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Advances in Geospatial Techniques on Ecosystem Monitoring
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Advances in Geospatial Techniques on Ecosystem Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Ecology Science and Engineering".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 11706

Special Issue Editors

Prof. Dr. Huaguo Huang

E-Mail Website
Guest Editor
Forestry College, Beijing Forestry University, No. 35 Qinghua East Road, Beijing 100083, China
Interests: 3D modelling of remote sensing in all spectrum including visual, NIR, thermal lidar, microwave and remote sensing of forests
Special Issues, Collections and Topics in MDPI journals
Dr. Lin Sun

E-Mail Website
Guest Editor
College of Geomatics, Shandong University of Science and Technology, Qingdao 266510, China
Interests: remote sensing; aerosol retrieval; cloud/cloud shadow detection; atmospheric correction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geospatial techniques play an important role in supporting effective monitoring in various ecosystems, including but not limited to forests, agriculture, grassland, wetland, deserts, and oceans. In recent years, from ground to space, from visible to microwave, extensive remote sensing observation capacities and spatial analysis techniques have been rapidly developed in different fields. The advances of these geospatial techniques have changed the ecosystem monitoring mode. However, there are still gaps between ecosystem end users and technique providers, or even between observers and analysts. The information and its accuracy extracted from geospatial techniques are still not perfect to satisfy the needs of ecosystem monitoring. Further development is required to improve the accuracy and reliability of mensuration and the attributes estimated from these new technologies. New application areas are waiting to be discovered.

This Special Issue will feature multidisciplinary research to advance our understanding of recent developments in geospatial techniques on various ecosystem monitoring. This includes research on the development and implementation of new theories, methods, and practice in different ecosystems. Our aim is to gather high-quality research on new insights that inform how to better observe, analyze, manage, and improve geospatial techniques and ultimately provide solutions for different ecosystem groups

Dr. Huaguo Huang
Dr. Lin Sun
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. Applied Sciences 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 2400 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

  • UAV
  • remote sensing
  • GIS
  • spatial analysis
  • ecosystem

Published Papers (7 papers)

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Research

20 pages, 13824 KiB  
Article
Assessing Dynamic Changes, Driving Mechanisms and Predictions of Multisource Vegetation Remote Sensing Products in Chinese Regions
by Yang Han, Yilin Lin, Peng Zhou, Jinjiang Duan, Zhaoxiang Cao, Jian Wang and Kui Yang
Appl. Sci. 2023, 13(9), 5229; https://doi.org/10.3390/app13095229 - 22 Apr 2023
Viewed by 1225
Abstract
Terrestrial vegetation, a critical component of the Earth’s land surface, directly impacts the planet’s material and energy balance. This study investigated the dynamics of terrestrial vegetation in China from 2000 to 2019 using three remote sensing products (NDVI, EVI, and SIF) and explored [...] Read more.
Terrestrial vegetation, a critical component of the Earth’s land surface, directly impacts the planet’s material and energy balance. This study investigated the dynamics of terrestrial vegetation in China from 2000 to 2019 using three remote sensing products (NDVI, EVI, and SIF) and explored the driving mechanisms behind these changes. We considered three meteorological factors, nine land use types, and two socio-economic factors while employing mathematical models to analyze the data. Additionally, we used the CA–Markov model to predict the spatial distribution of vegetation remote sensing products for 2020–2025. Our findings indicate the following: (1) Throughout the study period, the vegetation indices, NDVI, EVI, and SIF, all exhibited increasing trends. The SIF showed a more direct response to vegetation cover changes and was less influenced by other driving factors. The SIF outperforms the NDVI and EVI in detecting vegetation trend changes, particularly regarding sensitivity. (2) Vegetation cover changes are driven by multiple meteorological factors, such as temperature, precipitation, and relative humidity. These factors exhibit a strong spatial correlation with the distribution of vegetation remote sensing products. Among these factors, the SIF shows a higher sensitivity to temperature compared to the NDVI and EVI, while the NDVI and EVI display greater sensitivity to precipitation and relative humidity. (3) Within the study area, land use types reveal a gradient from northwest to southeast, which is consistent with the spatial distribution of the vegetation remote sensing products. For green vegetation types, the three remote sensing products exhibit varying sensitivity levels, with the SIF demonstrating the highest sensitivity to green vegetation types. (4) Overall, the future vegetation outlook in China is promising, especially in the southeastern regions where significant vegetation improvement trends are evident. However, the vegetation conditions in some northwestern areas remain less favorable, necessitating the reinforcement of ecological construction and improvement measures. Additionally, a significant positive correlation exists between population size, GDP, and vegetation remote sensing products. This study highlights the variability in the dynamics and driving mechanisms of terrestrial vegetation remote sensing products in China and employs the CA–Markov model for predicting future vegetation patterns. Our research contributes to the theoretical and technical understanding of remote sensing for terrestrial vegetation in the Chinese context. Full article
(This article belongs to the Special Issue Advances in Geospatial Techniques on Ecosystem Monitoring)
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22 pages, 8317 KiB  
Article
Spatiotemporal Variation of Net Primary Productivity and Its Response to Climate Change and Human Activities in the Yangtze River Delta, China
by Dengpan Li, Lei Tian, Mingyang Li, Tao Li, Fang Ren, Chunhong Tian and Ce Yang
Appl. Sci. 2022, 12(20), 10546; https://doi.org/10.3390/app122010546 - 19 Oct 2022
Cited by 5 | Viewed by 1354
Abstract
Exploring the temporal and spatial changes, as well as driving factors, of net primary productivity (NPP) of terrestrial ecosystems is essential for maintaining regional carbon balance. This work focuses on the spatiotemporal variation and future trends of NPP and the response mechanisms of [...] Read more.
Exploring the temporal and spatial changes, as well as driving factors, of net primary productivity (NPP) of terrestrial ecosystems is essential for maintaining regional carbon balance. This work focuses on the spatiotemporal variation and future trends of NPP and the response mechanisms of NPP to various driving factors. The Theil–Sen estimator, as well as Mann–Kendall and Hurst exponent methods, were used to analyze the spatiotemporal dynamics and future trends of NPP, and geographical detectors and correlation analysis were used to reveal the response of NPP to various driver changes to environmental factors. The results showed that the NPP was generally on an increasing trend in the Yangtze River Delta region from 2000 to 2019, with the average NPP value of 550.17 g C m−2 a−1, of which 85.90% was the increasing regions and 14.10% was the decreasing regions, showing a significant spatiotemporal heterogeneity characteristic. The trend of future changes in NPP is dominated by an anti-persistence trend in the study area, i.e., the opposite of the past trend. Notably, annual precipitation is the most significant positive driver of NPP; while NPP was negatively correlated with population, meanwhile, different land use/land cover (LULC) also significantly affected the spatial distribution of NPP. Besides, there was a two-factor enhanced interaction between the various drivers on NPP, with the highest interaction occurring between temperature and elevation. Overall, this study provides data support for future regional NPP predictions and ecosystem evaluations. Full article
(This article belongs to the Special Issue Advances in Geospatial Techniques on Ecosystem Monitoring)
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15 pages, 6138 KiB  
Article
Improving Landslide Recognition on UAV Data through Transfer Learning
by Kaixin Yang, Wei Li, Xinran Yang and Lei Zhang
Appl. Sci. 2022, 12(19), 10121; https://doi.org/10.3390/app121910121 - 08 Oct 2022
Cited by 1 | Viewed by 1365
Abstract
As a frequent geological disaster, landslides cause serious casualties and economic losses every year. When landslides occur, rapid access to disaster information is the premise of implementing disaster relief and reduction. Traditional satellite remote sensing may not be able to timely obtain the [...] Read more.
As a frequent geological disaster, landslides cause serious casualties and economic losses every year. When landslides occur, rapid access to disaster information is the premise of implementing disaster relief and reduction. Traditional satellite remote sensing may not be able to timely obtain the image data from the disaster areas due to orbital cycle and weather impacts. Visual interpretation of remote sensing data and machine learning methods need to be improved the detection efficiency. This paper studies landslide recognition based on the UAV remote sensing image. The affected area of the Zhangmu Port region in Tibet by the Nepal earthquake occurred on 25 April 2015 was selected to carry out the landslide investigation. Aiming at the problem of insufficient training sample data of landslides, we adopt the transfer learning method. The evaluation indexes show that the proposed method can automatically identify landslide disasters. Comparing with the SSD model, our new approach has better detection performance, providing thus accurate data support for scientific decision-making of disaster rescue. Full article
(This article belongs to the Special Issue Advances in Geospatial Techniques on Ecosystem Monitoring)
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13 pages, 1776 KiB  
Article
Comparison of Canopy Cover and Leaf Area Index Estimation from Airborne LiDAR and Digital Aerial Photogrammetry in Tropical Forests
by Chenyun Li, Yanfeng Zheng, Xinjie Zhang, Fayun Wu, Linyuan Li and Jingyi Jiang
Appl. Sci. 2022, 12(19), 9882; https://doi.org/10.3390/app12199882 - 30 Sep 2022
Cited by 1 | Viewed by 1708
Abstract
Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the [...] Read more.
Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests. Full article
(This article belongs to the Special Issue Advances in Geospatial Techniques on Ecosystem Monitoring)
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23 pages, 4760 KiB  
Article
Study on the Structural Properties of an Ecospatial Network in Inner Mongolia and Its Relationship with NPP
by Xiaoci Wang, Ruirui Wang, Qiang Yu, Hongjun Liu, Wei Liu, Jun Ma, Teng Niu and Linzhe Yang
Appl. Sci. 2022, 12(10), 4872; https://doi.org/10.3390/app12104872 - 11 May 2022
Cited by 8 | Viewed by 1537
Abstract
In the context of strengthening the construction of ecological civilization and accelerating the “carbon peak” in China, the regional ecological pattern and its connection with carbon sink capacity have become an urgent topic. Given that Inner Mongolia is a large carbon emission province [...] Read more.
In the context of strengthening the construction of ecological civilization and accelerating the “carbon peak” in China, the regional ecological pattern and its connection with carbon sink capacity have become an urgent topic. Given that Inner Mongolia is a large carbon emission province and the conflict between economic development and ecological protection is particularly prominent, we took Inner Mongolia as an example to extract its ecospatial network, then calculated the integrity index, topological indices, and recovery robustness of the network and evaluated integrity and other properties of the ecospatial network structure by combining them with the ecological background. In addition, we analyzed the relationship between the topological indices and net primary productivity (NPP). The results showed that the network was scale-free and heterogeneous, with low integrity, connectivity and stability, which were the focus of future optimization. The nodes with important functions were mainly distributed in the farm-forest ecotone, grasslands, and the agro-pastoral ecotone; under the simulation attack, the node recovery robustness was stronger than the corridor recovery robustness, and NPP was negatively and significantly correlated with the woodland nodes and grassland nodes. In terms of ecological restoration, the unused land in the west is a key area, and it is necessary to add new ecological nodes and corridors. In terms of enhancing carbon sequestration capacity, under the premise of ensuring network connectivity, the appropriate and rational merging of ecological nodes and corridors within woodlands and grasslands is a particularly effective means. This study provides a reference for evaluating and optimizing the ecological pattern of areas with prominent ecological problems and improving the carbon sink of ecosystems in terms of their ecospatial network structure. Full article
(This article belongs to the Special Issue Advances in Geospatial Techniques on Ecosystem Monitoring)
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14 pages, 3697 KiB  
Article
Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
by Jingxu Wang, Shengwang Meng, Qinnan Lin, Yangyang Liu and Huaguo Huang
Appl. Sci. 2022, 12(9), 4372; https://doi.org/10.3390/app12094372 - 26 Apr 2022
Cited by 5 | Viewed by 1598
Abstract
Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance [...] Read more.
Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R2 = 0.7890) than at the plot scale (R2 = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (−0.9121 at tree scale and −0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (<1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy. Full article
(This article belongs to the Special Issue Advances in Geospatial Techniques on Ecosystem Monitoring)
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41 pages, 9935 KiB  
Article
Suitable Land-Use and Land-Cover Allocation Scenarios to Minimize Sediment and Nutrient Loads into Kwan Phayao, Upper Ing Watershed, Thailand
by Jiraporn Kulsoontornrat and Suwit Ongsomwang
Appl. Sci. 2021, 11(21), 10430; https://doi.org/10.3390/app112110430 - 05 Nov 2021
Cited by 6 | Viewed by 1718
Abstract
Human activity and land-use changes have affected the water quality of Kwan Phayao, Upper Ing watershed, due to the associated high sediment load and eutrophication. This study aims to identify suitable LULC allocation scenarios for minimizing sediment and nutrient export into the lake. [...] Read more.
Human activity and land-use changes have affected the water quality of Kwan Phayao, Upper Ing watershed, due to the associated high sediment load and eutrophication. This study aims to identify suitable LULC allocation scenarios for minimizing sediment and nutrient export into the lake. For this purpose, the LULC status and change were first assessed, based on classified LULC data in 2009 and 2019 from Landsat images, using the SVM algorithm. Later, the land requirements of three scenarios between 2020 and 2029 were estimated, based on their characteristics, and applied to predict LULC change using the CLUE-S model. Then, actual LULC data in 2019 and predicted LULC data under three scenarios between 2020 and 2029 were used to estimate sediment and nutrient export using the SDR and NDR models. Finally, the ecosystem service change index identified a suitable LULC allocation for minimizing sediment or/and nutrient export. According to the results, LULC status and change indicated perennial trees and orchards, para rubber, and rangeland increased, while forest land and paddy fields decreased. The land requirements of the three scenarios provided reasonable results, as expected, particularly Scenario II, which adopts linear programming to calculate the land requirements for maximizing ecosystem service values. For sediment and nutrient export estimation under the predicted LULC for the three scenarios, Scenario II led to the lowest yield of sediment and nutrient exports, and provided the lowest average ESCI value among the three scenarios. Thus, the LULC allocation under Scenario II was chosen as suitable for minimizing sediment or/and nutrient export into Kwan Phayao. These results can serve as crucial information to minimize sediment and nutrient loads for land-use planners, land managers, and decision makers. Full article
(This article belongs to the Special Issue Advances in Geospatial Techniques on Ecosystem Monitoring)
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