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Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management

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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 (30 November 2021) | Viewed by 70218

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Special Issue Editors

Prof. Dr. Dieu Tien Bui

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Guest Editor

Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Notodden, Norway
Interests: sensors; LiDAR; GIS and geospatial technology; geo-hazards; artificial intelligence; soil engineering; marine geology; environmental managements
Special Issues, Collections and Topics in MDPI journals

Dr. Tien Dat Pham

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Guest Editor

Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
Interests: forest monitoring; forest conservation; applied remote sensing; machine learning; REDD+
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in computer vision, pattern recognition, and artificial intelligence (AI) technologies have resulted in the development of new machine learning, geospatial data mining techniques, and allowing the monitoring of forest ecosystems with higher accuracy. Earth observation (e.g., optical, SAR, UAV, and LiDAR) data provides an important tool for monitoring forests and identifying attributes such as species, biomass, and carbon stocks. Advanced machine learning and remote sensing approaches offer a way to reduce the uncertainty in estimates of forest ecosystem service loss, and are needed for the monitoring, reporting, and verification (MRV) of international conservation programs such as Reducing Emissions from Deforestation and Forest Degradation (REDD+).

In this context, this Special Issue encourages authors to share recent advances in forest modeling, tree species, structure, biomass and carbon stock estimation, and sustainable conservation and management, with an emphasis on issues addressed by means of advanced geospatial artificial intelligence. This is an emerging scientific multidiscipline, which combines innovations in geospatial technology, remote sensing, UAV photogrammetry, advanced artificial intelligence techniques (i.e., deep learning, data mining, hybrid and ensemble techniques, tensor learning for classification and regression tasks, meta-heuristic optimization, and high-performance computing) to extract knowledge from geospatial data.

Advanced image processing techniques based on multi-modality datasets, data fusion using state-of-the-art data mining, machine learning, and deep learning techniques (neural networks, ensemble learning, tensor learning, artificial intelligence, automatic learning) can also be applied for an accurate investigation. Numerous models can be proposed to identify and effectively monitor problems with a special focus on the conservation and management of sustainable natural resources.

We kindly invite the scientific community to contribute novel and original research to this Special Issue addressing at least one of the following topics:

Recent advances in geospatial technology, remote sensing, UAV photogrammetry, and machine learning for forest monitoring;
Recent advances in geospatial artificial intelligence for forest aboveground biomass and carbon stock estimation;
Recent advances in geospatial artificial intelligence for forest fire prediction;
Recent advances in the temporal dynamics of forest change;
Recent advances in monitoring tree species and structure;
Recent advances in data fusion techniques for forest monitoring;
Recent advances in geospatial artificial intelligence for forest conservation and management;
Real-world case studies with findings of clear interest to the scientific community.

Finally, authors are encouraged to share codes and data so that their studies can be easily reproduced and serve as a seed for future improvements.

Prof. Dr. Dieu Tien Bui
Dr. Tien Dat Pham
Guest Editors

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Keywords

Tree species
Forest biomass
Carbon stocks
Forest Fire
Artificial Intelligence
Remote Sensing
Hybrid and ensemble
Optimization
REDD+.

Published Papers (12 papers)

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Research

20 pages, 4832 KiB

Open AccessArticle

A Deep Fusion uNet for Mapping Forests at Tree Species Levels with Multi-Temporal High Spatial Resolution Satellite Imagery

by Ying Guo, Zengyuan Li, Erxue Chen, Xu Zhang, Lei Zhao, Enen Xu, Yanan Hou and Lizhi Liu

Remote Sens. 2021, 13(18), 3613; https://doi.org/10.3390/rs13183613 - 10 Sep 2021

Cited by 8 | Viewed by 3902

Abstract

It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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32 pages, 11435 KiB

Open AccessArticle

Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest

by Heikki Astola, Lauri Seitsonen, Eelis Halme, Matthieu Molinier and Anne Lönnqvist

Remote Sens. 2021, 13(12), 2392; https://doi.org/10.3390/rs13122392 - 18 Jun 2021

Cited by 26 | Viewed by 4203

Abstract

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (<30 m) satellites together with modern machine learning algorithms provide new prospects for spaceborne large area forest inventory. In this study, we investigated the capability of Sentinel-2 (S2) image and metadata, topography data, and canopy height model (CHM), as well as their combinations, to predict growing stock volume with deep neural networks (DNN) in four forestry districts in Central Finland. We focused on investigating the relevance of different input features, the effect of DNN depth, the amount of training data, and the size of image data sampling window to model prediction performance. We also studied model transfer between different silvicultural districts in Finland, with the objective to minimize the amount of new field data needed. We used forest inventory data provided by the Finnish Forest Centre for model training and performance evaluation. Leaving out CHM features, the model using RGB and NIR bands, the imaging and sun angles, and topography features as additional predictive variables obtained the best plot level accuracy (RMSE% = 42.6%,

| B I A S % |

= 0.8%). We found

3 \times 3

pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (

| B I A S % |

= 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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19 pages, 9916 KiB

Open AccessArticle

Comparison of Numerical Calculation Methods for Stem Diameter Retrieval Using Terrestrial Laser Data

by Lei You, Jie Wei, Xiaojun Liang, Minghua Lou, Yong Pang and Xinyu Song

Remote Sens. 2021, 13(9), 1780; https://doi.org/10.3390/rs13091780 - 02 May 2021

Cited by 5 | Viewed by 2496

Abstract

Terrestrial laser scanning (TLS) can be used as a millimeter-level measurement tool for forest inventories. However, the stem diameter retrieval accuracy in sample plot scanning is not yet convincing. The errors in each step of stem diameter retrieval algorithms must be evaluated. In this study, six numerical calculation methods for the numerical calculation step, i.e., cylinder fitting (CYF), circle fitting (CF), convex hull line fitting (CLF), the proposed caliper simulation method (CSM), closure B-spline curve fitting (SP) and closure Bézier curve fitting with global convexity (SPC), were applied to stem diameter retrieval, and the similarities and differences were evaluated. The ovality, completeness and roughness were used to evaluate the stem slice point cloud quality. A total of 165 stem slice point clouds at breast height collected from three Larix kaempferi plots were used. Compared with the field-measured stem diameters at breast height (DBHs), the root mean square errors (RMSEs) of the CYF, CF, CLF, CSM, SP and SPC methods were 0.30 cm, 0.30 cm, 0.51 cm, 0.51 cm, 0.56 cm and 0.54 cm, respectively. Compared with the SPC method results, the RMSE of the CSM results was 0.05 cm. The results illustrated that the CYF and CF methods performed the same, as did the CLF and CSM methods. Most DBHs retrieved by the CYF and CF methods were smaller than the field-measured DBHs, and most DBHs retrieved by the CLF, CSM, SP and SPC methods were larger than the field-measured DBHs. This study demonstrated that the CYF and CF methods perform the best and are the most robust, and the measurements by a diameter tape and a caliper are similar enough for forestry inventories. Evaluating and preprocessing stem slice point clouds is a potential way to improve stem diameter retrieval accuracy. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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26 pages, 14189 KiB

Open AccessArticle

Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

by Kinh Bac Dang, Manh Ha Nguyen, Duc Anh Nguyen, Thi Thanh Hai Phan, Tuan Linh Giang, Hoang Hai Pham, Thu Nhung Nguyen, Thi Thuy Van Tran and Dieu Tien Bui

Remote Sens. 2020, 12(19), 3270; https://doi.org/10.3390/rs12193270 - 08 Oct 2020

Cited by 32 | Viewed by 5219

Abstract

The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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45 pages, 12494 KiB

Open AccessArticle

Automated Method for Delineating Harvested Stands Based on Harvester Location Data

by Timo Melkas, Kirsi Riekki and Juha-Antti Sorsa

Remote Sens. 2020, 12(17), 2754; https://doi.org/10.3390/rs12172754 - 25 Aug 2020

Cited by 2 | Viewed by 3110

Abstract

The data produced by cut-to-length harvesters provide new large-scale data source for event-based update of national forest stand inventory by Finnish Forest Centre. This study aimed to automate geoprocessing, which generates delineations of operated areas from harvester location data. Automated algorithms were developed and tested with a dataset of 455 harvested objects, recorded during harvestings. In automated stand delineation, the location points are clustered, the stand points are identified and external strip roads are separated. Then, stand polygons are produced. To validate the results, automatic delineations were compared to 57 observed delineations from field measurements and aerial images. A detailed comparison method was developed to study the correspondence. Stand polygonization parameter was adjusted and areal correspondence with 1% error on average was obtained for stands over 0.75 ha. Good stand shape agreement was observed. Overall, the automated method worked well, and the operative stand delineations were found suitable for updating the forest inventory data. To modify the operative stands towards forest inventory stands, a balancing algorithm is introduced to create a solid, unique stand boundary between overlapping stands. This algorithm is beneficial for upkeep of stand networks. In addition, the Global Navigation Satellite System (GNSS) accuracy of the harvesters was examined and estimated numerically. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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25 pages, 9726 KiB

Open AccessArticle

New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring

by Thanh Tung Hoang, Van Thinh Truong, Masato Hayashi, Takeo Tadono and Kenlo Nishida Nasahara

Remote Sens. 2020, 12(17), 2707; https://doi.org/10.3390/rs12172707 - 21 Aug 2020

Cited by 12 | Viewed by 5979

Abstract

Highly detailed and accurate forest maps are important for various applications including forest monitoring, forestry policy, climate change, and biodiversity loss. This study demonstrates a comprehensive and geographically transferable approach to produce a 12 category high-resolution land use/land cover (LULC) map over mainland Vietnam in 2016 by remote sensing data. The map included several natural forest categories (evergreen broadleaf, deciduous (mostly deciduous broadleaf), and coniferous (mostly evergreen coniferous)) and one category representing all popular plantation forests in Vietnam such as acacia (Acacia mangium, Acacia auriculiformis, Acacia hybrid), eucalyptus (Eucalyptus globulus), rubber (Hevea brasiliensis), and others. The approach combined the advantages of various sensor data by integrating their posterior probabilities resulting from applying a probabilistic classifier (comprised of kernel density estimation and Bayesian inference) to each datum individually. By using different synthetic aperture radar (SAR) images (PALSAR-2/ScanSAR, PALSAR-2 mosaic, Sentinel-1), optical images (Sentinel-2, Landsat-8) and topography data (AW3D30), the resultant map achieved 85.6% for the overall accuracy. The major forest classes including evergreen broadleaf forests and plantation forests had a user’s accuracy and producer’s accuracy ranging from 86.0% to 95.3%. Our map identified 9.55 × 10⁶ ha (±0.16 × 10⁶ ha) of natural forests and 3.89 × 10⁶ ha (±0.11 × 10⁶ ha) of plantation forests over mainland Vietnam, which were close to the Vietnamese government’s statistics (with differences of less than 8%). This study’s result provides a reliable input/reference to support forestry policy and land sciences in Vietnam. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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22 pages, 4770 KiB

Open AccessArticle

Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables

by Joongbin Lim, Kyoung-Min Kim, Eun-Hee Kim and Ri Jin

Remote Sens. 2020, 12(12), 2049; https://doi.org/10.3390/rs12122049 - 25 Jun 2020

Cited by 16 | Viewed by 4071

Abstract

The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after the Korean War. In this study, we developed a model to classify the five dominant tree species in North Korea (Korean red pine, Korean pine, Japanese larch, needle fir, and Oak) using satellite data and machine-learning techniques. The model was applied to the Gwangneung Forest area in South Korea; the Mt. Baekdu area of China, which borders North Korea; and to Goseong-gun, at the border of South Korea and North Korea, to evaluate the model’s applicability to North Korea. Eighty-three percent accuracy was achieved in the classification of the Gwangneung Forest area. In classifying forest types in the Mt. Baekdu area and Goseong-gun, even higher accuracies of 91% and 90% were achieved, respectively. These results confirm the model’s regional applicability. To expand the model for application to North Korea, a new model was developed by integrating training data from the three study areas. The integrated model’s classification of forest types in Goseong-gun (South Korea) was relatively accurate (80%); thus, the model was utilized to produce a map of the predicted dominant tree species in Goseong-gun (North Korea). Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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26 pages, 11003 KiB

Open AccessArticle

Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods

by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki and Soo-Mi Choi

Remote Sens. 2020, 12(10), 1689; https://doi.org/10.3390/rs12101689 - 25 May 2020

Cited by 49 | Viewed by 5507

Abstract

This study aimed to prepare forest fire susceptibility mapping (FFSM) using a ubiquitous GIS and an ensemble of adaptive neuro fuzzy interface system (ANFIS) with genetic (GA) and simulated annealing (SA) algorithms (ANFIS-GA-SA) and an ensemble of radial basis function (RBF) with an imperialist competitive algorithm (ICA) (RBF-ICA) model in Chaharmahal and Bakhtiari Province, Iran. The forest fire areas were determined using MODIS satellite imagery and a field survey. The modeling and validation of the models were performed with 70% (183 locations) and 30% (79 locations) of forest fire locations (262 locations), respectively. In order to prepare the FFSM, 10 criteria were then used, namely altitude, rainfall, slope angle, temperature, slope aspect, wind effect, distance to roads, land use, distance to settlements and soil type. After the FFSM was prepared, the maps were designed and implemented for web GIS and mobile application. A receiver operating characteristic (ROC)- area under the curve (AUC) index was used to validate the prepared maps. The ROC-AUC results showed an accuracy of 0.903 for the ANFIS-GA-SA model and an accuracy of 0.878 for the RBF-ICA model. The results of the spatial autocorrelation showed that the occurrence of fire in the study area has a cluster distribution and most of the spatial dependence is related to the distance to settlement, soil and rainfall variables. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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24 pages, 3851 KiB

Open AccessArticle

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

by Tien Dat Pham, Naoto Yokoya, Junshi Xia, Nam Thang Ha, Nga Nhu Le, Thi Thu Trang Nguyen, Thi Huong Dao, Thuy Thi Phuong Vu, Tien Duc Pham and Wataru Takeuchi

Remote Sens. 2020, 12(8), 1334; https://doi.org/10.3390/rs12081334 - 23 Apr 2020

Cited by 79 | Viewed by 11690

Abstract

This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R²) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R² = 0.683, RMSE = 25.08 Mg·ha⁻¹) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha⁻¹ to 142 Mg·ha⁻¹ (with an average of 72.47 Mg·ha⁻¹). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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16 pages, 42541 KiB

Open AccessArticle

A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga Harbor, New Zealand

by Nam Thang Ha, Merilyn Manley-Harris, Tien Dat Pham and Ian Hawes

Remote Sens. 2020, 12(3), 355; https://doi.org/10.3390/rs12030355 - 21 Jan 2020

Cited by 64 | Viewed by 8678

Abstract

Seagrass has been acknowledged as a productive blue carbon ecosystem that is in significant decline across much of the world. A first step toward conservation is the mapping and monitoring of extant seagrass meadows. Several methods are currently in use, but mapping the resource from satellite images using machine learning is not widely applied, despite its successful use in various comparable applications. This research aimed to develop a novel approach for seagrass monitoring using state-of-the-art machine learning with data from Sentinel–2 imagery. We used Tauranga Harbor, New Zealand as a validation site for which extensive ground truth data are available to compare ensemble machine learning methods involving random forests (RF), rotation forests (RoF), and canonical correlation forests (CCF) with the more traditional maximum likelihood classifier (MLC) technique. Using a group of validation metrics including F1, precision, recall, accuracy, and the McNemar test, our results indicated that machine learning techniques outperformed the MLC with RoF as the best performer (F1 scores ranging from 0.75–0.91 for sparse and dense seagrass meadows, respectively). Our study is the first comparison of various ensemble-based methods for seagrass mapping of which we are aware, and promises to be an effective approach to enhance the accuracy of seagrass monitoring. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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25 pages, 6877 KiB

Open AccessArticle

JAXA Annual Forest Cover Maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and Auxiliary Data

by Van Thinh Truong, Thanh Tung Hoang, Duong Phan Cao, Masato Hayashi, Takeo Tadono and Kenlo Nishida Nasahara

Remote Sens. 2019, 11(20), 2412; https://doi.org/10.3390/rs11202412 - 17 Oct 2019

Cited by 11 | Viewed by 8066

Abstract

Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi-temporal PALSAR-2/ScanSAR images, multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images, and Shuttle Radar Topography Mission (SRTM) images to map annual forest cover for mainland Vietnam during 2015–2018. Average overall accuracies of our forest/non-forest (FNF) maps (86.6% ± 3.1%) were greater than recent maps of Japan Aerospace Exploration Agency (JAXA, (77.5% ± 3.2%)) and European Space Agency (ESA, (85.4% ± 1.6%)). Our estimates of mainland Vietnam’s forest area were close to that of the Vietnamese government. A comparison of the spatial distribution of forest estimated from JAXA and ESA FNF maps showed that our FNF map in 2015 agreed relatively well with the ESA map, with 77% of pixels being consistent. This study demonstrates the merit of using multi-temporal PALSAR-2/ScanSAR images for annual forest mapping at a national scale. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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18 pages, 3768 KiB

Open AccessFeature PaperArticle

A Semi-empirical Approach Based on Genetic Programming for the Study of Biophysical Controls on Diameter-Growth of Fagus orientalis in Northern Iran

by Mahmoud Bayat, Phan Thanh Noi, Rozita Zare and Dieu Tien Bui

Remote Sens. 2019, 11(14), 1680; https://doi.org/10.3390/rs11141680 - 15 Jul 2019

Cited by 17 | Viewed by 3545

Abstract

This paper examines the possible ecological controls on the diameter increment of oriental beech (Fagus orientalis Lipsky) in a high altitude forest in northern Iran. The main objectives of the study are computer-generated abiotic surfaces and associated plot estimates of (i) growing-season-cumulated potential solar radiation, (ii) seasonal air temperature, (iii) topographic wetness index in representing soil water distribution, and (iv) wind velocity generated from the simulation of fluid-flow dynamics in complex terrain. Plot estimates of the tree growth are based on averaged plot measurements of diameter at breast height increment during a growing period of nine years (2003–2012). Biotic variables related to the tree diameter increment involve averaged 2003 tree diameter and basal area measured in individual forest plots. In the modelling data (144 plots), the assemblage of modelled and observed site variables explained 75% of the variance in plot-level diameter increment. In the validation data (32 plots), the degree of explained variance was 77%. Mean tree diameter at breast height showed the strongest correlation with diameter increment, explaining 32% of the variation between-plot, followed by the configuration of topography and re-distribution of surface water (19.5%) and plot basal area (16.9%). On average, localised estimates of solar radiation and wind velocity potentially contribute to about 20% of the control on plot-level mean increment in oriental beech of the area. The results of the genetic programming showed that controlling the stand basal area and tree size by thinning and/or selective harvesting can have a favourable impact on the future distribution of mean diameter in oriental beech. Full article

(This article belongs to the Special Issue Advanced Geospatial Artificial Intelligence for Forest Modeling, Prediction, Conservation and Management)

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