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Monitoring of Forest Ecological Environment Based on Remote Sensing Technology

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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 (31 March 2021) | Viewed by 32757

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Special Issue Editor

Dr. Yasumasa Hirata

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Guest Editor

Forestry and Forest Products Research Institute, Tsukuba 305-8687, Japan
Interests: forest eclogy; forest ecosystems services; change detection; airborne LiDAR; forest mapping; forest structrue; indivudual tree detection

Special Issue Information

Dear Colleagues,

Forests play important roles in the production of timber and nontimber forest products, biodiversity conservation of genes, species, and ecosystems, mitigation of climate change, and so on, and they provide various benefits called ecosystem services to human life. However, due to the expansion of human activities and the impact of climate change, forests are rapidly decreasing, and the remaining forests are deteriorating. In order to control the progress of deforestation and forest degradation and to maximize forest ecosystem services, it is necessary to properly monitor forest ecological environments.

The progress of machine learning, including deep learning, and the spread of big data processing technology, such as Google Earth Engine, have made it possible to evaluate forest ecological environments and their changes widely and in detail. In addition, the forest condition can be monitored in detail by restoring the three-dimensional structure of the forest from drone photographs via SfM methods or airborne LiDAR. Moreover, terrestrial LiDAR can visualize the detailed situation in the forest.

In this Special Issue of Remote Sensing, I welcome original and innovative research papers focusing on monitoring of the forest ecological environment and its change from local to global scales based on novel remote sensing technology.

Dr. Yasumasa Hirata
Guest Editor

Manuscript Submission Information

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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

Forest monitoring
Change detection
Ecological environment
Ecosystem services
Time-series analysis
3D structure of forest
Optical remote sensing
LiDAR remote sensing
SfM

Published Papers (9 papers)

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25 pages, 12655 KiB

Open AccessArticle

Correction of Terrain Effects on Forest Canopy Height Estimation Using ICESat-2 and High Spatial Resolution Images

by Bin Li, Tianzhong Zhao, Xiaohui Su, Guangpeng Fan, Wenjie Zhang, Zhuo Deng and Yonghui Yu

Remote Sens. 2022, 14(18), 4453; https://doi.org/10.3390/rs14184453 - 7 Sep 2022

Cited by 5 | Viewed by 1946

Abstract

The Ice, Cloud, and Land Elevation Satellite–2 (ICESat–2) carries the Advanced Topographic Laser Altimeter System (ATLAS), enabling global canopy height measurements from forest canopy height models (CHMs). Topographic slope is a crucial factor affecting the accuracy of canopy height estimates from ICESat–2 CHMs, but it has not been sufficiently studied. This paper aims to eliminate the influence of slope on canopy height estimates from ICESat–2 data and establishes a method for correcting forest canopy heights based on high spatial resolution digital orthophoto maps (DOM). The cross-track photons are corrected horizontally to eliminate the estimation error. Multi-resolution segmentation is used to segment tree crowns in the DOM, and the distance and relative position between the top of canopy (TOC) photons and the center point of the crown are calculated. TOC photon correction rules are established for different terrains, and the vertical error of the TOC photons is corrected. The results indicate that the vertical error increases exponentially with the slope. The cross-track photon correction and the TOC photon correction method eliminate the effect of slope on canopy height estimates. The cross-track photon correction method reduces the mean absolute error (MAE) and root mean square error (RMSE) of the canopy height estimates by 35.71% and 35.98%, respectively. The TOC photon correction approach further reduces the MAE and RMSE by 23% and 19.23%, respectively. The proposed method has significantly higher accuracy for forest canopy height estimation using ICESat–2 data than the traditional method. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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19 pages, 4790 KiB

Open AccessArticle

46-Year (1973–2019) Permafrost Landscape Changes in the Hola Basin, Northeast China Using Machine Learning and Object-Oriented Classification

by Raul-David Șerban, Mihaela Șerban, Ruixia He, Huijun Jin, Yan Li, Xinyu Li, Xinbin Wang and Guoyu Li

Remote Sens. 2021, 13(10), 1910; https://doi.org/10.3390/rs13101910 - 13 May 2021

Cited by 18 | Viewed by 3251

Abstract

Land use and cover changes (LUCC) in permafrost regions have significant consequences on ecology, engineered systems, and the environment. Obtaining more details about LUCC is crucial for sustainable development, land conservation, and environment management. The Hola Basin (957 km²) in the northernmost part of Northeast China, a boreal forest landscape underlain by discontinuous, sporadic, and isolated permafrost, was selected for the case study. The LUCC was analyzed using the Landsat archive of satellite images from 1973 to 2019. A thematic change detection analysis was performed by combining the object-based image analysis (OBIA) and the Support Vector Machine (SVM) algorithm. Four types of LUCC (forest, grass, water, and anthropic) were extracted with an overall accuracy of 80% for 1973 and >90% for 1986, 2000, and 2019. Forest, the dominant class (750 km² in 1973), declined by 88 km² (11.8%) from 1973 to 1986 but had a recovery of 78 km² (12.5%) from 2000 to 2019. Grass, the second-largest class (187 km² in 1973), increased by 86 km² (46.5%) between 1973 and 1986 and decreased by 90 km² (40%) between 2000 and 2019. The anthropic class continuously increased from 10 km² (1973) to 37 km² (2019). Major features in LUCC are attributed to rapid population growth, resource exploitation, agriculture intensification, economic development, and frequent forest fires. Under a pronounced climate warming, these drivers have been accelerating the degradation of permafrost, subsequently triggering natural hazards and deteriorating the ecological environment. This study represents a benchmark for sustainable LUCC management in the Hola Basin, Northeast China. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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20 pages, 6087 KiB

Open AccessArticle

A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

by Kaisen Ma, Yujiu Xiong, Fugen Jiang, Song Chen and Hua Sun

Remote Sens. 2021, 13(8), 1442; https://doi.org/10.3390/rs13081442 - 8 Apr 2021

Cited by 16 | Viewed by 3958

Abstract

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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20 pages, 4782 KiB

Open AccessArticle

Analysis and Prediction of Gap Dynamics in a Secondary Deciduous Broadleaf Forest of Central Japan Using Airborne Multi-LiDAR Observations

by Kazuho Araki and Yoshio Awaya

Remote Sens. 2021, 13(1), 100; https://doi.org/10.3390/rs13010100 - 30 Dec 2020

Cited by 2 | Viewed by 2074

Abstract

Gaps are important for growth of vegetation on the forest floor. However, monitoring of gaps in large areas is difficult. Airborne light detection and ranging (LiDAR) data make precise gap mapping possible. We formulated a method to describe changes in gaps by time-series tracking of gap area changes using three digital canopy height models (DCHMs) based on LiDAR data collected in 2005, 2011, and 2016 over secondary deciduous broadleaf forest. We generated a mask that covered merging or splitting of gaps in the three DCHMs and allowed us to identify their spatiotemporal relationships. One-fifth of gaps merged with adjacent gaps or split into several gaps between 2005 and 2016. Gap shrinkage showed a strong linear correlation with gap area in 2005, via lateral growth of gap-edge trees between 2005 and 2016, as modeled by a linear regression analysis. New gaps that emerged between 2005 and 2011 shrank faster than gaps present in 2005. A statistical model to predict gap lifespan was developed and gap lifespan was mapped using data from 2005 and 2016. Predicted gap lifespan decreased greatly due to shrinkage and splitting of gaps between 2005 and 2016. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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25 pages, 5520 KiB

Open AccessArticle

Tree Line Identification and Dynamics under Climate Change in Wuyishan National Park Based on Landsat Images

by Dandan Xu, Qinghong Geng, Changshan Jin, Zikun Xu and Xia Xu

Remote Sens. 2020, 12(18), 2890; https://doi.org/10.3390/rs12182890 - 6 Sep 2020

Cited by 10 | Viewed by 4537

Abstract

The alpine tree line ecotone, reflecting interactions between climate and ecology, is very sensitive to climate change. To identify tree line responses to climate change, including intensity and local variations in tree line advancement, the use of Landsat images with long-term data series and fine spatial resolution is an option. However, it is a challenge to extract tree line data from Landsat images due to classification issues with outliers and temporal inconsistency. More importantly, direct classification results in sharp boundaries between forest and non-forest pixels/segments instead of representing the tree line ecotone (three ecological regions—tree species line, tree line, and timber line—are closely related to the tree line ecotone and are all significant for ecological processes). Therefore, it is important to develop a method that is able to accurately extract the tree line from Landsat images with a high temporal consistency and to identify the appropriate ecological boundary. In this study, a new methodology was developed based on the concept of a local indicator of spatial autocorrelation (LISA) to extract the tree line automatically from Landsat images. Tree line responses to climate change from 1987 to 2018 in Wuyishan National Park, China, were evaluated, and topographic effects on local variations in tree line advancement were explored. The findings supported the methodology based on the LISA concept as a valuable classifier for assessing the local spatial clusters of alpine meadows from images acquired in nongrowing seasons. The results showed that the automatically extracted line from Landsat images was the timber line due to the restriction in spatial autocorrelation. The results also indicate that parts of the tree line in the study area shifted upward vertically by 50 m under a 1 °C temperature increase during the period from 1987 to 2018, with local variations influenced by slope, elevation, and interactions with aspect. Our study contributes a novel result regarding the response of the alpine tree line to global warming in a subtropical region. Our method for automatic tree line extraction can provide fundamental information for ecosystem managers. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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13 pages, 3640 KiB

Open AccessArticle

Causal Analysis of Accuracy Obtained Using High-Resolution Global Forest Change Data to Identify Forest Loss in Small Forest Plots

by Yusuke Yamada, Toshihiro Ohkubo and Katsuto Shimizu

Remote Sens. 2020, 12(15), 2489; https://doi.org/10.3390/rs12152489 - 3 Aug 2020

Cited by 9 | Viewed by 3342

Abstract

Identifying areas of forest loss is a fundamental aspect of sustainable forest management. Global Forest Change (GFC) datasets developed by Hansen et al. (in Science 342:850–853, 2013) are publicly available, but the accuracy of these datasets for small forest plots has not been assessed. We used a forest-wide polygon-based approach to assess the accuracy of using GFC data to identify areas of forest loss in an area containing numerous small forest plots. We evaluated the accuracy of detection of individual forest-loss polygons in the GFC dataset in terms of a “recall ratio”, the ratio of the spatial overlap of a forest-loss polygon determined from the GFC dataset to the area of a corresponding reference forest-loss polygon, which we determined by visual interpretation of aerial photographs. We analyzed the structural relationships of recall ratio with area of forest loss, tree species, and slope of the forest terrain by using linear non-Gaussian acyclic modelling. We showed that only 11.1% of forest-loss polygons in the reference dataset were successfully identified in the GFC dataset. The inferred structure indicated that recall ratio had the strongest relationships with area of forest loss, forest tree species, and height of the forest canopy. Our results indicate the need for careful consideration of structural relationships when using GFC datasets to identify areas of forest loss in regions where there are small forest plots. Moreover, further studies are required to examine the structural relationships for accuracy of land-use classification in forested areas in various regions and with different forest characteristics. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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15 pages, 5464 KiB

Open AccessArticle

Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

by Katsuto Shimizu, Tetsuji Ota, Nobuya Mizoue and Hideki Saito

Remote Sens. 2020, 12(11), 1876; https://doi.org/10.3390/rs12111876 - 9 Jun 2020

Cited by 11 | Viewed by 4987

Abstract

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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19 pages, 4603 KiB

Open AccessArticle

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

by Vanessa Sousa da Silva, Carlos Alberto Silva, Midhun Mohan, Adrián Cardil, Franciel Eduardo Rex, Gabrielle Hambrecht Loureiro, Danilo Roberti Alves de Almeida, Eben North Broadbent, Eric Bastos Gorgens, Ana Paula Dalla Corte, Emanuel Araújo Silva, Rubén Valbuena and Carine Klauberg

Remote Sens. 2020, 12(9), 1438; https://doi.org/10.3390/rs12091438 - 1 May 2020

Cited by 23 | Viewed by 5099

Abstract

Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R² with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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Other

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12 pages, 3255 KiB

Open AccessLetter

Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data

by Qu Zhou, Xianghan Sun, Liqiao Tian, Jian Li and Wenkai Li

Remote Sens. 2020, 12(2), 274; https://doi.org/10.3390/rs12020274 - 14 Jan 2020

Cited by 3 | Viewed by 2405

Abstract

Accurate monitoring of plant phenology is vital to effective understanding and prediction of the response of vegetation ecosystems to climate change. Satellite remote sensing is extensively employed to monitor vegetation phenology. However, fall phenology, such as peak foliage coloration, is less well understood compared with spring phenological events, and is mainly determined using the vegetation index (VI) time-series. Each VI only emphasizes a single vegetation property. Thus, selecting suitable VIs and taking advantage of multiple spectral signatures to detect phenological events is challenging. In this study, a novel grouping-based time-series approach for satellite remote sensing was proposed, and a wide range of spectral wavelengths was considered to monitor the complex fall foliage coloration process with simultaneous changes in multiple vegetation properties. The spatial and temporal scale effects of satellite data were reduced to form a reliable remote sensing time-series, which was then divided into groups, namely pre-transition, transition and post-transition groups, to represent vegetation dynamics. The transition period of leaf coloration was correspondingly determined to divisions with the smallest intra-group and largest inter-group distances. Preliminary results using a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2013 at the Harvard Forest (spatial scale: ~3500 m; temporal scale: ~8 days) demonstrated that the method can accurately determine the coloration period (correlation coefficient: 0.88; mean absolute difference: 3.38 days), and that the peak coloration periods displayed a shifting trend to earlier dates. The grouping-based approach shows considerable potential in phenological monitoring using satellite time-series. Full article

(This article belongs to the Special Issue Monitoring of Forest Ecological Environment Based on Remote Sensing Technology)

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