Digital Mapping for Ecological Land

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: 23 September 2024 | Viewed by 9535

Special Issue Editors


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Guest Editor
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Interests: remote sensing monitoring algorithms for ecological restoration; remote sensing application research on natural resources

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Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing 210008, China
Interests: remote sensing of aquaculture; land cover and land use change; wetland remote sensing; remote sensing of water environment and water ecology
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China
Interests: agricultural remote sensing; algorithms and models for processing multi-source geological data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital mapping of ecological land usually includes various information related to the ecological environment, such as: 1. Land cover and land use information: This includes information on the types of land cover and land use, such as forests, grasslands, wetlands, croplands, urban areas, and water bodies. 2. Topography and terrain information: This includes information on elevation, slope, aspect, and other terrain characteristics. 3. Soil information: This includes information on soil type, soil texture, soil depth, and other soil properties. 4. Vegetation information: This includes information on vegetation types, species composition, and distribution. 5. Hydrological information: This includes information on rivers, lakes, wetlands, and other water bodies, as well as information on water quality, water flow, and water resources. 6. Climatic information: This includes information on temperature, precipitation, and other climate-related variables. 7. Ecological sensitivity and vulnerability information: This includes information on the sensitivity and vulnerability of various ecological systems to human activities and environmental changes.  8. Biodiversity information: This includes information on the distribution and abundance of various plant and animal species, as well as information on their habitats and ecological roles. Overall, digital mapping of ecological land provides a comprehensive and detailed picture of the ecological environment, which can be used for ecological conservation, land management, and environmental planning.

The aim of creating the special issue for the subject of 'digital mapping of ecological land' could be to bring together various researchers and experts in the field to discuss the latest advancements, challenges, and opportunities in this area. This issue could provide an excellent platform for sharing knowledge, exchanging ideas, and discussing different perspectives on how digital mapping can help us better understand and protect our ecosystems. Moreover, this special issue could also help raise awareness about the importance of ecological mapping and its potential to inform policy decisions, land management practices, and conservation efforts.

Themes:

  • Advances in remote sensing and digital mapping technologies for ecological land mapping;
  • Ecological land mapping for conservation and management of biodiversity;
  • Integrating socio-economic data into ecological land mapping;
  • Challenges and opportunities in ecological land mapping for climate change adaptation and mitigation;
  • Ecological land mapping for monitoring and evaluation of ecosystem services;
  • Applications of ecological land mapping in urban and rural landscapes;
  • Land deformation and geohazard monitoring mapping.

Article types:
Original research articles
Review articles
Case studies
Methodological articles

Dr. Rongyuan Liu
Dr. Juhua Luo
Prof. Dr. Jingcheng Zhang
Guest Editors

Manuscript Submission Information

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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. Land is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • natural resources
  • ecological environment
  • lake
  • wetland
  • agriculture
  • remote sensing
  • big data analysis
  • GIS analysis
  • ecological restoration

Published Papers (8 papers)

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Research

23 pages, 10686 KiB  
Article
Threshold Effects between Ecosystem Services and Natural and Social Drivers in Karst Landscapes
by Mantong Zhao, Qiuwen Zhou, Yingzhong Luo, Yuan Li, Yalin Wang and Ershuang Yuan
Land 2024, 13(5), 691; https://doi.org/10.3390/land13050691 - 15 May 2024
Viewed by 556
Abstract
It has been shown that there are thresholds of influence on the response of ecosystem services to their drivers, and the range of drivers that provide high levels of ecosystem services can be delineated through thresholds. However, due to the spatial heterogeneity of [...] Read more.
It has been shown that there are thresholds of influence on the response of ecosystem services to their drivers, and the range of drivers that provide high levels of ecosystem services can be delineated through thresholds. However, due to the spatial heterogeneity of landscapes in karst regions, the results of ecosystem service threshold studies in non-karst regions may not be applicable to karst regions. This study explores the threshold effects between ecosystem services in karst landscapes and their natural and social drivers. It is shown that there are nonlinear constraints between them, and different critical thresholds exist for different kinds of ecosystem services. The main thresholds for water supply services include the slope (43.64°) and relief amplitude (331.60 m); for water purification services, they include relief amplitude (147.05 m) and distance to urban land (DTUL) (32.30 km); for soil conservation services, they include the normalized difference vegetation index (NDVI) (0.80) and nighttime light intensity (43.58 nW∙cm−2∙sr−1); the main thresholds for biodiversity maintenance services include population density (1481.06 person∙km−2) and distance to urban land (DTUL) (32.80 km). This enables regional ecological conservation planning based on different threshold ranges corresponding to different ecosystem services to meet the different needs of different decision makers. Full article
(This article belongs to the Special Issue Digital Mapping for Ecological Land)
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18 pages, 6096 KiB  
Article
A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)
by Rafaela Tiengo, Silvia Merino-De-Miguel, Jéssica Uchôa and Artur Gil
Land 2024, 13(5), 666; https://doi.org/10.3390/land13050666 - 12 May 2024
Viewed by 751
Abstract
Small oceanic islands, such as São Miguel Island in the Azores (Portugal), face heightened susceptibility to the adverse impacts of climate change, biological invasions, and land cover changes, posing threats to biodiversity and ecosystem functions and services. Over the years, persistent conservation endeavors, [...] Read more.
Small oceanic islands, such as São Miguel Island in the Azores (Portugal), face heightened susceptibility to the adverse impacts of climate change, biological invasions, and land cover changes, posing threats to biodiversity and ecosystem functions and services. Over the years, persistent conservation endeavors, notably those supported by the EU LIFE Programme since 2003, have played a pivotal role in alleviating biodiversity decline, particularly in the eastern region of São Miguel Island. This study advocates the application of remote sensing data and techniques to support the management and effective monitoring of LIFE Nature projects with land cover impacts. A land cover change detection approach utilizing Rao’s Q diversity index identified and assessed changes from 2002 to 2021 in intervention areas. The study analyzed the changes in LIFE project areas using ASTER, Landsat 8, and Sentinel 2 data through Google Earth Engine on Google Colab (with Python). This methodological approach identified and assessed land cover changes in project intervention areas within defined timelines. This technological integration enhances the potential of remote sensing for near-real-time monitoring of conservation projects, making it possible to assess their land cover impacts and intervention achievements. Full article
(This article belongs to the Special Issue Digital Mapping for Ecological Land)
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17 pages, 4582 KiB  
Article
Research on the Optimization of Multi-Class Land Cover Classification Using Deep Learning with Multispectral Images
by Yichuan Li, Junchuan Yu, Ming Wang, Minying Xie, Laidian Xi, Yunxuan Pang and Changhong Hou
Land 2024, 13(5), 603; https://doi.org/10.3390/land13050603 - 30 Apr 2024
Viewed by 473
Abstract
With the advancement of artificial intelligence, deep learning has become instrumental in land cover classification. While there has been a notable emphasis on refining model structures to improve classification accuracy, it is imperative to also emphasize the pivotal role of data-driven optimization techniques. [...] Read more.
With the advancement of artificial intelligence, deep learning has become instrumental in land cover classification. While there has been a notable emphasis on refining model structures to improve classification accuracy, it is imperative to also emphasize the pivotal role of data-driven optimization techniques. This paper presents an in-depth investigation into optimizing multi-class land cover classification using high-resolution multispectral images from Worldview3. We explore various optimization strategies, including refined sampling strategies, data band combinations, loss functions, and model enhancements. Our optimizations led to a substantial increase in the Mean Intersection over Union (mIoU) classification accuracy, improving from a baseline of 0.520 to a final accuracy of 0.709, which represents a 35.2% enhancement. Specifically, by optimizing the classic semantic segmentation network in four key aspects, we improved the mIoU by 15.5%. Further improvements through changes in data combinations, sampling methods, and loss functions led to an overall 17.2% increase in mIoU. The proposed model optimization methods enabled the OUNet to outperform the baseline model by providing more precise edge detection and feature representation, while reducing the model parameters scale. Experimental evidence shows that in the application of multi-class land surface classification, increasing the quantity and diversity of samples, avoiding data imbalance issues, is equally valuable for improving overall classification accuracy as it is for enhancing model performance. Full article
(This article belongs to the Special Issue Digital Mapping for Ecological Land)
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19 pages, 11968 KiB  
Article
Validation of Remotely Sensed Land Surface Temperature at Lake Baikal’s Surroundings Using In Situ Observations
by Egor Dyukarev, Nadezhda Voropay, Oksana Vasilenko and Elena Rasputina
Land 2024, 13(4), 555; https://doi.org/10.3390/land13040555 - 21 Apr 2024
Viewed by 593
Abstract
The accuracy of Land Surface Temperature (LST) products retrieved from satellite data in mountainous-coastal areas is not well understood. This study presents an analysis of the spatial and temporal variability of the differences between the LST and in situ observed air and surface [...] Read more.
The accuracy of Land Surface Temperature (LST) products retrieved from satellite data in mountainous-coastal areas is not well understood. This study presents an analysis of the spatial and temporal variability of the differences between the LST and in situ observed air and surface temperatures (ISTs) for the southeastern slope of Lake Baikal’s surroundings. The IST was measured at 12 ground observation sites located on the southeastern macro-slope of the Primorskiy Ridge (Baikal, Russia) within an elevation range of 460–1656 m above sea level from 2009 to 2021. LST was estimated using 617 Landsat (7 and 8) images from 2009–2021, taking into account brightness temperature, surface emissivity and vegetation cover fraction. The comparison of the LST from satellite data and the IST from ground observation showed relatively high differences, which varied depending on the season and site type. A neural network was suggested and calibrated to improve the LST data. The corrected remote-sensed temperature was found to reproduce the IST very well, with mean differences of about 0.03 °C and linear correlation coefficients of 0.98 and 0.95 for the air and surface IST. Full article
(This article belongs to the Special Issue Digital Mapping for Ecological Land)
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14 pages, 8066 KiB  
Article
Biophysical Effects of Land Cover Changes on Land Surface Temperature on the Sichuan Basin and Surrounding Regions
by Xiangming Mao, Gula Tang, Jiaqiang Du and Xiaotong Tian
Land 2023, 12(11), 1959; https://doi.org/10.3390/land12111959 - 24 Oct 2023
Cited by 1 | Viewed by 892
Abstract
The biophysical effect of land cover changes (LCC) on local temperature is currently a hot topic. This work selects one of the nine agricultural divisions in China, the Sichuan Basin and surrounding regions, as the study area. By combining long-term series satellite remote [...] Read more.
The biophysical effect of land cover changes (LCC) on local temperature is currently a hot topic. This work selects one of the nine agricultural divisions in China, the Sichuan Basin and surrounding regions, as the study area. By combining long-term series satellite remote sensing products with the space-and-time method, the spatial and temporal variations of the actual biophysical effects of LCC on land surface temperature (LST) are obtained. The results show that: (1) From 2001 to 2020, LCCs from Savannas to Cropland, from Cropland to Savannas, and from Savannas to Mixed Forest occurred frequently within the study area, and their area proportions of the total conversions are 21.7%, 18.5%, and 17.6%, respectively. (2) The biophysical feedback of LCC in the study area led to a LST increase of 0.01 ± 0.004 K at annual scale, which presents a seasonal pattern of “strong warming in summer and autumn yet weak cooling in winter”. It can exacerbate 14.3% or alleviate 8.3% of the background climate warming effect, illustrating the importance of biophysical effects on local climate change. The interaction between savannas and cropland or mixed forest and urbanizations formed the main driver for the above patterns. (3) Both the occurrence area of LCC and the warming effects at annual or seasonal scale show a trend of “first rising and then declining”, whereas the cooling effect in winter exhibits continuous enhancement over time. The monodirectional or mutual conversion between cropland and savannas is the dominant conversion responsible for these temporal patterns. The findings can provide realistic scientific guidance for informing rational policies on land management and targeted strategies for climate change response in the study area. Full article
(This article belongs to the Special Issue Digital Mapping for Ecological Land)
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15 pages, 3471 KiB  
Article
Estimation of Landslide and Mudslide Susceptibility with Multi-Modal Remote Sensing Data and Semantics: The Case of Yunnan Mountain Area
by Fan Yang, Xiaozhi Men, Yangsheng Liu, Huigeng Mao, Yingnan Wang, Li Wang, Xiran Zhou, Chong Niu and Xiao Xie
Land 2023, 12(10), 1949; https://doi.org/10.3390/land12101949 - 20 Oct 2023
Cited by 2 | Viewed by 910
Abstract
Landslide and mudslide susceptibility predictions play a crucial role in environmental monitoring, ecological protection, settlement planning, etc. Currently, multi-modal remote sensing data have been used for precise landslide and mudslide disaster prediction with spatial details, spectral information, or terrain attributes. However, features regarding [...] Read more.
Landslide and mudslide susceptibility predictions play a crucial role in environmental monitoring, ecological protection, settlement planning, etc. Currently, multi-modal remote sensing data have been used for precise landslide and mudslide disaster prediction with spatial details, spectral information, or terrain attributes. However, features regarding landslide and mudslide susceptibility are often hidden in multi-modal remote sensing images, beyond the features extracted and learnt by deep learning approaches. This paper reports our efforts to conduct landslide and mudslide susceptibility prediction with multi-modal remote sensing data involving digital elevation models, optical remote sensing, and an SAR dataset. Moreover, based on the results generated by multi-modal remote sensing data, we further conducted landslide and mudslide susceptibility prediction with semantic knowledge. Through the comparisons with the ground truth datasets created by field investigation, experimental results have proved that remote sensing data can only enhance deep learning techniques to detect the landslide and mudslide, rather than the landslide and mudslide susceptibility. Knowledge regarding the potential clues about landslide and mudslide, which would be critical for estimating landslide and mudslide susceptibility, have not been comprehensively investigated yet. Full article
(This article belongs to the Special Issue Digital Mapping for Ecological Land)
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15 pages, 10430 KiB  
Article
Mapping and Analyses of Land Subsidence in Hengshui, China, Based on InSAR Observations
by Man Li, Daqing Ge, Xiaofang Guo, Ling Zhang, Bin Liu, Yan Wang, Qiong Wu, Xiangxing Wan and Yu Wang
Land 2023, 12(9), 1684; https://doi.org/10.3390/land12091684 - 28 Aug 2023
Cited by 1 | Viewed by 871
Abstract
In this paper, we use interferometric synthetic aperture radar (InSAR) annual and time-series analysis of RADARSAT-2 SAR data, spanning from September 2011 to October 2022, to study the temporal–spatial characteristics of land subsidence in Hengshui, North China Plain. The data reveal two large-scale [...] Read more.
In this paper, we use interferometric synthetic aperture radar (InSAR) annual and time-series analysis of RADARSAT-2 SAR data, spanning from September 2011 to October 2022, to study the temporal–spatial characteristics of land subsidence in Hengshui, North China Plain. The data reveal two large-scale subsidence areas in Hengshui, individually located to the north of Hengshui city around the Hutuo River and to the east or south along the Fuyang, Suolu and Qingliang Rivers. The fastest subsidence arises after 2017, with the maximum rate exceeding 11 cm/year. We correlate the observed subsidence with the central table variation of groundwater depression, groundwater table variation of three confined aquifers, hydraulic head declines of three confined aquifers and the time-dependent total hydraulic head variation. We find a spatial consistency between land subsidence and groundwater depression or hydraulic head declines of three confined aquifers, and subsidence displacement and total hydraulic heads both manifest clear seasonal variability. This suggests that the subsidence is primarily caused by groundwater extraction for agricultural use. We also observe that the subsidence rates in Hengshui did not decrease but rather increased when the groundwater table significantly rose after September 2019. It is very likely that as a result of the occurrence of thick and widespread continuity of clay layers with high compressibility in the Quaternary deposit of Hengshui, a new preconsolidation head could be generated due to groundwater table drop, leading to the effective hydraulic head still existing in the aquitards even if the groundwater table rises in the aquifer systems. Full article
(This article belongs to the Special Issue Digital Mapping for Ecological Land)
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12 pages, 14238 KiB  
Article
Land Surface Temperature Estimation from Landsat-9 Thermal Infrared Data Using Ensemble Learning Method Considering the Physical Radiance Transfer Process
by Xin Ye, Rongyuan Liu, Jian Hui and Jian Zhu
Land 2023, 12(7), 1287; https://doi.org/10.3390/land12071287 - 26 Jun 2023
Cited by 2 | Viewed by 3325
Abstract
Accurately estimating land surface temperature (LST) is a critical concern in thermal infrared (TIR) remote sensing. According to the thermal radiance transfer equation, the observed data in each channel are coupled with both emissivity and atmospheric parameters in addition to the LST. To [...] Read more.
Accurately estimating land surface temperature (LST) is a critical concern in thermal infrared (TIR) remote sensing. According to the thermal radiance transfer equation, the observed data in each channel are coupled with both emissivity and atmospheric parameters in addition to the LST. To solve this ill-posed problem, classical algorithms often require the input of external parameters such as land surface emissivity and atmospheric profiles, which are often difficult to obtain accurately and timely, and this may introduce additional errors and limit the applicability of the LST retrieval algorithms. To reduce the dependence on external parameters, this paper proposes a new algorithm to directly estimate the LST from the top-of-atmosphere brightness temperature in Landsat-9 two-channel TIR data (channels 10 and 11) without external parameters. The proposed algorithm takes full advantage of the adeptness of the ensemble learning method to solve nonlinear problems. It considers the physical radiance transfer process and adds the leaving-ground bright temperature and atmospheric water vapor index to the input feature set. The experimental results show that the new algorithm achieves accurate LST estimation results compared with the ground-measured LST and is consistent with the Landsat-9 LST product. In subsequent work, further studies will be undertaken on developing end-to-end deep learning models, mining more in-depth features between TIR channels, and reducing the effect of spatial heterogeneity on accuracy validation. Full article
(This article belongs to the Special Issue Digital Mapping for Ecological Land)
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