Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Remote Sensing and Ecosystem Modeling for Nature-Based Solutions

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 11908

Share This Special Issue

Special Issue Editors

Dr. Lei Ma

E-Mail Website1 Website2 Website3
Guest Editor

Department of Geographical Sciences, University of Maryland, College Park, MD 20770, USA
Interests: process-based ecosystem modeling; terrestrial carbon cycle and climate change; remote sensing of land cover change and forest structure

Dr. Aolin Jia

E-Mail Website1 Website2
Guest Editor

University of Maryland, College Park, United States
Interests: hydrology; surface energy balance; climate variability; remote sensing; data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nature-based solutions (NbCS) represent sustainable planning and environmental management that weave natural features or processes to promote carbon neutrality, climate change adaptation, and meteorological extreme resilience. Potential pathways involve increasing carbon storage through afforestation/reforestation or reducing carbon emissions by halting deforestation. Assessing NbCS benefits and impacts requires advanced understanding of current forest dynamics and responses to climate change.

Remote sensing offers accurate observations at large spatial scales on land surface change, forest structure and distribution, carbon, water and energy fluxes. Such a rich suite of data can monitor forest functioning in response to changing climate and environmental stress (e.g., drought, heat waves, and wildfire). It can also facilitate modeling of forest biophysical and biogeochemical processes in processes based ecosystem models by improving initialization conditions or underlying assumptions.

This Special Issue seeks submissions that explore the use of remote sensing and ecosystem modeling to quantify carbon mitigation potential, evaluate vulnerability and resilience to climate change, and assess the implementations of NbCS. We welcome research that focuses on improving remote sensing techniques and products for monitoring forest biogeochemical cycle, ecohydrological and energy budget, as well as work that integrates remote sensing with empirical or process-based modeling that can contribute to understanding the role of nature-based solutions in achieving carbon neutrality, climate change adaptation, and meteorological extreme resilience.

Dr. Lei Ma
Dr. Aolin Jia
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

nature based solutions
remote sensing observations
afforestation, reforestation, deforestation
carbon, water and energy fluxes
carbon sequestration potential
empirical or process-based modeling

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

18 pages, 8110 KiB

Open AccessArticle

Data-Driven Assessment of the Impact of Hurricanes Ian and Nicole: Natural and Armored Dunes in the Aftermath of Hurricanes on Florida’s Central East Coast

by Kelly M. San Antonio, Daniel Burow, Hyun Jung Cho, Matthew J. McCarthy, Stephen C. Medeiros, Yao Zhou and Hannah V. Herrero

Remote Sens. 2024, 16(9), 1557; https://doi.org/10.3390/rs16091557 - 27 Apr 2024

Cited by 1 | Viewed by 2209

Abstract

Hurricanes Ian and Nicole caused devastating destruction across Florida in September and November 2022, leaving widespread damage in their wakes. This study focuses on the assessment of barrier islands’ shorelines, encompassing natural sand dunes and dune vegetation as well as armored dunes with man-made infrastructure such as seawalls. High-resolution satellite imagery from Planet was used to assess the impacts of these hurricanes on the beach shorelines of Volusia, Flagler, and St. Johns Counties on the Florida Central East Coast. Shorefront vegetation was classified into two classes. Normalized Difference Vegetation Index (NDVI) values were calculated before the hurricanes, one month after Hurricane Ian, one month after Hurricane Nicole, and one-year post landfall. LiDAR (Light Detection and Ranging) was incorporated to calculate vertical changes in the shorelines before and after the hurricanes. The results suggest that natural sand dunes were more resilient as they experienced less impact to vegetation and elevation and more substantial recovery than armored dunes. Moreover, the close timeframe of the storm events suggests a compound effect on the weakened dune systems. This study highlights the importance of understanding natural dune resilience to facilitate future adaptive management efforts because armored dunes may have long-term detrimental effects on hurricane-prone barrier islands. Full article

(This article belongs to the Special Issue Remote Sensing and Ecosystem Modeling for Nature-Based Solutions)

► Show Figures

Graphical abstract

10 pages, 2534 KiB

Open AccessCommunication

Use of Remote Sensing and Biogeochemical Modeling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes

by Marta Chiesi, Luca Fibbi, Silvana Vanucci and Fabio Maselli

Remote Sens. 2024, 16(2), 232; https://doi.org/10.3390/rs16020232 - 6 Jan 2024

Cited by 1 | Viewed by 1188

Abstract

The current communication presents the application of a consolidated model combination strategy to analyze the medium-term carbon fluxes in two Mediterranean pine wood ecosystems. This strategy is based on the use of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC, the outputs of which are combined taking into account the actual development phase of each ecosystem. The two pine ecosystems examined correspond to an old-growth forest and to a secondary succession after clearcuts, which differently respond to the same climatic condition during a ten-year period (2013–2022). Increasing dryness, in fact, exerts a fundamental role in controlling the gross primary and net ecosystem production of the mature stand, while the effect of forest regeneration is prevalent for the uprising of the same variables in the other stand. In particular, the simulated net carbon exchange fluctuates around 200 g C m⁻² year⁻¹ in the first stand and rises to over 600 g C m⁻² year⁻¹ in the second stand; correspondingly, the accumulation of new biomass is nearly undetectable in the former case while becomes notable in the latter. The study, therefore, supports the potential of the applied strategy for predicting the forest carbon balances consequent on diversified natural and human-induced factors. Full article

(This article belongs to the Special Issue Remote Sensing and Ecosystem Modeling for Nature-Based Solutions)

► Show Figures

Figure 1

17 pages, 6133 KiB

Open AccessArticle

Ecological Security Assessment of “Grain-for-Green” Program Typical Areas in Northern China Based on Multi-Source Remote Sensing Data

by Xingtao Liu, Hang Li, Shudong Wang, Kai Liu, Long Li and Dehui Li

Remote Sens. 2023, 15(24), 5732; https://doi.org/10.3390/rs15245732 - 15 Dec 2023

Cited by 2 | Viewed by 1558

Abstract

The Inner Mongolia segment of the Yellow River basin (IMYRB) is a typical area for ecological restoration in China. At the end of the 20th century, influenced by climate and human activities, such as mining, grazing, and farmland abandonment, the ecological security of the IMYRB was under more significant pressure. To alleviate the pressure on natural ecosystems and improve the fragile ecological situation, China implemented the “Grain-for-Green” (GFG) project in 1999. However, the evolutionary characteristics of the ecological security of the IMYRB in the first two decades of the 21st century are still lacking. Quantitative and long-term ecological security information of “Grain-for-Green” is needed. Based on this, this study used the “Pressure (P)-State (S)-Response (R)” method and proposed an ecological security assessment and early warning system based on multi-source remote sensing data. The evaluation results indicated a significant improvement in ecological security in the IMYRB from 2000 to 2020. Compared to 2000, the ecological security of the IMYRB had improved significantly in 2020, with an increase of 11.02% (ES > 0.65) and a decrease of 8.89% (ES < 0.35). For the early warning aspect of ecological security, there was a 26.31% growth in non-warning areas, with a 5% decrease in warning areas. Based on the analysis of ecologically critical factors, we proposed the implications for future ecological management as follows. (1) In ecologically fragile areas such as the IMYRB, continued implementation of the GFG was necessary. (2) Vegetation restoration should be scientific and tailored adaptive. (3) The protection of arable land also showed necessity. (4) The grazing management skills should be upgraded. Our study demonstrated that the ecological benefits derived from the “GFG” project are not immediate but cumulative and persistent. The continuous implementation of “GFG” will likely alleviate the pressure exerted by human activities on the natural environment. Full article

(This article belongs to the Special Issue Remote Sensing and Ecosystem Modeling for Nature-Based Solutions)

► Show Figures

Figure 1

22 pages, 4754 KiB

Open AccessArticle

Modeling Uncertainty of GEDI Clear-Sky Terrain Height Retrievals Using a Mixture Density Network

by Jonathan Sipps and Lori A. Magruder

Remote Sens. 2023, 15(23), 5594; https://doi.org/10.3390/rs15235594 - 1 Dec 2023

Viewed by 1507

Abstract

Early spaceborne laser altimetry mission development starts in pre-phase A design, where diverse ideas are evaluated against mission science requirements. A key challenge is predicting realistic instrument performance through forward modeling at an arbitrary spatial scale. Analytical evaluations compromise accuracy for speed, while radiative transfer modeling is not applicable at the global scale due to computational expense. Instead of predicting the arbitrary properties of a lidar measurement, we develop a baseline theory to predict only the distribution of uncertainty, specifically for the terrain elevation retrieval based on terrain slope and fractional canopy cover features through a deep neural network Gaussian mixture model, also known as a mixture density network (MDN). Training data were created from differencing geocorrected Global Ecosystem Dynamics Investigation (GEDI) L2B elevation measurements with 32 independent reference lidar datasets in the contiguous U.S. from the National Ecological Observatory Network. We trained the MDN and selected hyperparameters based on the regional distribution predictive capability. On average, the relative error of the equivalent standard deviation of the predicted regional distributions was 15.9%, with some anomalies in accuracy due to generalization and insufficient feature diversity and correlation. As an application, we predict the percent of elevation residuals of a GEDI-like lidar within a given mission threshold from 60°S to 78.25°N, which correlates to a qualitative understanding of prediction accuracy and instrument performance. Full article

(This article belongs to the Special Issue Remote Sensing and Ecosystem Modeling for Nature-Based Solutions)

► Show Figures

Figure 1

17 pages, 5124 KiB

Open AccessArticle

Quantification of Urban Greenspace in Shenzhen Based on Remote Sensing Data

by Yu Bai, Menghang Liu, Weimin Wang, Xiangyun Xiong and Shenggong Li

Remote Sens. 2023, 15(20), 4957; https://doi.org/10.3390/rs15204957 - 13 Oct 2023

Cited by 2 | Viewed by 1951

Abstract

Rapid urbanization has led to the expansion of Shenzhen’s built-up land and a substantial reduction in urban greenspace (UG). However, the changes in UG in Shenzhen are not well understood. Here, we utilized long-time-series land cover data and the Normalized Difference Vegetation Index (NDVI) as a proxy for greenspace quality to systematically analyze changes in the spatio-temporal pattern and the exposure and inequality of UG in Shenzhen. The results indicate that the UG area has been decreasing over the years, although the rate of decrease has slowed in recent years. The UG NDVI trend exhibited some seasonal variations, with a noticeable decreasing trend in spring, particularly in the eastern part of Shenzhen. Greenspace exposure gradually increased from west to east, with Dapeng and Pingshan having the highest greenspace exposure regardless of the season. Over the past two decades, inequality in greenspace exposure has gradually decreased during periods of urban construction in Shenzhen, with the fastest rate of decrease in spring and the slowest rate of decrease in summer. These findings provide a scientific basis for a better understanding of the current status of UG in Shenzhen and promote the healthy development of the city. Additionally, this study provides scientific evidence and insights for relevant decision-making institutions. Full article

(This article belongs to the Special Issue Remote Sensing and Ecosystem Modeling for Nature-Based Solutions)

► Show Figures

Figure 1

19 pages, 28738 KiB

Open AccessArticle

A Spatial and Temporal Evolution Analysis of Desert Land Changes in Inner Mongolia by Combining a Structural Equation Model and Deep Learning

by Xinyue Chang, Bing Zhang, Hongbo Zhu, Weidong Song, Dongfeng Ren and Jiguang Dai

Remote Sens. 2023, 15(14), 3617; https://doi.org/10.3390/rs15143617 - 20 Jul 2023

Cited by 2 | Viewed by 1624

Abstract

With the wide application of remote sensing technology, target detection based on deep learning has become a research hotspot in the field of remote sensing. In this paper, aimed at the problems of the existing deep-learning-based desert land intelligent extraction methods, such as the spectral similarity of features and unclear texture features, we propose a multispectral remote sensing image desert land intelligent extraction method that takes into account band information. Firstly, we built a desert land intelligent interpretation dataset based on band weighting to enhance the desert land foreground features of the images. On this basis, we introduced the deformable convolution adaptive feature extraction capability to U-Net and developed the Y-Net model to extract desert land from Landsat remote sensing images covering the Inner Mongolia Autonomous Region. Finally, in order to analyze the spatial and temporal trends of the desert land in the study area, we used a structural equation model (SEM) to evaluate the direct and indirect effects of natural conditions and human activities, i.e., population density (PD), livestock volume (LS), evaporation (Evp), temperature (T), days of sandy wind conditions (LD), humidity (RH), precipitation (P), anthropogenic disturbance index (Adi), and cultivated land (CL). The results show that the F1-score of the Y-Net model proposed in this paper is 95.6%, which is 11.5% more than that of U-Net. Based on the Landsat satellite images, the area of desert land in the study area for six periods from 1990 to 2020 was extracted. The results show that the area of desert land in the study area first increased and then decreased. The main influencing factors have been precipitation, humidity, and anthropogenic disturbance, for which the path coefficients are 0.646, 0.615, and 0.367, respectively. This study will be of great significance in obtaining large-scale and long-term time series of desert land cover and revealing the inner mechanism of desert land area change. Full article

(This article belongs to the Special Issue Remote Sensing and Ecosystem Modeling for Nature-Based Solutions)

► Show Figures

Journal Menu

Journal Browser

Remote Sensing and Ecosystem Modeling for Nature-Based Solutions

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI