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EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 43706

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Special Issue Editors

Dr. Andrea Taramelli

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Guest Editor

1. Associate Professor, IUSS—Istituto Universitario di Studi Superiori di Pavia, Palazzo del Broletto - Piazza della Vittoria n.15, 27100 Pavia, Italy
2. Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy
Interests: geomorphology; geophysics and surface change; remote sensing; natural hazard; GIS

Dr. Christopher Small

E-Mail Website
Guest Editor

Lamont-Doherty Earth Observatory Marine Geology and Geophysics, Columbia University, New York, NY 10027, USA
Interests: geophysics; land surface processes; remote sensing; population and environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous mapping and monitoring of the environment is of paramount importance for adopting proper protection, for conservation or recovery policies, for assessing their effectiveness, for prioritizing managing activities, and for defining spatial planning measures.

The use of EO data for mapping and monitoring the environment (natural, semi-natural, agricultural, and anthropic) based on automatic or semi-automatic procedures enables a more rapid generation of mapping products compared to those that are field-based, allowing one to reach hardly accessible areas (e.g., wetlands) and ensuring a wide spatial and temporal product coverage.

The Copernicus Programme, along with the fleet of the Sentinels (optical and SAR sensors), have made available to different users—private, institutional, scientific—a growing amount of free data covering the whole globe at several spatial resolutions and with a high revisit time. The Sentinels constellation therefore represents a great opportunity, enabling the improvement of land and sea monitoring and paving the way for the generation and delivery of new EO-derived products and services both experimental and consolidated in the domain of agriculture, food security, raw materials, soils, biodiversity, environmental degradation and hazards, inland and coastal waters, and forestry.

All this means a new chance for changing the environmental geoinformation domain by developing new or adapting already available algorithms and workflow (e.g., data fusion a/o integration), creating new products and tools (e.g., added value information products, automatic or semi-automatic tools), and creating downstream applications and services in favor of both public and private sector stakeholders as providers or users.

This Special Issue aims to present and showcase EO-based solutions for environmental mapping (status, changes, and pressures) with the goal of establishing new regional and national downstreaming operational services and supporting users to fulfill their information needs, especially those related to legal obligations. Hence, user requirements (required parameters and products technical specification) are also of high interest.

The papers of this Special Issue will aim to present the state of the research of proposed products and procedures, with practical cases, having as a final goal their implementation on a national scale.

These articles shall address, but are not limited to, the following:

methods for defining user requirements: analysis of domains, applications, spatial, spectral and temporal sampling, and radiometric requirements
multi-source data integration or fusion methods (e.g., active/passive remote sensing, airborne, in situ, modeling, and socio-economic);
mapping products: showing the current status, changes occurred, or future predictions of the environment or associated added value information
mapping products analysis: spatial and temporal pattern analysis, landscape, and class metrics
the development of new algorithms and workflows or adaptation of those already existing according to Sentinels characteristics, especially in the perspective of national downstream operational services
procedures: automatic or semi-automatic procedures
innovative and dedicated EO tools (e.g., platforms, tools, data cubes, and coding).

Where relevant, the articles should tackle the aspects of accuracy, validation, standardisation, limitations, and transferability for an easy and seamless integration in national processes and systems.

Dr. Andrea Taramelli
Dr. Christopher Small
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. 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

Earth Observation
mapping product
user-driven approach
operational services
spatial and temporal patterns

Published Papers (9 papers)

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Research

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17 pages, 6155 KiB

Open AccessArticle

Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images

by Daniela Smiraglia, Federico Filipponi, Stefania Mandrone, Antonella Tornato and Andrea Taramelli

Remote Sens. 2020, 12(11), 1862; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111862 - 08 Jun 2020

Cited by 30 | Viewed by 5058

Abstract

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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14 pages, 5041 KiB

Open AccessArticle

by Marcio A. S. Santos, Eduardo D. Assad, Angelo C. Gurgel and Nizam Omar

Remote Sens. 2020, 12(11), 1791; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111791 - 02 Jun 2020

Cited by 1 | Viewed by 3370

Abstract

Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country’s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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Graphical abstract

31 pages, 5009 KiB

Open AccessArticle

An Interaction Methodology to Collect and Assess User-Driven Requirements to Define Potential Opportunities of Future Hyperspectral Imaging Sentinel Mission

by Andrea Taramelli, Antonella Tornato, Maria Lucia Magliozzi, Stefano Mariani, Emiliana Valentini, Massimo Zavagli, Mario Costantini, Jens Nieke, Jennifer Adams and Michael Rast

Remote Sens. 2020, 12(8), 1286; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081286 - 18 Apr 2020

Cited by 27 | Viewed by 5330

Abstract

Evolution in the Copernicus Space Component is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce existing services. In this context, the European Commission is intending to evaluate the overall potential utility of a complementary Copernicus hyperspectral mission to be added to the Copernicus Sentinels fleet. Hyperspectral imaging is a powerful remote sensing technology that, allowing the characterization and quantification of Earth surface materials, has the potential to deliver significant enhancements in quantitative value-added products. This study aims to illustrate the interaction methodology that was set up to collect and assess user-driven requirements in different thematic areas to demonstrate the potential benefit of a future Copernicus hyperspectral mission. Therefore, an ad hoc interaction matrix was circulated among several user communities to gather preferences about hyperspectral-based products and services. The results show how the involvement of several user communities strengthens the identification of these user requirements. Moreover, the requirement evaluation is used to identify potential opportunities of hyperspectral imaging in addressing operational needs associated with policy obligations at European, national, and local levels. The frequency distribution of spectral range classes and spatial and temporal resolutions are also derived from the preference expressed by the user communities in each thematic area investigated. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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23 pages, 7307 KiB

Open AccessArticle

Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition

by Emiliana Valentini, Andrea Taramelli, Sergio Cappucci, Federico Filipponi and Alessandra Nguyen Xuan

Remote Sens. 2020, 12(8), 1229; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081229 - 12 Apr 2020

Cited by 31 | Viewed by 6626

Abstract

Coastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an airborne hyperspectral and light detection and ranging (LiDAR) remote sensing dataset. A correlation model is applied to describe the continuum of dune cover typologies, determine the class metrics from landscape ecology and the morphology parameters, and extract the relationship intensity among them. As a main result, the mixture of different vegetation types such as herbaceous, shrubs, and trees classes shows to be a key element for the sediment distribution pattern and a proxy for dune sediment retention capacity, and the anthropic fingerprints can play an even major role influencing both ecological and morphological features. The novelty of the approach is mostly based on the synergistic use of LiDAR with hyperspectral that allowed (i) the benefit from already existing processing methods to simplify the way to obtain thematic maps and coastal metrics and (ii) an improved detection of natural and anthropic landscape. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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22 pages, 5469 KiB

Open AccessArticle

Nearshore Sandbar Classification of Sabaudia (Italy) with LiDAR Data: The FHyL Approach

by Andrea Taramelli, Sergio Cappucci, Emiliana Valentini, Lorenzo Rossi and Iolanda Lisi

Remote Sens. 2020, 12(7), 1053; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071053 - 25 Mar 2020

Cited by 14 | Viewed by 3974

Abstract

An application of the FHyL (field spectral libraries, airborne hyperspectral images and topographic LiDAR) method is presented. It is aimed to map and classify bedforms in submerged beach systems and has been applied to Sabaudia coast (Tirrenyan Sea, Central Italy). The FHyl method allows the integration of geomorphological observations into detailed maps by the multisensory data fusion process from hyperspectral, LiDAR, and in-situ radiometric data. The analysis of the sandy beach classification provides an identification of the variable bedforms by using LiDAR bathymetric Digital Surface Model (DSM) and Bathymetric Position Index (BPI) along the coastal stretch. The nearshore sand bars classification and analysis of the bed form parameters (e.g., depth, slope and convexity/concavity properties) provide excellent results in very shallow waters zones. Thanks to well-established LiDAR and spectroscopic techniques developed under the FHyL approach, remote sensing has the potential to deliver significant quantitative products in coastal areas. The developed method has become the standard for the systematic definition of the operational coastal airborne dataset that must be provided by coastal operational services as input to national downstream services. The methodology is also driving the harmonization procedure of coastal morphological dataset definition at the national scale and results have been used by the authorities to adopt a novel beach management technique. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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26 pages, 6612 KiB

Open AccessArticle

Assessment of Green Infrastructure in Riparian Zones Using Copernicus Programme

by Laura Piedelobo, Andrea Taramelli, Emma Schiavon, Emiliana Valentini, José-Luis Molina, Alessandra Nguyen Xuan and Diego González-Aguilera

Remote Sens. 2019, 11(24), 2967; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242967 - 11 Dec 2019

Cited by 23 | Viewed by 4337

Abstract

This article presents an approach to identify Green Infrastructure (GI), its benefits and condition. This information enables environmental agencies to prioritise conservation, management and restoration strategies accordingly. The study focuses on riparian areas due to their potential to supply Ecosystem Services (ES), such as water quality, biodiversity, soil protection and flood or drought risk reduction. Natural Water Retention Measures (NWRM) related to agriculture and forestry are the type of GI considered specifically within these riparian areas. The approach is based on ES condition indicators, defined by the European Environment Agency (EEA) to support the policy targets of the 2020 Biodiversity Strategy. Indicators that can be assessed through remote sensing techniques are used, namely: capacity to provide ecosystem services, proximity to protected areas, greening response and water stress. Specifically, the approach uses and evaluates the potential of freely available products from the Copernicus Land Monitoring Service (CLMS) to monitor GI. Moreover, vegetation and water indices are calculated using data from the Sentinel-2 MSI Level-2A scenes and integrated in the analysis. The approach has been tested in the Italian Po river basin in 2018. Firstly, agriculture and forest NWRM were identified in the riparian areas of the river network. Secondly, the Riparian Zones products from the CLMS local component and the satellite-based indices were linked to the aforementioned ES condition indicators. This led to the development of a pixel-based model that evaluates the identified GI according to: (i) its disposition to provide riparian regulative ES and (ii) its condition in the analysed year. Finally, the model was used to prioritise GI for conservation or restoration initiatives, based on its potential to deliver ES and current condition. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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21 pages, 3454 KiB

Open AccessArticle

The Potential of Open Geodata for Automated Large-Scale Land Use and Land Cover Classification

by Patrick Leinenkugel, Ramona Deck, Juliane Huth, Marco Ottinger and Benjamin Mack

Remote Sens. 2019, 11(19), 2249; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11192249 - 27 Sep 2019

Cited by 34 | Viewed by 4297

Abstract

This study examines the potential of open geodata sets and multitemporal Landsat satellite data as the basis for the automated generation of land use and land cover (LU/LC) information at large scales. In total, six openly available pan-European geodata sets, i.e., CORINE, Natura 2000, Riparian Zones, Urban Atlas, OpenStreetMap, and LUCAS in combination with about 1500 Landsat-7/8 scenes were used to generate land use and land cover information for three large-scale focus regions in Europe using the TimeTools processing framework. This fully automated preprocessing chain integrates data acquisition, radiometric, atmospheric and topographic correction, spectral–temporal feature extraction, as well as supervised classification based on a random forest classifier. In addition to the evaluation of the six different geodata sets and their combinations for automated training data generation, aspects such as spatial sampling strategies, inter and intraclass homogeneity of training data, as well as the effects of additional features, such as topography and texture metrics are evaluated. In particular, the CORINE data set showed, with up to 70% overall accuracy, high potential as a source for deriving dominant LU/LC information with minimal manual effort. The intraclass homogeneity within the training data set was of central relevance for improving the quality of the results. The high potential of the proposed approach was corroborated through a comparison with two similar LU/LC data sets, i.e., GlobeLand30 and the Copernicus High Resolution Layers. While similar accuracy levels could be observed for the latter, for the former, accuracy was considerable lower by about 12–24%. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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18 pages, 4771 KiB

Open AccessArticle

Pre-Constrained Machine Learning Method for Multi-Year Mapping of Three Major Crops in a Large Irrigation District

by Yeqiang Wen, Songhao Shang and Khalil Ur Rahman

Remote Sens. 2019, 11(3), 242; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030242 - 24 Jan 2019

Cited by 20 | Viewed by 3783

Abstract

The accurate mapping of crops can provide effective information for regional agricultural management, which is helpful to improve crop production efficiency. Recently, remote sensing data offers a comprehensive approach to achieve crop identification on a regional scale. However, the classification methods for multi-year mapping needs further study in regions with a complex planting structure, due to the mixed pixels at a spatial distribution and the high error in different years at a temporal scale. The objective of this study is to map the multi-year spatial distribution of three main crops (maize, sunflower, and wheat) in the Hetao irrigation district of China for the period 2012–2016 based on a pre-constrained classification method. The pre-constrained method integrates a parameterized phenology-based vegetation indexes classifier and two non-parametric machine learning algorithms—support vector machine (SVM) and random forest (RF). Results indicated that the performance of the pre-constrained classification method was excellent in the multi-year mapping of major crops in the study area, with absolute relative errors mainly less than 14% in the whole irrigation district and less than 20% in the five counties. The corresponding overall accuracy was 87.9%, and the Kappa coefficient was 0.80. Mapping results showed that maize is mainly distributed in Hangjinhouqi, southern Linhe, northern Wuyuan, and eastern Wulateqianqi, while wheat is relatively less and scatteredly distributed in Hangjinhouqi and Wuyuan. Moreover, the sunflower planting area increased significantly and expanded spatially from Wuyuan and western Wulateqianqi to northern Hangjinhouqi and Linhe from 2012 to 2016. In addition, the phenology-based vegetation indexes classifier was found to be effective in improving the classification accuracy based on the contribution analysis. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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Review

Jump to: Research

27 pages, 8503 KiB

Open AccessReview

Monitoring Green Infrastructure for Natural Water Retention Using Copernicus Global Land Products

by Andrea Taramelli, Michele Lissoni, Laura Piedelobo, Emma Schiavon, Emiliana Valentini, Alessandra Nguyen Xuan and Diego González-Aguilera

Remote Sens. 2019, 11(13), 1583; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11131583 - 03 Jul 2019

Cited by 19 | Viewed by 6220

Abstract

Nature-based solutions are increasingly relevant tools for spatial and environmental planning, climate change adaptation (CCA), and disaster risk reduction (DRR). For this reason, a wide range of institutions, governments, and financial bodies are currently promoting the use of green infrastructure (GI) as an alternative or a complement to traditional grey infrastructure. A considerable amount of research already certifies the benefits and multi-functionality of GI: natural water retention measures (NWRMs), as GIs related specifically to the water sector are also known, are, for instance, a key instrument for the prevention and mitigation of extreme phenomena, such as floods and droughts. However, there are persisting difficulties in locating and identifying GI and one of the most promising solutions to this issue, the use of satellite-based data products, is hampered by a lack of well-grounded knowledge, experiences, and tools. To bridge this gap, we performed a review of the Copernicus Global Land Service (CGLS) products, which consist of freely-available bio-geophysical indices covering the globe at mid-to-low spatial resolutions. Specifically, we focused on vegetation and energy indices, examining previous research works that made use of them and evaluating their current quality, aiming to define their potential for studying GI and especially NWRMs related to agriculture, forest, and hydro-morphology. NWRM benefits are also considered in the analysis, namely: (i) NWRM biophysical impacts (BPs), (ii) ecosystem services delivered by NWRMs (ESs), and (iii) policy objectives (POs) expressed by European Directives that NWRMs can help to achieve. The results of this study are meant to assist GI users in employing CGLS products and ease their decision-making process. Based on previous research experiences and the quality of the currently available versions, this analysis provides useful tools to identify which indices can be used to study several types of NWRMs, assess their benefits, and prioritize the most suitable ones. Full article

(This article belongs to the Special Issue EO Based Environmental Mapping Services: Matching Agriculture, Urban Areas and Protected Areas Information Needs)

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