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Applications of Remote Sensing to Forest Ecology and Environmental Monitoring

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 11317

Special Issue Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: remote sensing of vegetation ecosystem structure and functioning; land cover dynamics; ecoinformatics; ecosystem restoration and conservation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest ecosystem is the most active terrestrial ecosystem that possessing exceptional value for global biodiversity conservation, ecosystem services and human well-being. Forest degradation and biodiversity losses caused by land conversion and over-use have strongly increased since the mid-20th century in many forested localities globally. Meanwhile, forest transitions, defined as a shift from net deforestation to net reforestation, have occurred widely and rapidly over recent decades. Reforestations including both active tree planation and spontaneously natural regeneration are expected to have considerable potential to contribute to climate mitigation, biodiversity conservation, and sustainable development goals. Remote sensing technology has tremendous potential to map, quantify, and monitor forest change at various spatial and temporal scales. Advanced studies on applications of remote sensing to a broad scope of forest ecology and environmental monitoring will help people improve global forest management, and guide future ecosystem restoration and sustainable development.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Reviews on remote sensing applications to forest ecology and conservation.
  • Study on forest ecosystem dynamics (e.g., deforestation/degradation, greening, carbon sequestration) using multi-source remote sensing (e.g., unmanned aerial vehicle (UAV), airborne, and satellite) at local, regional, and global scales.
  • Remote sensing methods or tools (e.g., new models, metrics, and data processing applications) developed for forest ecological and environmental monitoring by addressing key ecological processes and policies on forest management, conservation, and restoration.
  • Study on forest ecosystems that relevant to nature-based solutions for climate change and biodiversity crisis by cooperating remote sensing with field surveys.
  • Quantitative estimation on essential biodiversity variables (EBVs) in forest ecosystems using remote sensing, field measurements, and machine learning.
  • Spatial-temporal analysis of forest functional traits and diversity and their associated environmental drivers.
  • High-resolution mapping on forest functional types, species types, and management types.

I look forward to receiving your contributions.

Dr. Wang Li
Guest Editor

Manuscript Submission Information

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Keywords

  • remote sensing
  • forest management
  • environmental monitoring
  • biodiversity
  • ecosystem restoration

Published Papers (5 papers)

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Research

28 pages, 13868 KiB  
Article
Delimitation of Ecological Corridor Using Technological Tools
by Vinícius Duarte Nader Mardeni, Henrique Machado Dias, Alexandre Rosa dos Santos, Daniel Medina Corrêa Santos, Tais Rizzo Moreira, Rita de Cássia Freire Carvalho, Elaine Cordeiro dos Santos, Clebson Pautz and Cecilia Uliana Zandonadi
Sustainability 2023, 15(18), 13696; https://doi.org/10.3390/su151813696 - 13 Sep 2023
Viewed by 983
Abstract
Ecological corridors are effective strategies to address the environmental consequences of forest fragmentation by connecting fragmented areas through various techniques. This study aims to propose the implementation of an ecological corridor in the Itapemirim River basin in Espírito Santo, Brazil. The specific objectives [...] Read more.
Ecological corridors are effective strategies to address the environmental consequences of forest fragmentation by connecting fragmented areas through various techniques. This study aims to propose the implementation of an ecological corridor in the Itapemirim River basin in Espírito Santo, Brazil. The specific objectives of this study are as follows: delimiting the Permanent Preservation Areas (APPs) in the Itapemirim River watershed and comparing land use and land cover within these areas. The MapBiomas platform and Landsat 8 satellite images were utilized to map land use and land cover, while the criteria set by Law No. 12.651 were followed to define the APP boundaries. The calculation of the landscape ecology indices and the identification of the forest fragments with the highest potential for ecological corridor implementation were conducted using the Fuzzy logic. The QGIS 3.26 application, along with the LecoS 3.0.1 plugin and Fragstats 4.2, were employed to characterize and quantify landscape ecology indices. The costs assessment and determination of the optimal route for implementing the ecological corridor were performed, considering bothdistance and physical impediments. The least cost path algorithm was utilized, taking into account land use and land cover, APP, fragment potential, slope, and subnormal clusters. Evaluating land costs and expropriation expenses required to define the ecological corridor in the study area. The identified forest fragments for inclusion in the ecological corridor were the Caparaó National Park, the Serra das Torres State Natural Monument, and other selected fragments based on the application of Fuzzy logic to landscape ecology indices. The corridor route was determined using the least cost path algorithm, considering various factors. This study revealed that the predominant land use and land cover class in the area is pasture, and a significant portion (68.58%) of the designated Permanent Preservation Areas were in conflict with legal regulations. The bare land value per hectare for pasture was the second highest among the land use and land cover categories, accounting for 64.28% of the total. The priority area analysis indicated that 31.86% of the region had high or very high importance for forest restoration, while 42.97% had low or very low priority. The findings demonstrate that the least cost path algorithm, coupled with a multi-criteria decision-making approach using the Analytic Hierarchy Process (AHP), is a valuable tool for planning and implementing ecological networks. These methods effectively consider critical factors in decision-making processes related to the optimal location of the ecological corridor. In conclusion, this study provides insights into the implementation of an ecological corridor in the Itapemirim River basin, emphasizing the importance of considering multiple factors and utilizing appropriate methodologies for effective decision-making in ecological planning. Full article
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20 pages, 11410 KiB  
Article
Urban and Peri-Urban Vegetation Monitoring Using Satellite MODIS NDVI Time Series, Singular Spectrum Analysis, and Fisher–Shannon Statistical Method
by Luciano Telesca, Michele Lovallo, Gianfranco Cardettini, Angelo Aromando, Nicodemo Abate, Monica Proto, Antonio Loperte, Nicola Masini and Rosa Lasaponara
Sustainability 2023, 15(14), 11039; https://doi.org/10.3390/su151411039 - 14 Jul 2023
Cited by 3 | Viewed by 1136
Abstract
The purpose of this work was to evaluate the potential of Singular Spectrum Analysis (SSA) and the Fisher–Shannon method to analyse NDVI MODIS time series and to capture and estimate inner vegetation anomalies in forest covers. In particular, the Fisher–Shannon method allows to [...] Read more.
The purpose of this work was to evaluate the potential of Singular Spectrum Analysis (SSA) and the Fisher–Shannon method to analyse NDVI MODIS time series and to capture and estimate inner vegetation anomalies in forest covers. In particular, the Fisher–Shannon method allows to calculate two quantities, the Fisher Information Measure (FIM) and the Shannon entropy power (SEP), which are used to characterise the complexity of a time series in terms of organisation/disorder. Pilot sites located both in urban (Milano, Torino, and Roma) and peri-urban areas (Appia Park, Castel Porziano, and Castel Volturno) were selected. Among the six sites, Roma, Castel Porziano, and Castel Volturno are affected by the parasite Toumeyella parvicornis. The time series was analysed using the products available in Google Earth Engine. To explore and characterise long-term vegetation dynamics, the time series was analysed using a multistep processing chain based on the (i) normalisation of the satellite time series, (ii) removal of seasonality and any other periodical cycles using SSA, (iii) analysis of the de-trended data using the Fisher–Shannon statistical method, and (iv) validation through comparison with independent data and ancillary information. Our findings point out to a clear discrimination between healthy and unhealthy sites, being the first (Milano, Torino, Appia) characterised by a larger FIM (lower SEP) and the second (Roma, Castel Porziano, Castel Volturno) by a lower FIM (larger SEP). The results of the investigations showed that the use of the SSA and Fisher–Shannon statistical methods coupled with the NDVI time series of the MODIS satellite made it possible to effectively identify and characterise subtle but physically significant signals veiled by seasonality and annual cycles. Full article
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14 pages, 6001 KiB  
Article
Deep Learning in Forest Tree Species Classification Using Sentinel-2 on Google Earth Engine: A Case Study of Qingyuan County
by Tao He, Houkui Zhou, Caiyao Xu, Junguo Hu, Xingyu Xue, Liuchang Xu, Xiongwei Lou, Kai Zeng and Qun Wang
Sustainability 2023, 15(3), 2741; https://doi.org/10.3390/su15032741 - 02 Feb 2023
Cited by 5 | Viewed by 3368
Abstract
Forest tree species information plays an important role in ecology and forest management, and deep learning has been used widely for remote sensing image classification in recent years. However, forest tree species classification using remote sensing images is still a difficult task. Since [...] Read more.
Forest tree species information plays an important role in ecology and forest management, and deep learning has been used widely for remote sensing image classification in recent years. However, forest tree species classification using remote sensing images is still a difficult task. Since there is no benchmark dataset for forest tree species, a forest tree species dataset (FTSD) was built in this paper to fill the gap based on the Sentinel-2 images. The FTSD contained nine kinds of forest tree species in Qingyuan County with 8,815 images, each with a resolution of 64 × 64 pixels. The images were produced by combining forest management inventory data and Sentinel-2 images, which were acquired with less than 20% clouds from 1 April to 31 October, including the years 2017, 2018, 2019, 2020, and 2021. Then, the images were preprocessed and downloaded from Google Earth Engine (GEE). Four different band combinations were compared in the paper. Moreover, a Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) were also calculated using the GEE. Deep learning algorithms including DenseNet, EfficientNet, MobileNet, ResNet, and ShuffleNet were trained and validated in the FTSD. RGB images with red, green, and blue (PC1, PC2, and NDVI) obtained the highest validation accuracy in four band combinations. ResNet obtained the highest validation accuracy in all algorithms after 500 epochs were trained in the FTSD, which reached 84.91%. As a famous and widely used remote sensing classification satellite imagery dataset, NWPU RESISC-45 was also trained and validated in the paper. ResNet achieved a high validation accuracy of 87.90% after training 100 epochs in NWPU RESISC-45. The paper shows in forest tree species classification based on remote sensing images and deep learning that (1) PCA and NDVI can be combined to improve the accuracy of classification; (2) ResNet is more suitable than other deep learning algorithms including DenseNet, EfficientNet, MobileNet, and ShuffleNet in remote sensing classification; and (3) being too shallow or deep in ResNet does not perform better in the FTSD, that is, 50 layers are better than 34 and 101 layers. Full article
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31 pages, 14973 KiB  
Article
Impacts of Land Use and Land Cover Changes on Land Surface Temperature over Cachar Region, Northeast India—A Case Study
by Kumar Ashwini and Briti Sundar Sil
Sustainability 2022, 14(21), 14087; https://doi.org/10.3390/su142114087 - 28 Oct 2022
Cited by 8 | Viewed by 2845
Abstract
The promptness of industrialisation and expanding urbanisation to achieve targets of economics are resulting in the transfiguration of permeable surfaces into impervious ones through LULC adaptation, leaving a herculean footprint on the ecosystem. The LULC escalates land surface temperature (LST), which further stimulates [...] Read more.
The promptness of industrialisation and expanding urbanisation to achieve targets of economics are resulting in the transfiguration of permeable surfaces into impervious ones through LULC adaptation, leaving a herculean footprint on the ecosystem. The LULC escalates land surface temperature (LST), which further stimulates urban heat islands (UHIs), ultimately remaining in tune with high levels of air pollution, energy use, and corresponding health hazards. The present evaluation first used Landsat TM/OLI satellite data to identify the labyrinth of the LULC rotation and, secondly, gauged its effects on the LST in the Cachar district of Assam, India, for the years 1990, 2000, 2010, and 2020. It embraces Cellular Automata (CA) and GIS methodologies to pull out the urbanization pattern and its ramifications in various LULC brackets of Cachar, India. It also embraces spatiotemporal LULC monitoring (1990–2020) and urban growth modelling (2030–2040). From the period 1990 to 2020, satellite-based LULC showed a net urban expansion of 269.43 km2 (7.13% increase). Some correlations were developed to show the relationship between spatial indices such as NDVI, NDBI, and NDWI with Land Surface Temperature (LST). Resultantly, a positive relation exists between LST and NDBI, but a negative correlation prevails between LST and NDVI, as well as NDWI. This evaluation will be of service to urban and environmental planners, providing them with detailed knowledge on how land cover is changing uniquely in northeast India. Full article
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23 pages, 4996 KiB  
Article
Geotechnology Applied to Analysis of Vegetation Dynamics and Occurrence of Forest Fires on Indigenous Lands in Cerrado-Amazonia Ecotone
by Felipe Gimenes Rodrigues Silva, Alexandre Rosa dos Santos, Nilton Cesar Fiedler, Juarez Benigno Paes, Rodrigo Sobreira Alexandre, Plinio Antonio Guerra Filho, Rosane Gomes da Silva, Marks Melo Moura, Evandro Ferreira da Silva, Samuel Ferreira da Silva, Saira G. de Oliveira Santos Rodrigues Silva, Raphael Maia Aveiro Cessa, Washington Amaral Ferreira and Fabio Gonçalves Marinho
Sustainability 2022, 14(11), 6919; https://doi.org/10.3390/su14116919 - 06 Jun 2022
Cited by 3 | Viewed by 2136
Abstract
The Cerrado-Amazonia Ecotone is one of the largest ecosystems in Brazil and is internationally considered a biodiversity hotspot. The occurrence of fires is common in these areas, directly affecting biomass losses and the reduction of vegetative vigor of forest typologies. Information obtained through [...] Read more.
The Cerrado-Amazonia Ecotone is one of the largest ecosystems in Brazil and is internationally considered a biodiversity hotspot. The occurrence of fires is common in these areas, directly affecting biomass losses and the reduction of vegetative vigor of forest typologies. Information obtained through remote sensing and geoprocessing can assist in the evaluation of vegetation behavior and its relation to the occurrence of forest fires. In this context, the objective of the present study was to analyze temporal vegetation dynamics, as well as their relationship with rainfall and fire occurrence on Indigenous lands, located in the Cerrado-Amazonia Ecotone of Mato Grosso state, Brazil. Normalized Difference Vegetation Index (NDVI) images of the MOD13Q1 MODIS product and burnt area of the MCD45A1 MODIS product, and rainfall images from the Tropical Rainfall Measuring Mission (TRMM) sensor were used. The period analyzed was from 2007 to 2016. After pre-processing the NDVI, TRMM and burnt area images, correlation analyses were performed between the rainfall, vegetation index and burnt area images, considering different lags (−3 to 3), to obtain the best response time for the variables. The analyses of inter-annual vegetation index trends were carried out following Mann–Kendall monotonic trend and seasonal trend analysis methodologies. Significant correlations were observed between NDVI and rainfall (R = 0.84), in grass regions and between NDVI and burnt area (R = −0.74). The Mann–Kendall monotonic trend indicates vegetation index stability with positive variations in grass regions. The analysis of seasonal trends identified different vegetation responses, with this biome presenting a diverse phytophysiognomy and seasonal vegetation with different phases for amplitudes. This variation is evidenced by the various phytophysiognomies and their responses in relation to biomass gains and losses. The correlation and regression of the NDVI and rainfall in the vegetation type of grass areas show that the burnt area tends to increase with the reduction of NDVI. Finally, no defined pattern of vegetation cycles or phases was observed in terms of seasonality and the proposed methodology can be adapted to other world biomes. Full article
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