E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

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

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: 31 December 2019.

Special Issue Editors

Guest Editor
Dr. Andrea Taramelli

Associate Professor, IUSS—Istituto Universitario di Studi Superiori di Pavia, Palazzo del Broletto - Piazza della Vittoria n.15, 27100 Pavia, Italy
Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy
Website | E-Mail
Phone: +39 03 8237 5847 (IUSS) or +39 06 5007 4635 (ISPRA
Interests: geomorphology, geophysics and surface change, remote sensing, natural hazard, GIS
Guest Editor
Dr. Christopher Small

Adjunct Professor, Columbia University, Lamont-Doherty Earth Observatory Marine Geology and Geophysics, 304B Oceanography 61 Route 9W - PO Box 1000 Palisades, NY 10964-8000 US
Website | E-Mail
Phone: +1 347-535-4274
Interests: geophysics, land surface processes, remote sensing, population and environment

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 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 1800 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 (2 papers)

View options order results:
result details:
Displaying articles 1-2
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
Pre-Constrained Machine Learning Method for Multi-Year Mapping of Three Major Crops in a Large Irrigation District
Remote Sens. 2019, 11(3), 242; https://doi.org/10.3390/rs11030242
Received: 30 November 2018 / Revised: 21 January 2019 / Accepted: 22 January 2019 / Published: 24 January 2019
PDF Full-text (4771 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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
Figures

Graphical abstract

Review

Jump to: Research

Open AccessReview
Monitoring Green Infrastructure for Natural Water Retention Using Copernicus Global Land Products
Remote Sens. 2019, 11(13), 1583; https://doi.org/10.3390/rs11131583
Received: 29 May 2019 / Revised: 17 June 2019 / Accepted: 28 June 2019 / Published: 3 July 2019
PDF Full-text (8503 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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
Figures

Graphical abstract

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top