Submit to Forests Review for Forests Propose a Special Issue

Journal Menu

Journal Browser

Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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 (4 July 2021) | Viewed by 28347

Share This Special Issue

Special Issue Editor

Dr. Giorgos Mallinis

E-Mail Website
Guest Editor

Laboratory of Photogrammetry and Remote Sensing (PERS Lab), School of Rural and Surveying Engineering, The Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
Interests: land use/land cover (LULC) mapping; forests; classification development and comparison; geographic object-based image analysis; natural disasters; UAS; ecosystem services
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are the dominant terrestrial ecosystem of the Earth, shaping regional and global ecological processes, functions, and services. Over the last few decades, the retrieval of accurate information on forest ecosystems has become increasingly important at various levels of detail and scales. Spatially explicit, up-to-date forest attribute information is fundamental for supporting forest inventories, sustainable forest management, biodiversity conservation, forest ecosystem service mapping and assessment, carbon accounting, climate change mitigation, policy formulation, and reporting obligations to international treaties, among others.

Satellite and airborne remote sensing data have greatly contributed to forest attribute assessment and monitoring over the past five decades. The diverse spectral, spatial, and temporal information acquired from different sensor types and platforms has enabled the development of various approaches for forest attribute mapping, monitoring, and assessment.

At present, advances in sensor technology and appearance of new platforms such as unmanned aerial systems (UAS), rapid development of computational capacities, and freely available dense time series of satellite data, among others, offers the opportunity to provide information on forest attributes with unprecedented accuracy and detail.

However, several challenges still exist, related to the development of more accurate, transferable, and operational modeling approaches, generation of forest attribute maps with enhanced spatial, thematic, and temporal resolution to cover user needs and demands, multisensor and multiplatform fusion, and big data analysis for exploiting the nearly continuous data stream of earth observations.

This forthcoming Special Issue on “Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology” calls for original research papers with a focus on the development of new or improvement of existing methodological approaches for assessing the state and temporal dynamics of forest attributes, fusion of various data sources, and generation of products for multiscale forest attributes mapping and monitoring. Papers presenting research efforts on change detection and trajectory-based analysis for dynamic monitoring of forest attributes are also of interest. Further, submissions integrating remotely sensed 3D data with optical data for forest attributes modeling and mapping are particularly welcome.

Prof. Dr. Giorgos Mallinis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Forests is an international peer-reviewed open access monthly 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 2600 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 mapping
Forest inventory
Time–series analysis
Multitemporal analysis
Unmanned aerial systems
3D data
Multispectral and hyperspectral
Satellite
Image classification

Published Papers (10 papers)

Download All Papers

Research

20 pages, 4275 KiB

Open AccessArticle

Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

by Yingying Li, Zhengyong Zhao, Sunwei Wei, Dongxiao Sun, Qi Yang and Xiaogang Ding

Forests 2021, 12(11), 1430; https://0-doi-org.brum.beds.ac.uk/10.3390/f12111430 - 20 Oct 2021

Cited by 9 | Viewed by 1880

Abstract

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R²), and less effective for AK at only 8% and 6% (R²). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R²). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

16 pages, 3832 KiB

Open AccessArticle

Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions

by Nikos Theofanous, Irene Chrysafis, Giorgos Mallinis, Christos Domakinis, Natalia Verde and Sofia Siahalou

Forests 2021, 12(7), 902; https://0-doi-org.brum.beds.ac.uk/10.3390/f12070902 - 11 Jul 2021

Cited by 17 | Viewed by 2393

Abstract

Plantations of fast-growing forest species such as black locust (Robinia Pseudoacacia) can contribute to energy transformation, mitigate industrial pollution, and restore degraded, marginal land. In this study, the synergistic use of Sentinel-2 and Sentinel-1 time series data is explored for modeling aboveground biomass (AGB) in black locust short-rotation plantations in northeastern Greece. Optimal modeling dates and EO sensor data are also identified through the analysis. Random forest (RF) models were originally developed using monthly Sentinel-2 spectral indices, while, progressively, monthly Sentinel-1 bands were incorporated in the statistical analysis. The highest accuracy was observed for the models generated using Sentinel-2 August composites (R² = 0.52). The inclusion of Sentinel-1 bands in the spectral indices’ models had a negligible effect on modeling accuracy during the leaf-on period. The correlation and comparative performance of the spectral indices in terms of pairwise correlation with AGB varied among the phenophases of the forest plantations. Overall, the field-measured AGB in the forest plantations plots presented a higher correlation with the optical Sentinel-2 images. The synergy of Sentinel-1 and Sentinel-2 data proved to be a non-efficient approach for improving forest biomass RF models throughout the year within the geographical and environmental context of our study. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

11 pages, 2769 KiB

Open AccessArticle

Assessing Landsat Images Availability and Its Effects on Phenological Metrics

by Jean-François Mas and Francisca Soares de Araújo

Forests 2021, 12(5), 574; https://0-doi-org.brum.beds.ac.uk/10.3390/f12050574 - 03 May 2021

Cited by 5 | Viewed by 2092

Abstract

Landsat imagery offers the most extended continuous land surface observation at 30 m spatial resolution and is widely used in land change studies. On the other hand, the recent developments on big data, such as cloud computing, give new opportunities for carrying out satellite-based continuous land cover monitoring including land use/cover change and more subtle changes as forest degradation, agriculture intensification and vegetation phenological patterns alterations. However, in the range 0–10

^{\circ}

south latitude, especially in the summer and autumn, there is a high rainfall and high clouds presence. We hypothesise that it will be challenging to characterise vegetation phenology in regions where the number of valid (cloud-free) remotely-sensed observation is low or when the observations are unevenly distributed over the year. This paper aims to evaluate whether there is sufficient availability of Landsat 7 and 8 images over Brazil to support the analysis of phenodynamics of vegetation. We used Google Earth Engine to assess Landsat data availability during the last decades over the Brazilian territory. The valid observations (excluding clouds and shadow areas) from Landsat 4/5/7/8 during the period 1984–2017 were determined at pixel level. The results show a lower intensity of Landsat observations in the northern and northeastern parts of Brazil compared to the southern region, mainly due to clouds’ presence. Taking advantage of the overlapping areas between satellite paths where the number of observations is larger, we modelled the loss of information caused by a lower number of valid (cloud free) observations. We showed that, in the deciduous woody formations of the Caatinga dominium, the scarcity of valid observations has an adverse effect on indices’ performance aimed at describing vegetation phenology. However, the combination of Landsat data with satellite constellation such as Sentinel will likely permit to overcome many of these limitations. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

16 pages, 11404 KiB

Open AccessArticle

A New Method for Forest Canopy Hemispherical Photography Segmentation Based on Deep Learning

by Kexin Li, Xinwang Huang, Jingzhe Zhang, Zhihu Sun, Jianping Huang, Chunxue Sun, Qiancheng Xie and Wenlong Song

Forests 2020, 11(12), 1366; https://0-doi-org.brum.beds.ac.uk/10.3390/f11121366 - 19 Dec 2020

Cited by 6 | Viewed by 2674

Abstract

Research Highlights: This paper proposes a new method for hemispherical forest canopy image segmentation. The method is based on a deep learning methodology and provides a robust and fully automatic technique for the segmentation of forest canopy hemispherical photography (CHP) and gap fraction (GF) calculation. Background and Objectives: CHP is widely used to estimate structural forest variables. The GF is the most important parameter for calculating the leaf area index (LAI), and its calculation requires the binary segmentation result of the CHP. Materials and Methods: Our method consists of three modules, namely, northing correction, valid region extraction, and hemispherical image segmentation. In these steps, a core procedure is hemispherical canopy image segmentation based on the U-Net convolutional neural network. Our method is compared with traditional threshold methods (e.g., the Otsu and Ridler methods), a fuzzy clustering method (FCM), commercial professional software (WinSCANOPY), and the Habitat-Net network method. Results: The experimental results show that the method presented here achieves a Dice similarity coefficient (DSC) of 89.20% and an accuracy of 98.73%. Conclusions: The method presented here outperforms the Habitat-Net and WinSCANOPY methods, along with the FCM, and it is significantly better than the Otsu and Ridler threshold methods. The method takes the original canopy hemisphere image first and then automatically executes the three modules in sequence, and finally outputs the binary segmentation map. The method presented here is a pipelined, end-to-end method. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

17 pages, 3469 KiB

Open AccessArticle

Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine

by Sifiso Xulu, Nkanyiso Mbatha, Kabir Peerbhay and Michael Gebreslasie

Forests 2020, 11(12), 1283; https://0-doi-org.brum.beds.ac.uk/10.3390/f11121283 - 29 Nov 2020

Cited by 7 | Viewed by 3928

Abstract

South Africa is reported to experience timber shortages as a result of growing timber demands and pulp production, coupled with the government’s reluctance to grant new forestry permits. Rampant timber theft in the country makes these circumstances worse. The emergence of cloud-based platforms, such as Google Earth Engine (GEE), has greatly improved the accessibility and usability of high spatial and temporal Sentinel-1 and -2 data, especially in data-poor countries that lack high-performance computing systems for forest monitoring. Here, we demonstrate the potential of these resources for forest harvest detection. The results showed that Sentinel-1 data are efficient in detecting clear-cut events; both VH and VV backscatter signals decline sharply in accordance with clear-cutting and increase again when forest biomass increases. When correlated with highly responsive NDII, the VH and VV signals reached the best accuracies of 0.79 and 0.83, whereas the SWIR1 achieved –0.91. A Random Forest (RF) algorithm based on Sentinel-2 data also achieved over 90% accuracies for classifying harvested and forested areas. Overall, our study presents a cost-effective method for mapping clear-cut events in an economically important forestry area of South Africa while using GEE resources. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

19 pages, 6602 KiB

Open AccessArticle

Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

by Xuegang Mao, Yueqing Deng, Liang Zhu and Yao Yao

Forests 2020, 11(12), 1271; https://0-doi-org.brum.beds.ac.uk/10.3390/f11121271 - 28 Nov 2020

Cited by 3 | Viewed by 1910

Abstract

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

27 pages, 4733 KiB

Open AccessArticle

Detection of Standing Deadwood from Aerial Imagery Products: Two Methods for Addressing the Bare Ground Misclassification Issue

by Katarzyna Zielewska-Büttner, Petra Adler, Sven Kolbe, Ruben Beck, Lisa Maria Ganter, Barbara Koch and Veronika Braunisch

Forests 2020, 11(8), 801; https://0-doi-org.brum.beds.ac.uk/10.3390/f11080801 - 25 Jul 2020

Cited by 17 | Viewed by 4143

Abstract

Deadwood mapping is of high relevance for studies on forest biodiversity, forest disturbance, and dynamics. As deadwood predominantly occurs in forests characterized by a high structural complexity and rugged terrain, the use of remote sensing offers numerous advantages over terrestrial inventory. However, deadwood misclassifications can occur in the presence of bare ground, displaying a similar spectral signature. In this study, we tested the potential to detect standing deadwood (h > 5 m) using orthophotos (0.5 m resolution) and digital surface models (DSM) (1 m resolution), both derived from stereo aerial image matching (0.2 m resolution and 60%/30% overlap (end/side lap)). Models were calibrated in a 600 ha mountain forest area that was rich in deadwood in various stages of decay. We employed random forest (RF) classification, followed by two approaches for addressing the deadwood-bare ground misclassification issue: (1) post-processing, with a mean neighborhood filter for “deadwood”-pixels and filtering out isolated pixels and (2) a “deadwood-uncertainty” filter, quantifying the probability of a “deadwood”-pixel to be correctly classified as a function of the environmental and spectral conditions in its neighborhood. RF model validation based on data partitioning delivered high user’s (UA) and producer’s (PA) accuracies (both > 0.9). Independent validation, however, revealed a high commission error for deadwood, mainly in areas with bare ground (UA = 0.60, PA = 0.87). Post-processing (1) and the application of the uncertainty filter (2) improved the distinction between deadwood and bare ground and led to a more balanced relation between UA and PA (UA of 0.69 and 0.74, PA of 0.79 and 0.80, under (1) and (2), respectively). Deadwood-pixels showed 90% location agreement with manually delineated reference to deadwood objects. With both alternative solutions, deadwood mapping achieved reliable results and the highest accuracies were obtained with deadwood-uncertainty filter. Since the information on surface heights was crucial for correct classification, enhancing DSM quality could substantially improve the results. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

15 pages, 3396 KiB

Open AccessArticle

Comparison of Canopy Closure Estimation of Plantations Using Parametric, Semi-Parametric, and Non-Parametric Models Based on GF-1 Remote Sensing Images

by Jiarui Li and Xuegang Mao

Forests 2020, 11(5), 597; https://0-doi-org.brum.beds.ac.uk/10.3390/f11050597 - 25 May 2020

Cited by 18 | Viewed by 2870

Abstract

Canopy closure (CC) is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. Canopy closure estimation models, using a combination of measured data and remote sensing data, can largely replace traditional survey methods for CC. However, it is difficult to estimate the forest CC based on high spatial resolution remote sensing images. This study used China Gaofen-1 satellite (GF-1) images, and selected China’s north temperate Wangyedian Forest Farm (WYD) and subtropical Gaofeng Forest Farm (GF) as experimental areas. A parametric model (multiple linear regression (MLR)), non-parametric model (random forest (RF)), and semi-parametric model (generalized additive model (GAM)) were developed. The ability of the three models to estimate the CC of plantations based on high spatial resolution remote sensing GF-1 images and their performance in the two experimental areas was analyzed and compared. The results showed that the decision coefficient (R²), root mean square error (RMSE), and relative root mean square error (rRMSE) values of the parametric model (MLR), semi-parametric model (GAM), and non-parametric model (RF) for the WYD forest ranged from 0.45 to 0.69, 0.0632 to 0.0953, and 9.98% to 15.05%, respectively, and in the GF forest the R², RMSE, and rRMSE values ranged from 0.40 to 0.59, 0.0967 to 0.1152, and 16.73% to 19.93%, respectively. The best model in the two study areas was the GAM and the worst was the RF. The accuracy of the three models established in the WYD was higher than that in the GF area. The RMSE and rRMSE values for the MLR, GAM, and RF established using high spatial resolution GF-1 remote sensing images in the two test areas were within the scope of existing studies, indicating the three CC estimation models achieved satisfactory results. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

21 pages, 6897 KiB

Open AccessArticle

Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA

by John Hogland, David L.R. Affleck, Nathaniel Anderson, Carl Seielstad, Solomon Dobrowski, Jon Graham and Robert Smith

Forests 2020, 11(4), 426; https://0-doi-org.brum.beds.ac.uk/10.3390/f11040426 - 09 Apr 2020

Cited by 5 | Viewed by 2526

Abstract

Effective forest management is predicated on accurate information pertaining to the characteristics and condition of forests. Unfortunately, ground-based information that accurately describes the complex spatial and contextual nature of forests across broad landscapes is cost prohibitive to collect. In this case study we address technical challenges associated with estimating forest characteristics from remotely sensed data by incorporating field plot layouts specifically designed for calibrating models from such data, applying new image normalization procedures to bring images of varying spatial resolutions to a common radiometric scale, and implementing an ensemble generalized additive modeling technique. Image normalization and ensemble models provided accurate estimates of forest types, presence/absence of longleaf pine (Pinus palustris), and tree basal areas and tree densities over a large segment of the panhandle of Florida, USA. This study overcomes several of the major barriers associated with linking remotely sensed imagery with plot data to estimate key forest characteristics over large areas. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Graphical abstract

16 pages, 3555 KiB

Open AccessArticle

Combining GF-2 and Sentinel-2 Images to Detect Tree Mortality Caused by Red Turpentine Beetle during the Early Outbreak Stage in North China

by Zhongyi Zhan, Linfeng Yu, Zhe Li, Lili Ren, Bingtao Gao, Lixia Wang and Youqing Luo

Forests 2020, 11(2), 172; https://0-doi-org.brum.beds.ac.uk/10.3390/f11020172 - 05 Feb 2020

Cited by 25 | Viewed by 2819

Abstract

In recent years, the red turpentine beetle (RTB) (Dendroctonus valens LeConte) has invaded the northern regions of China. Due to the short invasion time, the outbreak of tree mortality corresponded to a low level of damage. Important information about tree mortality, provided by remote sensing at both single-tree and forest stand scale, is needed in forest management at the early stages of outbreak. In order to detect RTB-induced tree mortality at a single-tree scale, we evaluated the classification accuracies of Gaofen-2 (GF2) imagery at different spatial resolutions (1 and 4 m) using a pixel-based method. We also simultaneously applied an object-based method to 1 m pan-sharpened images. We used Sentinel-2 (S2) imagery with different resolutions (10 and 20 m) to detect RTB-induced tree mortality and compared their classification accuracies at a larger scale—the stand scale. Three kinds of machine learning algorithms—the classification and regression tree (CART), the random forest (RF), and the support vector machine (SVM)—were applied and compared in this study. The results showed that 1 m resolution GF2 images had the highest classification accuracy using the pixel-based method and SVM algorithm (overall accuracy = 77.7%). We found that the classification of three degrees of damage percentage within the S2 pixel (0%, <15%, and 15% < x < 50%) was not successful at a forest stand scale. However, 10 m resolution S2 images could acquire effective binary classification (<15%: overall accuracy = 74.9%; 15% < x < 50%: overall accuracy = 81.0%). Our results indicated that identifying tree mortality caused by RTB at a single-tree and forest stand scale was accomplished with the combination of GF2 and S2 images. Our results are very useful for the future exploration of the patterns of spatial and temporal changes in insect pest transmission at different spatial scales. Full article

(This article belongs to the Special Issue Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Monitoring and Assessing Forest Attributes Based on Remote Sensing Technology

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI