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Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE)

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 23615

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Special Issue Editors

Dr. Cholho Song

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Guest Editor

OJEong Resilience Institute (OJERI), Korea University, Seoul, Korea
Interests: ecosystem services assessment; land degradation modeling; forest bio-physical modeling; land accounts and monitoring

Dr. Chul-Hee Lim

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Guest Editor

College of General Education, Kookmin University, Seoul, Republic of Korea
Interests: climate change adaptation; deforestation; spatial modeling; disaster risk reduction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Woo-Kyun Lee

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Guest Editor

Division of Environmental Science and Ecological Engineering, Korea University, Seoul 136701, Republic of Korea
Interests: forestry; land cover change detection; climate change adaptation and mitigation; climate change; disaster risk reduction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jay Sagin

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Guest Editor

School of Engineering, Nazarbayev University, Nur-Sultan, Kazakhstan
Interests: geomatics; including hydrology; hydrogeology; disaster management; trans-boundary river basins

Dr. Hadi

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Guest Editor

Ecosystem Services and Management, International Institute for Applied Systems Analysis (IIASA)
Interests: forest biophysical mapping, large scale land cover mapping/monitoring

Special Issue Information

Dear Colleagues,

The Mid-Latitude Ecotone (MLE) is an ecological transition zone around 30°–60° N degrees along with East Asia, Central Asia, and Europe. This region has various environmental changes and societal pressures, such as climate change issues, deforestation by plantation, and urbanization with a growing population. Furthermore, these problems lead the land degradation and deforestation as well as severe loss of ecosystem services in forests, grassland, and wetlands. Thus, knowing the previous and current ecological status using remote sensing is important to develop a proper management policy. With the growing awareness of the above environmental problems in the MLE, sharing knowledge among this region where there is different capacity and application of remote sensing is becoming important. Furthermore, various United Nations initiatives, such as System of Environmental-Economic Accounting (SEEA), Land Degradation Neutrality (LDN), and the Paris Agreement, have also encouraged implementing remote sensing to solve and assess environmental problems.

This Remote Sensing Special Issue aims to promote the application of remote sensing research in MLE focusing on ecosystem services and land degradation management. Many MEL countries have different remote sensing capacity, so this Special Issue focuses on both interesting regional-scale assessment and global-scale approaches that address current issues in this region. Authors are invited to submit papers, particularly on:

Monitoring land cover and land use changes using various spatiotemporal data in the MLE;
Assessing and monitoring ecosystems (forests, grasslands, wetlands, agriculture, deserts, etc);
Monitoring ecosystem functions, ecosystem services, land degradation in the MLE;
Land accounts system and land degradation neutrality using remote sensing;
Desertification, deforestation, restoration, urbanization, and landscape management issues;
Methods and approaches to manage the ecosystem services and land degradation.

Dr. Cholho Song
Prof. Dr. Chul-Hee Lim
Prof. Dr. Woo-Kyun Lee
Prof. Dr. Jay Sagin
Dr. Hadi
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

Mid-Latitude ecotone
Ecosystem services
Ecosystem function
Land degradation neutrality
Desertification
Land degradation
Afforestation and restoration
Landscape management
Earth observation
Remote sensing application

Published Papers (9 papers)

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Research

16 pages, 25523 KiB

Open AccessArticle

A Decrease in the Daily Maximum Temperature during Global Warming Hiatus Causes a Delay in Spring Phenology in the China–DPRK–Russia Cross-Border Area

by Minshu Su, Xiao Huang, Zhen Xu, Weihong Zhu and Zhehao Lin

Remote Sens. 2022, 14(6), 1462; https://doi.org/10.3390/rs14061462 - 19 Mar 2022

Cited by 5 | Viewed by 2110

Abstract

Spring phenology is the most sensitive indicator of climate change and exploring its response to climate change has important implications for ecosystem processes in the study area. The temperature changes before and after the global warming hiatus may affect the spatiotemporal pattern of land surface phenology. In this paper, taking the China–DPRK (Democratic People’s Republic of Korea)–Russia cross-border region as an example, based on GIMMS NDVI data, the Polyfit-Maximum method was used to extract the start date of the vegetation growing season (SOS). The variation trend of SOS and its response to climate change were analyzed in the early (1982–1998) and late (1998–2015) periods of the warming hiatus. At the regional scale, the spatial distribution of the SOS in the China–DPRK–Russia (CDR) cross-border area presents an elevation gradient, which is earlier in high-elevation areas and later in low-elevation areas. The temporal and spatial trend of SOS is mainly correlated by daytime maximum temperature (Tmax). The significant increase in Tmax in the early period promoted the advance of SOS (0.47 days/year), and the decrease in Tmax in the later period caused the delay of SOS (0.51 days/year). While the main influencing factor of the SOS changes in the region in the early and late periods was Tmax, the response of the SOS changes in China, DPRK and Russia to climate change also changed with the dramatic temperature changes during the warming hiatus. The Chinese side is increasingly responding to Tmax, while the North Korean side is becoming less responsive to climatic factors, and precipitation and radiation on the Russian side are driving the advance of the SOS. Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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19 pages, 6713 KiB

Open AccessEditor’s ChoiceArticle

Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series

by Beatriz Martínez, Sergio Sánchez-Ruiz, Manuel Campos-Taberner, F. Javier García-Haro and M. Amparo Gilabert

Remote Sens. 2022, 14(6), 1310; https://doi.org/10.3390/rs14061310 - 08 Mar 2022

Cited by 11 | Viewed by 2432

Abstract

The main objective of this study is to analyze the spatial and temporal variability of gross and net primary production (GPP and NPP) in Peninsular Spain across 15 years (2004–2018) and determine the relationship of those carbon fluxes with precipitation and air temperature. A time series study of daily GPP, NPP, mean air temperature, and monthly standardized precipitation index (SPI) at 1 km spatial resolution is conducted to analyze the ecosystem status and adaptation to changing environmental conditions. Spatial variability is analyzed for vegetation and specific forest types. Temporal dynamics are examined from a multiresolution analysis based on the wavelet transform (MRA-WT). The Mann–Kendall nonparametric test and the Theil–Sen slope are applied to quantify the magnitude and direction of trends (increasing or decreasing) within the time series. The use of MRA-WT to extract the annual component from daily series increased the number of statistically significant pixels. At pixel level, larger significant GPP and NPP negative changes (p-value < 0.1) are observed, especially in southeastern Spain, eastern Mediterranean coastland, and central Spain. At annual temporal scale, forests and irrigated crops are estimated to have twice the GPP of rainfed crops, shrublands, grasslands, and sparse vegetation. Within forest types, deciduous broadleaved trees exhibited the greatest annual NPP, followed by evergreen broadleaved and evergreen needle-leaved tree species. Carbon fluxes trends were correlated with precipitation. The temporal analysis based on daily TS demonstrated an increase of accuracy in the trend estimates since more significant pixels were obtained as compared to annual resolution studies (72% as to only 17%). Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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17 pages, 3149 KiB

Open AccessArticle

Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms

by Xiaoyi Wang, Caixia Liu, Guanting Lv, Jinfeng Xu and Guishan Cui

Remote Sens. 2022, 14(4), 1039; https://doi.org/10.3390/rs14041039 - 21 Feb 2022

Cited by 7 | Viewed by 3202

Abstract

Forest aboveground biomass (AGB) is of great significance since it represents large carbon storage and may reduce global climate change. However, there are still considerable uncertainties in forest AGB estimates, especially in rugged regions, due to the lack of effective algorithms to remove the effects of topography and the lack of comprehensive comparisons of methods used for estimation. Here, we systematically compare the performance of three sources of remote sensing data used in forest AGB estimation, along with three machine-learning algorithms using extensive field measurements (N = 1058) made in the Khingan Mountains of north-eastern China in 2008. The datasets used were obtained from the LiDAR-based Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation satellite (ICESat/GLAS), the optical-based Moderate Resolution Imaging Spectroradiometer (MODIS), and the SAR-based Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR). We show that terrain correction is effective for this mountainous study region and that the combination of terrain-corrected GLAS and PALSAR features with Random Forest regression produces the best results at the plot scale. Including further MODIS-based features added little power for prediction. Based upon the parsimonious data source combination, we created a map of AGB circa 2008 and its uncertainty, which yields a coefficient of determination (R²) of 0.82 and a root mean squared error of 16.84 Mg ha⁻¹ when validated with field data. Forest AGB values in our study area were within the range 79.81 ± 16.00 Mg ha⁻¹, ~25% larger than a previous, SAR-based, analysis. Our result provides a historic benchmark for regional carbon budget estimation. Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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17 pages, 4409 KiB

Open AccessArticle

Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China

by Li Peng, Shuang Zhou and Tiantian Chen

Remote Sens. 2022, 14(3), 780; https://doi.org/10.3390/rs14030780 - 08 Feb 2022

Cited by 5 | Viewed by 2142

Abstract

To address ecological threats such as land degradation in the karst regions, several ecological restoration projects have been implemented for improved vegetation coverage. Forests are the most important types of vegetation. However, the evaluation of forest restoration is often uncertain, primarily owing to the complexity of the underlying factors and lack of information related to changes in forest coverage in the future. To address this issue, a systematic case study based on the Guizhou Province, China, was carried out. First, three archetypes of driving factors were recognized through the self-organizing maps (SOM) algorithm: the high-strength ecological archetype, marginal archetype, and high-strength archetype dominated by human influence. Then, the probability of forest restoration in the context of ecological restoration was predicted using Bayesian belief networks in an effort to decrease the uncertainty of evaluation. Results show that the overall probability of forest restoration in the study area ranged from 22.27 to 99.29%, which is quite high. The findings from regions with different landforms suggest that the forest restoration probabilities of karst regions in the grid and the regional scales were lower than in non-karst regions. However, this difference was insignificant mainly because the ecological restoration in the karst regions accelerated local forest restoration and decreased the ecological impact. The proposed method of driving-factor clustering based on restoration as well as the method of predicting restoration probability have a certain reference value for forest management and the layout of ecological restoration projects in the mid-latitude ecotone. Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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13 pages, 2126 KiB

Open AccessArticle

Signatures of Wetland Impact: Spatial Distribution of Forest Aboveground Biomass in Tumen River Basin

by Guanting Lv, Guishan Cui, Xiaoyi Wang, Hangnan Yu, Xiao Huang, Weihong Zhu and Zhehao Lin

Remote Sens. 2021, 13(15), 3009; https://doi.org/10.3390/rs13153009 - 30 Jul 2021

Cited by 2 | Viewed by 2214

Abstract

The Tumen River Basin, located in the cross-border region of China, North Korea, and Russia, constitutes an important ecological barrier in China. Forest here is mainly distributed around wetland, with the distribution of wetland having the potential to regulate regional forest carbon storage. However, the spatially explicit map of forest aboveground biomass (AGB) and potential impact of drivers, i.e., wetland distribution and climate, is still lacking. We thus use a deep neural network and multi-source remote sensing data to quantify forest AGB in the Tumen River Basin. Our results show the mean forest AGB is 103.43 Mg ha⁻¹, with divergent spatial variation along its distance to wetland. The results of correlation analysis showed that with sufficient soil moisture supply, temperature dominant spatial variation of forest aboveground biomass. Noted that using the space for time substitution, we find when wetland decreased by less than 11.1%, the forest AGB decreased by more than 8%. Our result highlight the signatures of wetland impact on its nearby forest carbon storage, and urge the wetland protection, especially under the warming and drying future. Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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15 pages, 12185 KiB

Open AccessArticle

Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea

by Joon Kim, Chul-Hee Lim, Hyun-Woo Jo and Woo-Kyun Lee

Remote Sens. 2021, 13(15), 2946; https://doi.org/10.3390/rs13152946 - 27 Jul 2021

Cited by 10 | Viewed by 2838

Abstract

The role of forests to sequester carbon is considered an important strategy for mitigating climate change and achieving net zero emissions. However, forests in North Korea have continued to be cleared since the 1990s due to the lack of food and energy resources. Deforestation in this country has not been accurately classified nor consistently reported because of the characteristics of small patches. This study precisely determined the area of deforested land in North Korea through the vegetation phenological classification using high-resolution satellite imagery and deep learning algorithms. Effective afforestation target sites in North Korea were identified with priority grade. The U-Net deep learning algorithm and time-series Sentinel-2 satellite images were applied to phenological classification; the results reflected the small patch-like characteristics of deforestation in North Korea. Based on the phenological classification, the land cover of the country was classified with an accuracy of 84.6%; this included 2.6 million ha of unstocked forest and reclaimed forest. Sites for afforestation were prioritized into five grades based on deforested characteristics, altitude and slope. Forest area is expanded and the forest ecosystem is restored through successful afforestation, this may improve the overall ecosystem services in North Korea. In the long term, it will be possible to contribute to carbon neutrality and greenhouse gas reduction on the Korean Peninsula level through optimal afforestation by using these outcomes. Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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24 pages, 9097 KiB

Open AccessArticle

Development and Application of Earth Observation Based Machine Learning Methods for Characterizing Forest and Land Cover Change in Dilijan National Park of Armenia between 1991 and 2019

by Nathalie Morin, Antoine Masse, Christophe Sannier, Martin Siklar, Norman Kiesslich, Hovik Sayadyan, Loïc Faucqueur and Michaela Seewald

Remote Sens. 2021, 13(15), 2942; https://doi.org/10.3390/rs13152942 - 27 Jul 2021

Cited by 3 | Viewed by 2213

Abstract

Dilijan National Park is one of the most important national parks of Armenia, established in 2002 to protect its rich biodiversity of flora and fauna and to prevent illegal logging. The aim of this study is to provide first, a mapping of forest degradation and deforestation, and second, of land cover/land use changes every 5 years over a 28-year monitoring cycle from 1991 to 2019, using Sentinel-2 and Landsat time series and Machine Learning methods. Very High Spatial Resolution imagery was used for calibration and validation purposes of forest density modelling and related changes. Correlation coefficient R² between forest density map and reference values ranges from 0.70 for the earliest epoch to 0.90 for the latest one. Land cover/land use classification yield good results with most classes showing high users’ and producers’ accuracies above 80%. Although forest degradation and deforestation which initiated about 30 years ago was restrained thanks to protection measures, anthropogenic pressure remains a threat with the increase in settlements, tourism, or agriculture. This case study can be used as a decision-support tool for the Armenian Government for sustainable forest management and policies and serve as a model for a future nationwide forest monitoring system. Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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21 pages, 2639 KiB

Open AccessArticle

Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia

by Girma Berhe Adane, Birtukan Abebe Hirpa, Chul-Hee Lim and Woo-Kyun Lee

Remote Sens. 2021, 13(7), 1275; https://doi.org/10.3390/rs13071275 - 26 Mar 2021

Cited by 14 | Viewed by 2498

Abstract

Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR), merged satellite-gauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with ground-observed data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia—Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a non-parametric Mann–Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANN-CDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling–Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANN-CDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland sub-basin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANN-CDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the sub-basins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland sub-basin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency-, flood-, and drought-monitoring studies. However, some may require bias corrections to improve the data quality. Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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18 pages, 6017 KiB

Open AccessArticle

The Integration of Remote Sensing and Field Surveys to Detect Ecologically Damaged Areas for Restoration in South Korea

by Kyungil Lee, Hyun Chan Sung, Joung-Young Seo, Youngjae Yoo, Yoonji Kim, Jung Hyun Kook and Seong Woo Jeon

Remote Sens. 2020, 12(22), 3687; https://doi.org/10.3390/rs12223687 - 10 Nov 2020

Cited by 5 | Viewed by 2440

Abstract

Ecological damage refers to the reduction in the value of the environment due to human activities such as development. The intensity of ecosystem damage is worsening worldwide. Although the importance of restoration projects to reduce ecosystem damage is increasing, they are difficult to carry out, owing to the absence of data and monitoring of damaged areas. In this study, ecologically damaged areas for restoration in South Korea were detected using remote sensing and field surveys. For the analysis, national standardized vector datasets and Google Earth images were used; field surveys were conducted from 2018 to early 2020. Our results showed that 62% of the ecological damage that occurred in South Korea existed in forest ecosystems; the damaged areas were mostly smaller than 50,000 m². Additionally, most of the causes and types of damage due to human activities such as development were soil erosion related. The results also suggest the importance of obtaining monitoring data on ecologically damaged areas and the importance of establishing an appropriate restoration plan using this data. Full article

(This article belongs to the Special Issue Remote Sensing Application for Promoting Ecosystem Services and Land Degradation Management in Mid-Latitude Ecotone (MLE))

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