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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 23923

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Dr. Jan Brus

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Guest Editor

Department of Geoinformatics, Palacky University Olomouc, 77146 Olomouc, Czech Republic
Interests: spatial analyses and modeling of environmental data; landscape ecology; environmental data quality and uncertainty; 3D printing

Dr. Vilém Pechanec

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Guest Editor

Department of Geoinformatics, Faculty of Science, Palacký University, 17th listopadu 50, 771 46 Olomouc, Czech Republic
Interests: environmental geoinformatics; geonformatics & landscape ecology; modelling of ecosystem functions/services; spatial decision support system
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Special Issue Information

Dear Colleagues,

Remote sensing is a dynamically developing field, both in the technological and application level. Landscape ecology is widely recognized to be one of the most significant areas where it is used at an advanced level. Thanks to its properties, remote sensing is an effective way not only for ad hoc monitoring of remote places where it is not easy to perform ground measurements but also for regular, repeatable monitoring of individual landscape components. In a confrontation with destructive methods, it is possible to get an overview of the landscape change easily, effectively, and quickly to any extent and detail.

With the help of remote sensing of the Earth, the state of the landscape at all levels can not only be monitored, but the degree of fulfilment of selected landscape functions or the risk of degradation can also be classified and quantified.

The aim of this work is to extend our knowledge of Landscape Ecology and Landscape Sustainability. This Special Issue provides a place for building a solid knowledge base with a breadth of applicability. It will serve as a source for “best practices” of remote sensing applications in the landscape ecology domain. Studies working with data out of the visible spectrum or solving uncertainty issues are highly welcome.

Dr. Jan Brus
Dr. Vilém Pechanec
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. 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

Landscape monitoring
Landscape assessment
Multispectral data
Thermal data
Quantification of landscape functions
Landscape evaluation from image data
Spatial decision support systems
Uncertainty

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Published Papers (9 papers)

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Research

20 pages, 6032 KiB

Open AccessArticle

Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors

by Edward A. Velasco Pereira, María A. Varo Martínez, Francisco J. Ruiz Gómez and Rafael M. Navarro-Cerrillo

Remote Sens. 2023, 15(13), 3430; https://doi.org/10.3390/rs15133430 - 6 Jul 2023

Cited by 7 | Viewed by 2057

Abstract

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R² = 0.59) by following the model using ALS data (R² = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R² = 0.50). The validation set showed that R² values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha⁻¹. It is noteworthy that the individual Sentinel 1 (R² = 0.49). and Landsat 8 (R² = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R² = 0.55 (validation R² = 0.70) and a RMSE of 9.93 Mg ha⁻¹. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests’ structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha⁻¹) and overestimated moderate biomass levels (30–45 Mg ha⁻¹). The AGB change map showed values ranging from gains of 43.3 Mg ha⁻¹ to losses of −68.8 Mg ha⁻¹ during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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21 pages, 4273 KiB

Open AccessArticle

Ecological Risk Assessment and Prediction Based on Scale Optimization—A Case Study of Nanning, a Landscape Garden City in China

by Jianjun Chen, Yanping Yang, Zihao Feng, Renjie Huang, Guoqing Zhou, Haotian You and Xiaowen Han

Remote Sens. 2023, 15(5), 1304; https://doi.org/10.3390/rs15051304 - 26 Feb 2023

Cited by 17 | Viewed by 2212

Abstract

Analysis and prediction of urban ecological risk are crucial means for resolving the dichotomy between ecological preservation and economic development, thereby enhancing regional ecological security and fostering sustainable development. This study uses Nanning, a Chinese landscape garden city, as an example. Based on spatial granularity and extent perspectives, using 30 m land use data, the optimal scale for an ecological risk assessment (ERA) and prediction is confirmed. This study also explores the patterns of spatial and temporal changes in ecological risk in Nanning on the optimal scale. At the same time, the Patch-generating Land Use Simulation model is used to predict Nanning’s ecological risk in 2036 under two scenarios and to propose ecological conservation recommendations in light of the study results. The study results show that: a spatial granularity of 120 m and a spatial extent of 7 km are the best scales for ERA and prediction in Nanning. Although the spatial distribution of ecological risk levels is obviously different, the overall ecological risk is relatively low, and under the scenario of ecological protection in 2036, the area of high ecological risk in Nanning is small. The results can provide theoretical support for ERA and the prediction of landscape cities and ecological civilization construction. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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18 pages, 5078 KiB

Open AccessArticle

Assessing Forest Landscape Stability through Automatic Identification of Landscape Pattern Evolution in Shanxi Province of China

by Bowen Hou, Caiyong Wei, Xiangnan Liu, Yuanyuan Meng and Xiaoyue Li

Remote Sens. 2023, 15(3), 545; https://doi.org/10.3390/rs15030545 - 17 Jan 2023

Cited by 6 | Viewed by 2314

Abstract

The evolution of forest landscape patterns can reveal the landscape stability of forest dynamics undergoing complex ecological processes. Analysis of forest landscape dynamics in regions under ecological restoration can evaluate the impact of large-scale afforestation on habitat quality and provide a scientific basis for achieving sustainable eco-environment development. In this study, a method for assessing forest landscape stability by characterizing changes in forest landscape patterns was proposed. Toeplitz inverse covariance-based clustering (TICC) was used to automatically identify landscape pattern evolution by investigating the synergistic changes of two landscape indices—forest cover area (CA) and patch density (PD)—and to extract the short-term processes—degradation, restoration, and stable—that took place between 1987 and 2021. Four long-term evolution modes, no change, increase, decrease, and wave, based on the temporal distribution of short-term change processes, were also defined to assess landscape stability. Our results showed that (i) the forest’s short-term change processes have various forms. The restoration subsequence was the largest and accounted for 46% of the total subsequence and existed in 75% of the landscape units. The time distribution of these three change processes showed that more landscape units have begun to transition into a stable state. (ii) The long-term change modes showed an aggregation distribution law and indicated that 57% of the landscape units were stable and 6.7% were unstable. Therefore, our study can provide a new perspective for the dynamic analysis of landscape patterns and offer insights for formulating better ecological restoration strategies. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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19 pages, 12057 KiB

Open AccessArticle

Spatio-Temporal Patterns and Driving Forces of Desertification in Otindag Sandy Land, Inner Mongolia, China, in Recent 30 Years

by Yang Yi, Mingchang Shi, Jie Wu, Na Yang, Chen Zhang and Xiaoding Yi

Remote Sens. 2023, 15(1), 279; https://doi.org/10.3390/rs15010279 - 3 Jan 2023

Cited by 8 | Viewed by 2981

Abstract

Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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22 pages, 9412 KiB

Open AccessArticle

Optimization of Characteristic Phenological Periods for Winter Wheat Extraction Using Remote Sensing in Plateau Valley Agricultural Areas in Hualong, China

by Shenghui Lv, Xingsheng Xia and Yaozhong Pan

Remote Sens. 2023, 15(1), 28; https://doi.org/10.3390/rs15010028 - 21 Dec 2022

Cited by 5 | Viewed by 1753

Abstract

It is important to develop or validate remote sensing methods to explore agricultural management and food self-sufficiency in the agricultural areas of the Qinghai–Tibet Plateau under the influence of global change, ecological protection, and socio-economic development. Studies on the use of remote sensing to monitor crop planting on the Qinghai-Tibetan Plateau are limited, with inconclusive results. Therefore, in this study, we analyzed Sentinel-2A/B images and field survey data in Hualong, China (located in Hehuang Valley, Qinghai-Tibetan Plateau) for winter wheat identification and verification at different spatial scales based on the time series of the normalized difference phenology index (NDPI) and dynamic time warping (DTW) algorithm. The characteristic phenological period and the corresponding DTW threshold were optimized using remote sensing data extracted for winter wheat. The results showed that NDPI corresponding to the jointing-heading stage, grouting-harvesting stage, and jointing-harvesting stage with DTW could identify winter wheat regardless of whether the spatial scale was a single quadrat, a combination of two quadrats, or the entire study area. The NDPI corresponding to the jointing-heading stage (corresponding DTW threshold T = 0.158) could generate a relatively rational winter wheat map; the NDPI corresponding to the time series of the grouting-harvesting stage (combined with DTW threshold T = 0.195) could detect a planting area with relatively high accuracy when supported by cultivated land, which matches the statistical reporting of the winter wheat area data. Similarly, with the support of cultivated land data, the planted area could be identified early based on the phenological characteristics of winter wheat before overwintering; however, the extraction scheme needs to be optimized further. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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24 pages, 6894 KiB

Open AccessArticle

Impact of Land Use/Land Cover Change on Ecological Quality during Urbanization in the Lower Yellow River Basin: A Case Study of Jinan City

by Guangting Yu, Tongwen Liu, Qi Wang, Tao Li, Xiujing Li, Guanhan Song and Yougui Feng

Remote Sens. 2022, 14(24), 6273; https://doi.org/10.3390/rs14246273 - 11 Dec 2022

Cited by 16 | Viewed by 2239

Abstract

Rapid urbanization in the lower Yellow River basin has greatly contributed to the socio-economic development of Northern China, but it has also exacerbated land use/land cover change, with significant impacts on ecology. Ecological quality is a comprehensive spatial and temporal measure of an ecosystem’s elements, structure and function, reflecting the ecological state under external pressures. However, how land use/land cover change affects the ecological quality during urbanization has rarely been explored. In this study, Jinan, a megacity in the lower Yellow River basin, was taken as a typical region, and the response of ecological quality to the land use/land cover change in 2000, 2010 and 2020 was retrieved using the remote sensing ecological index. For the mixed land use/land cover change types, a type-decomposition and spatial heterogeneity quantification method based on the abundance index was proposed, and the impact mechanisms of the land use/land cover change on the ecological quality were revealed by coupling with GeoDetector. The results show that: (1) Farmland and built-up areas, as the dominant land use/land cover types, were the primary factors controlling the spatial pattern of ecological quality. (2) Urban expansion and farmland protection policies resulted in the transfer of farmland and woodland to built-up areas as well as the transfer of woodland and grassland to farmland, which intensified the degradation of ecological quality. (3) Ecological protection policies prompted the transfer of farmland and grassland to woodland and the transfer of farmland to grassland as the main cause for the improvement of ecological quality. (4) Although ecological protection and urban development were implemented in parallel, uneven land use/land cover changes resulted in a 1.4 times expanded area of poorer ecological quality with increasingly serious spatial agglomeration effects. This study can provide scientific references for the ecological conservation and high-quality, sustainable development of cities in the lower Yellow River basin. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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15 pages, 3698 KiB

Open AccessArticle

Ecological Assessment of Terminal Lake Basins in Central Asia under Changing Landscape Patterns

by Wei Yan, Xiaofei Ma, Yuan Liu, Kaixuan Qian, Xiuyun Yang, Jiaxin Li and Yifan Wang

Remote Sens. 2022, 14(19), 4842; https://doi.org/10.3390/rs14194842 - 28 Sep 2022

Cited by 9 | Viewed by 2307

Abstract

Climate change and anthropogenic activities drive the shrinkage of terminal lakes in arid areas to varying degrees. Ecological water conveyance (EWC) projects have emerged globally to restore the ecology of terminal lakes. However, there remains a lack of qualitative evaluation of the benefits of EWC on terminal lakes. This study compared the Taitema Lake Basin with the Aral Sea Basin in Central Asia, representative of terminal lake basins with and without EWC, respectively. The results show that the water area of Taitema Lake increased by 7.23 km²/year due to EWC (2000–2019), whereas that of the Aral Sea Basin decreased by 98.21% over the entire process of natural evolution (1972–2019). Land use changes before and after the EWC (1990–2019) included an increase and decrease in desert land and water bodies in the Aral Sea Basin, and a decrease and increase in desert land and arable land in the Tarim River Basin, respectively. The normalized difference vegetation index (NDVI) and actual evaporation (ETa) are the main factors influencing the change in the water area of the Aral Sea Basin with the changing environment, while EWC is the main factor influencing the change in the water area of Taitema Lake. The results confirm that EWC is a feasible measure for achieving ecological restoration of a terminal lake watershed in an arid area. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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19 pages, 5705 KiB

Open AccessArticle

Assembling Cultural and Natural Values in Vernacular Landscapes: An Experimental Analysis

by Pablo Altaba, Juan A. García-Esparza and Anna Valentín

Remote Sens. 2022, 14(17), 4155; https://doi.org/10.3390/rs14174155 - 24 Aug 2022

Cited by 4 | Viewed by 2116

Abstract

Cultural landscapes can host natural and cultural areas. However, often, this distinction is not clear cut and the attempts to clarify this blur the character of landscapes. Vernacular landscapes today act as a living legacy, subject to transformation, preservation, or abandonment. This study analyses these legacies in order to evaluate elements and interactions. The research uses GIS with spatial and thematic databases of cultural heritage and natural habitats, as well as open data, historical cartography, citizen participation, and fieldwork information sources. In combination with GIS tools, LiDAR images helped in the dataset evaluation process. A priority scale of conservation for different areas was outlined through a process cataloguing the natural and cultural assets with conservation and intervention rubrics. These settings are classified according to their cultural and natural value, conservation, surrounding environment, and potential threats. The experimental methodology of this study aims to add new options for characterising vernacular landscapes by adding soft participatory values to datasets. These prove to be reliable complementary information, improving accuracy. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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30 pages, 31287 KiB

Open AccessArticle

UAV-Based Remote Sensing for Managing Alaskan Native Heritage Landscapes in the Yukon-Kuskokwim Delta

by Jonathan S. Lim, Sean Gleason, Meta Williams, Gonzalo J. Linares Matás, Daniel Marsden and Warren Jones

Remote Sens. 2022, 14(3), 728; https://doi.org/10.3390/rs14030728 - 4 Feb 2022

Cited by 12 | Viewed by 4282

Abstract

The Yukon-Kuskokwim (Y-K) Delta is home to the Alaskan Native Yup’ik people who have inhabited this remote, subarctic tundra for over 1500 years. Today, their ancestral lifeways and cultural landscapes are at risk from severe climate change-related threats. In turn, we propose that remote sensing technologies, particularly with sensors mounted on Unmanned Aerial Vehicle (UAV) platforms, are uniquely suited for protecting Yup’ik landscape heritage. Based on collaborative, community-based fieldwork in Quinhagak, AK, we present evidence that cultural sites—ranging from historic fishing camps to pre-contact winter villages—exhibit predictably atypical vegetation patterns based on the local ecological biome. Furthermore, these vegetation patterns can be recorded and statistically quantified through the analysis of multispectral imagery obtained from UAV-mounted sensors with three different false color composite rasters and vegetation indices depending on biome type. Finally, we suggest how the Yupiit can combine these methodologies/workflows with local knowledge to monitor the broader heritage landscape in the face of climate change. Full article

(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)

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