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Remote Sensing for Mountain Ecosystems II

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

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 13322

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Dr. Bogdan Andrei Mihai

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Guest Editor

Department of Geomorphology-Pedology-Geomatics, Faculty of Geography, University of Bucharest, 050663 Bucharest, Romania
Interests: land use/land cover mapping; vegetation mapping; change detection; image classification; urban remote sensing; GIS; mapping and digital cartography
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Dr. Marcel Torok

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Department of Geography, Faculty of Chemistry, Biology, Geography Timișoara, West University of Timișoara, 300223 Timișoara, Romania
Interests: remote sensing; object based image analysis (OBIA); geographic information systems (GIS); cartography; geomorphology
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Special Issue Information

Dear Colleagues,

The integration of remote sensing data from various sensors, passive and active, spaceborne and airborne, can help the researchers to map in detail the current altitudinal zonation of mountain regions at different scales in a dynamic formula in order to identify the role of climate change and the contribution of anthropogenic factors in ecosystem transformation, especially at the interface areas like the upper treeline, the mixed forests, the forest-agriculture, and the forest–urban interfaces. Earth observation data processing and analysis must be continued with a field validation process of the results in order to search for novel ideas with which to explain the mountain landscape dynamic features.

There is a real need to extend the study areas of the papers to other mountain landscapes of the World. From tropical humid and desert mountains, up to the Mediterranean, temperate, Arctic and Antarctic mountains, are meaning features to be mapped and modeled, to be explained in a dynamic formula, using multisensor and multitemporal data integration. This Special Issue will focus on the advanced ecosystem modeling and mapping of areas ranging from forest zones and alpine/subalpine pastures to the glaciated grounds and the highest peaks, and from the montane agricultural areas to secondary pasture grounds. This will allow us to explain the trajectory of ecosystems under the influence of climate change and the social and economic pressure. Different approaches can focus on searching new uncorrelated or multimodal remote sensing data structures from Earth observation image archives, including historical imagery and declassified data, in order to extract relevant environmental information and to bring original interpretations of landscape changes at different levels of detail.

The topics mainly include but are not limited to the following:

Mountain ecosystem mapping and vegetation zonation using integrated remote sensing data and field validation, with a special focus on the alpine zone.
Change detection of transitional belts between mountain vertical zones under different climate conditions, with a special focus on the treeline and timberline ecotones.
Mapping and modeling the role of soil erosion in mountain ecosystem dynamics.
Mapping and modeling the relationship between morphodynamic processes and mountain ecosystems (e.g. avalanches, landslides, debris flows, etc.).
Mapping and modeling of natural and anthropogenic factors contribution to mountain ecosystem dynamics using multisensor and multitemporal remote sensing data.
Mapping and modeling of the mountain region's agroecosystems for the identification of traditional land management features and the current dynamics of the landscapes.
Mapping and modeling land cover and land use change with the help of ecosystem related indicators in the context of the emerging infrastructure development in mountain areas (tourism, mining, traffic, hydropower etc.).
Mapping and modeling of protected features of mountain ecosystems using integrated remote sensing imagery and predicting the dynamics of these areas. Change detection analysis in the mountains protected areas.
Multi-scale and multi-temporal analysis of the mountain environment through object-based image analysis (OBIA).

Dr. Bogdan Andrei Mihai
Dr. Marcel Torok
Guest Editors

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Keywords

multisensor data
altitudinal zonation
soil erosion
change detection
dynamic mapping
data mining

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Related Special Issue

Remote Sensing for Mountain Ecosystems in Remote Sensing (10 articles)

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Research

25 pages, 47040 KiB

Open AccessArticle

Mapping Earth Hummocks in Daisetsuzan National Park in Japan Using UAV-SfM Framework

by Yu Meng, Teiji Watanabe, Yuichi S. Hayakawa, Yuki Sawada and Ting Wang

Remote Sens. 2024, 16(19), 3610; https://doi.org/10.3390/rs16193610 - 27 Sep 2024

Viewed by 734

Abstract

Earth hummocks are periglacial landforms that are widely distributed in arctic and alpine regions. This study employed an uncrewed aerial vehicle (UAV) and a structure from motion (SfM) framework to map and analyze the spatial distribution and morphological characteristics of earth hummocks across an extensive area in Daisetsuzan National Park, Japan. The UAV-captured images were processed using SfM photogrammetry to create orthomosaic images and high-resolution DEMs. We identified the distribution and morphological characteristics of earth hummocks using orthoimages, hillshade maps, and DEMs and analyzed how their morphological parameters relate to topographical conditions. A total of 18,838 individual earth hummocks in an area of approximately 82,599 m² were mapped and analyzed across the two study areas, surpassing the scale of existing studies. The average length, width, and height of these earth hummocks are 1.22 m, 1.03 m, and 0.15 m, respectively, and topographical features such as slope, aspect, and landforms are demonstrated to have an essential influence on the morphology of the earth hummocks. These findings enhance our understanding of topographical features. Furthermore, this study demonstrates the efficacy of utilizing the UAV-SfM framework with multi-directional hillshade mapping as an alternative to manual field measurements in studying periglacial landforms in mountainous regions. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

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19 pages, 20471 KiB

Open AccessArticle

Combining Multitemporal Optical and Radar Satellite Data for Mapping the Tatra Mountains Non-Forest Plant Communities

by Marcin Kluczek, Bogdan Zagajewski and Marlena Kycko

Remote Sens. 2024, 16(8), 1451; https://doi.org/10.3390/rs16081451 - 19 Apr 2024

Cited by 2 | Viewed by 1099

Abstract

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

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21 pages, 26821 KiB

Open AccessArticle

Long-Term Volumetric Change Estimation of Red Ash Quarry Sites in the Afro-Alpine Ecosystem of Bale Mountains National Park in Ethiopia

by Mohammed Ahmed Muhammed, Abubeker Mohammed Hassen, Temesgen Alemayehu Abera, Luise Wraase, Behailu Legese Ejigu, Binyam Tesfaw Hailu, Georg Miehe and Dirk Zeuss

Remote Sens. 2024, 16(7), 1226; https://doi.org/10.3390/rs16071226 - 30 Mar 2024

Viewed by 1947

Abstract

The Bale Mountains National Park (BMNP) in Ethiopia comprises the largest fraction of the Afro-Alpine ecosystem in Africa, which provides vital mountain ecosystem services at local, regional, and global levels. However, the BMNP has been severely threatened by natural and anthropogenic disturbances in recent decades. In particular, landscape alteration due to human activities such as red ash quarrying has become a common practice in the BMNP, which poses a major environmental challenge by severely degrading the Afro-Alpine ecosystem. This study aims to quantify the long-term volumetric changes of two red ash quarry sites in the BMNP using historical aerial photographs and in situ data, and to assess their impact on the Afro-Alpine ecosystem. The Structure-from-Motion multi-view stereo photogrammetry algorithm was used to reconstruct the three-dimensional landscape for the year 1967 and 1984 while spatial interpolation techniques were applied to generate the current digital elevation models for 2023. To quantify the volumetric changes and landscape alteration of the quarry sites, differences in digital elevation models were computed. The result showed that the volume of resources extracted from the BMNP quarry sites increased significantly over the study period from 1984 to 2023 compared with the period from 1967 to 1984. In general, between 1967 and 2023, the total net surface volume of the quarry sites decreased by 503,721 ± 27,970 m³ and 368,523 ± 30,003 m³, respectively. The extent of the excavated area increased by 53,147 m² and 45,297 m² for Site 1 and 2, respectively. In terms of habitat loss, major gravel road construction inside the BMNP resulted in the reduction of Afro-Alpine vegetation by 476,860 m², ericaceous vegetation by 403,806 m² and Afromontane forest by 493,222 m² with associated decline in species diversity and density. The excavation and gravel road construction have contributed to the degradation of the Afro-Alpine ecosystem, especially the endemic Lobelia rhynchopetalum on the quarry sites and roads. If excavation continues at the same rate as in the last half century, it can threaten the whole mountain ecosystem of the National Park and beyond, highlighting the importance of preventing these anthropogenic changes and conserving the remaining Afro-Alpine ecosystem. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

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27 pages, 15565 KiB

Open AccessArticle

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

by Yonghui Nie, Yifan Hu, Rula Sa and Wenyi Fan

Remote Sens. 2024, 16(5), 846; https://doi.org/10.3390/rs16050846 - 28 Feb 2024

Cited by 1 | Viewed by 1056

Abstract

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

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19 pages, 6520 KiB

Open AccessArticle

Evaluating the Potential of Sentinel-2 Time Series Imagery and Machine Learning for Tree Species Classification in a Mountainous Forest

by Pan Liu, Chunying Ren, Zongming Wang, Mingming Jia, Wensen Yu, Huixin Ren and Chenzhen Xia

Remote Sens. 2024, 16(2), 293; https://doi.org/10.3390/rs16020293 - 11 Jan 2024

Cited by 6 | Viewed by 2318

Abstract

Accurate and reliable information on tree species composition and distribution is crucial in operational and sustainable forest management. Developing a high-precision tree species map based on time series satellite data is an effective and cost-efficient approach. However, we do not quantitatively know how the time scale of data acquisitions contributes to complex tree species mapping. This study aimed to produce a detailed tree species map in a typical forest zone of the Changbai Mountains by incorporating Sentinel-2 images, topography data, and machine learning algorithms. We focused on exploring the effects of the three-year time series of Sentinel-2 within monthly, seasonal, and yearly time scales on the classification of ten dominant tree species. A random forest (RF) and support vector machine (SVM) were compared and employed to map continuous tree species. The results showed that classification with monthly datasets (overall accuracy (OA): 83.38–87.45%) outperformed that with seasonal and yearly datasets (OA:72.38–85.91%), and the RF (OA: 81.70–87.45%) was better than the SVM (OA: 72.38–83.38%) at processing the same datasets. Short-wave infrared, the normalized vegetation index, and elevation were the most important variables for tree species classification. The highest classification accuracy of 87.45% was achieved by combining RF, monthly datasets, and topography information. In terms of single species’ accuracy, the F1 scores of the ten tree species ranged from 62.99% (Manchurian ash) to 97.04% (Mongolian Oak), and eight of them obtained high F1 scores greater than 87%. This study confirmed that monthly Sentinel-2 datasets, topography data, and machine learning algorithms have great potential for accurate tree species mapping in mountainous regions. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

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23 pages, 8987 KiB

Open AccessArticle

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

by Lei Deng, Enping Yan, Jiawei Jiang and Dengkui Mo

Remote Sens. 2023, 15(24), 5721; https://doi.org/10.3390/rs15245721 - 14 Dec 2023

Viewed by 2137

Abstract

Fine fuel load (FFL) is a crucial variable influencing the occurrence of wildfire. Accurate knowledge of the distribution of FFL in mountainous forests is essential for ongoing wildfire risk management and the stability of mountain ecosystems. Traditional methods of estimating forest fuel load typically involve ground surveys combined with remote sensing, which can be costly and inefficient. Therefore, low-cost, large-scale FFL estimation remains challenging. In this study, Sentinel-2A satellite imagery from the Changsha forest region was used as the data source. Firstly, different feature variables were constructed based on false-color (B843), true-color (B432), four-band (B8432) combinations, and the Normalized Difference Water Index (NDWI). Subsequently, a machine learning approach based on random convolution was employed to estimate FFL. This study also included accuracy assessments of the estimation results and the creation of FFL maps for the study area. The results showed that the FFL estimation based on the B8432 band combination achieved the highest accuracy, with RMSE and R² values of 5.847 t·hm⁻² and 0.656, respectively. FFL estimation results based on false-color imagery followed, with true-color imagery and NDWI index-based estimation results exhibiting lower accuracy. This study offers critical FFL insights using random convolution techniques applied to Sentinel-2A imagery, enhancing the ability to monitor and manage forest fuel conditions effectively, thereby facilitating more informed regional wildfire risk management strategies. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

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17 pages, 27120 KiB

Open AccessArticle

Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery

by Huixin Ren, Chunying Ren, Zongming Wang, Mingming Jia, Wensen Yu, Pan Liu and Chenzhen Xia

Remote Sens. 2023, 15(22), 5426; https://doi.org/10.3390/rs15225426 - 20 Nov 2023

Cited by 1 | Viewed by 1652

Abstract

Understanding accurate and continuous forest dynamics is of key importance for forest protection and management in the Greater Khingan Mountains (GKM). There has been a lack of finely captured and long-term information on forest disturbance and recovery since the mega-fire of 1987 which may limit the scientific assessment of the GKM’s vegetation conditions. Therefore, we proposed a rapid and robust approach to track the dynamics of forest disturbance and recovery from 1987 to 2021 using Landsat time series, LandTrendr, and random forests (RF) models. Furthermore, we qualified the spatial characteristics of forest changes in terms of burn severity, topography, and distances from roads and settlements. Our results revealed that the integrated method of LandTrendr and RF is well adapted to track forest dynamics in the GKM, with an overall accuracy of 0.86. From 1987 to 2021, forests in the GKM showed a recovery trend with a net increase of more than 4.72 × 10⁴ ha. Over 90% of disturbances occurred between 1987 and 2010 and over 75% of recovery occurred between 1987 and 1988. Mildly burned areas accounted for 51% of forest disturbance and severely burned areas contributed to 45% of forest recovery. Forest changes tended to occur in zones with elevations of 400–650 m, slopes of less than 9°, and within 6 km of roads and 24 km of settlements. Temporal trends of forest disturbance and recovery were mainly explained by the implementation timelines of major forestry policies. Our results provide high-resolution and time-series information on forest disturbance and recovery in the GKM which could support scientific decisions on forest management and sustainable utilization. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

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19 pages, 3980 KiB

Open AccessArticle

Vertical Characteristics of Vegetation Distribution in Wuyishan National Park Based on Multi-Source High-Resolution Remotely Sensed Data

by Yongpeng Ye, Dengsheng Lu, Zuohang Wu, Kuo Liao, Mingxing Zhou, Kai Jian and Dengqiu Li

Remote Sens. 2023, 15(20), 5023; https://doi.org/10.3390/rs15205023 - 19 Oct 2023

Viewed by 1470

Abstract

Identifying vertical characteristics of mountainous vegetation distribution is necessary for studying the ecological environment quality and biodiversity and for evaluating its responses to climate change. However, producing fine vegetation distribution in a complex mountainous area remains a huge challenge. This study developed a framework based on multi-source high-resolution satellite images to strengthen the understanding of vertical features of vegetation distribution. We fused GaoFen-6 and Sentinel-2 data to produce 2 m multispectral data, combined with ALOS PALSAR digital elevation model (DEM) data, and used an object-based method to extract variables for establishing a classification model. The spatial distribution of vegetation types in Wuyishan National Park (WNP) was then obtained using a hierarchical random forest classifier. The characteristics of different vegetation types along the elevation gradient and their distribution patterns under different human protection levels were finally examined. The results show that (1) An overall accuracy of 87.11% and a Kappa coefficient of 0.85 for vegetation classification was achieved. (2) WNP exhibits obviously vertical differentiation of vegetation types, showing four compound dominant zone groups and five dominant belts. (3) The composition of vegetation types in the scenic area differs significantly from other regions. The proportions of Masson pine and Chinese fir exhibit a noticeably decreasing trend as the distance increases away from roads, while the changes in broadleaf forest and bamboo forest are less pronounced. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

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