Analysis of Forest Landscape and Land-Use Based on Remote Sensing Technology

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (20 July 2021) | Viewed by 16502

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Guest Editor
Research Institute on Terrestrial Ecosystems (IRET-URT Lecce), National Research Council of Italy (CNR), Campus Ecotekne, 73100 Lecce, Italy
Interests: biodiversity; ecology; ecosystem services (ES); landscape and urban planning; strategic environmental assessment (SEA, Directive 2001/42/CE); geographic information systems (GIS)
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Special Issue Information

Dear Colleagues,

Ecosystem services are the main focus of sustainable land-use and forest management. The definition of ecosystem services is linked to the capacity of humanity to obtains one benefit from the natural or anthropic ecosystem. Documenting and interpreting the trends in plant growth at a global level is critical for understanding the associations and feedbacks between environmental change, land use, and ecosystem services.

Remote sensing is a useful set of techniques and tools of interpretation that allows for obtaining qualitative and quantitative information on objects located within the observation place. A key advantage of remote sensing is the capability to perform synoptic, spatially continuous, and frequent observations, resulting in large data volumes and multiple datasets at varying spatial and temporal resolutions. Remote sensing also allows for detecting the past land-use dynamics and disturbance events at local, regional, and global scales. One of the most important applications of remote sensing in the field of science and land-use management is the employment of vegetation indices. In particular, through the variation of vegetation indices, it is possible to analyse how the system can respond to specific stresses or inputs at different scales, from the single plant to the biome. Therefore, remote sensing allows for both discriminating the effects of one stressor or disturbance in space, but also understanding how the system responds to this disturbance in time, thanks to the possibility of constructing time series vegetation indices of the pre- and post-disturbance event. This allows for obtaining useful information to develop forest planning and management actions or, in general, in land-use actions based on a specific plant or ecosystem’s needs, with a significant reduction of natural resource uses and economic savings. Therefore, remote sensing can be one useful tool to maximize ecosystem services in a sustainable way.

The aim of this work is to develop new remote sensing analysis methodologies that allow for highlighting the cause and effect relationships between different forms of natural and anthropic disturbances or environmental changes, and the ecological functions fundamental for the production of ecosystem services like carbon sequestration, water cycling and regulation, soil fertility, and biomass productions.

Research can be transdisciplinary and involve different disciplines, such as ecology, physiology, botany, remote sensing, forestry, and others capable of contributing to linking and quantifying the causes and effects of stress or disturbances actions on the production of ecosystem services.

The contributions requested must be focused on methods that address the following:

  • Analyze the relationships between abiotic and biotic stresses or anthropic and natural disturbances, with specific ecological functions preferably included in the supporting ecosystem services.
  • Estimate the ecological or engineering resilience of natural systems at different scales, from the single plant to the entire biome.
  • Quantify or estimate the ecosystem benefits or services of specific ecological functions through field calibration.
  • Map the basic physiological processes of the plant or ecological functions of the ecosystem capable of providing useful indications in forestry management, such as the elimination of dead trees, emergency irrigation for first planting actions, pruning, and the use of fertilizers. This Issue can also be extended to agricultural actions.

Dr. Teodoro Semeraro
Guest Editor

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Keywords

  • Ecological functions monitoring
  • Ecosystem services quantification
  • Resilience analysis
  • Abiotic and biotic disturbances
  • Forest management
  • Land-use analysis
  • Spatial and temporal scale

Published Papers (4 papers)

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Research

18 pages, 7106 KiB  
Article
Analysis of Olive Grove Destruction by Xylella fastidiosa Bacterium on the Land Surface Temperature in Salento Detected Using Satellite Images
by Teodoro Semeraro, Riccardo Buccolieri, Marzia Vergine, Luigi De Bellis, Andrea Luvisi, Rohinton Emmanuel and Norbert Marwan
Forests 2021, 12(9), 1266; https://doi.org/10.3390/f12091266 - 16 Sep 2021
Cited by 8 | Viewed by 3356
Abstract
Agricultural activity replaces natural vegetation with cultivated land and it is a major cause of local and global climate change. Highly specialized agricultural production leads to extensive monoculture farming with a low biodiversity that may cause low landscape resilience. This is the case [...] Read more.
Agricultural activity replaces natural vegetation with cultivated land and it is a major cause of local and global climate change. Highly specialized agricultural production leads to extensive monoculture farming with a low biodiversity that may cause low landscape resilience. This is the case on the Salento peninsula, in the Apulia Region of Italy, where the Xylella fastidiosa bacterium has caused the mass destruction of olive trees, many of them in monumental groves. The historical land cover that characterized the landscape is currently in a transition phase and can strongly affect climate conditions. This study aims to analyze how the destruction of olive groves by X. fastidiosa affects local climate change. Land surface temperature (LST) data detected by Landsat 8 and MODIS satellites are used as a proxies for microclimate mitigation ecosystem services linked to the evolution of the land cover. Moreover, recurrence quantification analysis was applied to the study of LST evolution. The results showed that olive groves are the least capable forest type for mitigating LST, but they are more capable than farmland, above all in the summer when the air temperature is the highest. The differences in the average LST from 2014 to 2020 between olive groves and farmland ranges from 2.8 °C to 0.8 °C. Furthermore, the recurrence analysis showed that X. fastidiosa was rapidly changing the LST of the olive groves into values to those of farmland, with a difference in LST reduced to less than a third from the time when the bacterium was identified in Apulia six years ago. The change generated by X. fastidiosa started in 2009 and showed more or less constant behavior after 2010 without substantial variation; therefore, this can serve as the index of a static situation, which can indicate non-recovery or non-transformation of the dying olive groves. Failure to restore the initial environmental conditions can be connected with the slow progress of the uprooting and replacing infected plants, probably due to attempts to save the historic aspect of the landscape by looking for solutions that avoid uprooting the diseased plants. This suggests that social-ecological systems have to be more responsive to phytosanitary epidemics and adapt to ecological processes, which cannot always be easily controlled, to produce more resilient landscapes and avoid unwanted transformations. Full article
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16 pages, 3368 KiB  
Article
Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms
by Francesca Giannetti, Matteo Pecchi, Davide Travaglini, Saverio Francini, Giovanni D’Amico, Elia Vangi, Claudia Cocozza and Gherardo Chirici
Forests 2021, 12(6), 680; https://doi.org/10.3390/f12060680 - 26 May 2021
Cited by 23 | Viewed by 3961
Abstract
Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm hit the Northeast regions of Italy with wind gusts exceeding 200 km h [...] Read more.
Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm hit the Northeast regions of Italy with wind gusts exceeding 200 km h−1. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km2. The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019–October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and gmean for both BEAST and CCDC (SE < 3725.3 ha, SE% < 9.69, OA > 89.7, PA and UA > 0.87, gmean > 0.83). Full article
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16 pages, 4732 KiB  
Article
Fifty Years of Change in a Coniferous Forest in the Qilian Mountains, China—Advantages of High-Definition Remote Sensing
by Shu Fang and Zhibin He
Forests 2020, 11(11), 1188; https://doi.org/10.3390/f11111188 - 10 Nov 2020
Cited by 4 | Viewed by 1917
Abstract
Mountain ecosystems are significantly affected by climate change. However, due to slow vegetation growth in mountain ecosystems, climate-induced vegetation shifts are difficult to detect with low-definition remote sensing images. We used high-definition remote sensing data to identify responses to climate change in a [...] Read more.
Mountain ecosystems are significantly affected by climate change. However, due to slow vegetation growth in mountain ecosystems, climate-induced vegetation shifts are difficult to detect with low-definition remote sensing images. We used high-definition remote sensing data to identify responses to climate change in a typical Picea crassifolia Kom. forest in the Qilian Mountains, China, from 1968 to 2017. We found that: (1) Picea crassifolia Kom. forests were distributed in small patches or strips on shaded and partly shaded slopes at altitudes of 2700–3250 m, (2) the number, area, and concentration of forest patches have been increasing from 1968 to 2017 in relatively flat and partly sunny areas, but the rate of area increase and ascend of the tree line slowed after 2008, and (3) the establishment of plantation forests may be one of the reasons for the changes. The scale of detected change in Picea crassifolia Kom.forest was about or slightly below 30 m, indicating that monitoring with high-resolution remote sensing data will improve detectability and accuracy. Full article
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28 pages, 6419 KiB  
Article
Remote Sensing Applied in Forest Management to Optimize Ecosystem Services: Advances in Research
by Emilio Abad-Segura, Mariana-Daniela González-Zamar, Esteban Vázquez-Cano and Eloy López-Meneses
Forests 2020, 11(9), 969; https://doi.org/10.3390/f11090969 - 7 Sep 2020
Cited by 28 | Viewed by 6273
Abstract
Research Highlights: the wide variety of multispectral sensors that currently exist make it possible to improve the study of forest systems and ecosystem services. Background and Objectives: this study aims to analyze the current usefulness of remote sensing in forest management and ecosystem [...] Read more.
Research Highlights: the wide variety of multispectral sensors that currently exist make it possible to improve the study of forest systems and ecosystem services. Background and Objectives: this study aims to analyze the current usefulness of remote sensing in forest management and ecosystem services sciences, and to identify future lines of research on these issues worldwide during the period 1976–2019. Materials and Methods: a bibliometric technique is applied to 2066 articles published between 1976 and 2019 on these topics to find findings on scientific production and key subject areas. Results: scientific production has increased annually, so that in the last five years, 50.34% of all articles have been published. The thematic areas in which more articles were linked were environmental science, agricultural, and biological sciences, and earth and planetary sciences. Seven lines of research have been identified that generate contributions on this topic. In addition, the analysis of the relevance of the keywords has detected the ten main future directions of research. The growing worldwide trend of scientific production shows interest in developing aspects of this field of study. Conclusions: this study contributes to the academic, scientific, and institutional discussion to improve decision-making, and proposes new scenarios and uses of this technology to improve the administration and management of forest resources. Full article
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