senay and Mark F. Roth

Sensors 2018, 18(3), 880; https://doi.org/10.3390/s18030880 - 16 Mar 2018

Cited by 7 | Viewed by 4523

Abstract

Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relations between temperature and precipitation and a set of variables measured on the ground at individual wetlands and another set measured via satellite sensors within surrounding 4 km² landscape blocks. At the block scale, we used evapotranspiration and vegetation greenness as remotely sensed proxies for water availability and to estimate seasonal photosynthetic activity. We used sensors on the ground to coincidentally measure surface-water availability and amphibian calling activity at individual wetlands within blocks. Responses of landscape blocks generally paralleled changes in conditions measured on the ground, but the latter were more dynamic, and changes in ecological conditions on the ground that were critical for biota were not always apparent in measurements of related parameters in blocks. Here, we evaluate the effectiveness of decisions and assumptions we made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines. Full article

(This article belongs to the Special Issue Earth Observation for Biodiversity and Ecosystem Services: Remote Sensing Data Handling and Analysis)

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3286 KiB

Open AccessArticle

Extraction of Rice Phenological Differences under Heavy Metal Stress Using EVI Time-Series from HJ-1A/B Data

by Shuyuan Liu, Xiangnan Liu, Meiling Liu, Ling Wu, Chao Ding and Zhi Huang

Sensors 2017, 17(6), 1243; https://doi.org/10.3390/s17061243 - 30 May 2017

Cited by 19 | Viewed by 5101

Abstract

An effective method to monitor heavy metal stress in crops is of critical importance to assure agricultural production and food security. Phenology, as a sensitive indicator of environmental change, can respond to heavy metal stress in crops and remote sensing is an effective method to detect plant phenological changes. This study focused on identifying the rice phenological differences under varied heavy metal stress using EVI (enhanced vegetation index) time-series, which was obtained from HJ-1A/B CCD images and fitted with asymmetric Gaussian model functions. We extracted three phenological periods using first derivative analysis: the tillering period, heading period, and maturation period; and constructed two kinds of metrics with phenological characteristics: date-intervals and time-integrated EVI, to explore the rice phenological differences under mild and severe stress levels. Results indicated that under severe stress the values of the metrics for presenting rice phenological differences in the experimental areas of heavy metal stress were smaller than the ones under mild stress. This finding represents a new method for monitoring heavy metal contamination through rice phenology. Full article

(This article belongs to the Special Issue Earth Observation for Biodiversity and Ecosystem Services: Remote Sensing Data Handling and Analysis)

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1757 KiB

Open AccessArticle

Modeling the Footprint and Equivalent Radiance Transfer Path Length for Tower-Based Hemispherical Observations of Chlorophyll Fluorescence

by Xinjie Liu, Liangyun Liu, Jiaochan Hu and Shanshan Du

Sensors 2017, 17(5), 1131; https://doi.org/10.3390/s17051131 - 16 May 2017

Cited by 29 | Viewed by 5157

Abstract

The measurement of solar-induced chlorophyll fluorescence (SIF) is a new tool for estimating gross primary production (GPP). Continuous tower-based spectral observations together with flux measurements are an efficient way of linking the SIF to the GPP. Compared to conical observations, hemispherical observations made with cosine-corrected foreoptic have a much larger field of view and can better match the footprint of the tower-based flux measurements. However, estimating the equivalent radiation transfer path length (ERTPL) for hemispherical observations is more complex than for conical observations and this is a key problem that needs to be addressed before accurate retrieval of SIF can be made. In this paper, we first modeled the footprint of hemispherical spectral measurements and found that, under convective conditions with light winds, 90% of the total radiation came from an FOV of width 72°, which in turn covered 75.68% of the source area of the flux measurements. In contrast, conical spectral observations covered only 1.93% of the flux footprint. Secondly, using theoretical considerations, we modeled the ERTPL of the hemispherical spectral observations made with cosine-corrected foreoptic and found that the ERTPL was approximately equal to twice the sensor height above the canopy. Finally, the modeled ERTPL was evaluated using a simulated dataset. The ERTPL calculated using the simulated data was about 1.89 times the sensor’s height above the target surface, which was quite close to the results for the modeled ERTPL. Furthermore, the SIF retrieved from atmospherically corrected spectra using the modeled ERTPL fitted well with the reference values, giving a relative root mean square error of 18.22%. These results show that the modeled ERTPL was reasonable and that this method is applicable to tower-based hemispherical observations of SIF. Full article

(This article belongs to the Special Issue Earth Observation for Biodiversity and Ecosystem Services: Remote Sensing Data Handling and Analysis)

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4138 KiB

Open AccessArticle

Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering

by Quanhua Zhao, Xiaoli Li and Yu Li

Sensors 2017, 17(5), 1114; https://doi.org/10.3390/s17051114 - 12 May 2017

Cited by 9 | Viewed by 4138

Abstract

This paper presents a novel multilook SAR image segmentation algorithm with an unknown number of clusters. Firstly, the marginal probability distribution for a given SAR image is defined by a Gamma mixture model (GaMM), in which the number of components corresponds to the number of homogeneous regions needed to segment and the spatial relationship among neighboring pixels is characterized by a Markov Random Field (MRF) defined by the weighting coefficients of components in GaMM. During the algorithm iteration procedure, the number of clusters is gradually reduced by merging two components until they are equal to one. For each fixed number of clusters, the parameters of GaMM are estimated and the optimal segmentation result corresponding to the number is obtained by maximizing the marginal probability. Finally, the number of clusters with minimum global energy defined as the negative logarithm of marginal probability is indicated as the expected number of clusters with the homogeneous regions needed to be segmented, and the corresponding segmentation result is considered as the final optimal one. The experimental results from the proposed and comparing algorithms for simulated and real multilook SAR images show that the proposed algorithm can find the real number of clusters and obtain more accurate segmentation results simultaneously. Full article

(This article belongs to the Special Issue Earth Observation for Biodiversity and Ecosystem Services: Remote Sensing Data Handling and Analysis)

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2954 KiB

Open AccessArticle

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

by Wei Jin, Fei Gong, Xingbin Zeng and Randi Fu

Sensors 2016, 16(12), 2153; https://doi.org/10.3390/s16122153 - 16 Dec 2016

Cited by 16 | Viewed by 5288

Abstract

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. Full article

(This article belongs to the Special Issue Earth Observation for Biodiversity and Ecosystem Services: Remote Sensing Data Handling and Analysis)

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Review

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1743 KiB

Open AccessReview

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

by Alfonso Calera, Isidro Campos, Anna Osann, Guido D’Urso and Massimo Menenti

Sensors 2017, 17(5), 1104; https://doi.org/10.3390/s17051104 - 11 May 2017

Cited by 171 | Viewed by 12802

Abstract

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools. Full article

(This article belongs to the Special Issue Earth Observation for Biodiversity and Ecosystem Services: Remote Sensing Data Handling and Analysis)

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