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IJGI
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Geographic Information Science in Forestry
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Geographic Information Science in Forestry

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (1 August 2019) | Viewed by 16256

Special Issue Editor

Prof. Joseph F. Knight

E-Mail Website
Guest Editor
Department of Forest Resources, University of Minnesota Twin Cities, Minneapolis, MN, USA
Interests: remote sensing; GIS; forest

Special Issue Information

Dear Colleagues,

Forests have a critical role in ecosystem services and functions. Due to factors such as land use change, disturbances, fragmentation, climate change, pests, and disease, forests are under immense and increasing pressure. Producing accurate measures of forest inventory, health, and change remains challenging. Complications include the large spatial extents of forests, variability in forest stand parameters, and the spectral homogeneity of forests relative to other land cover types. Advances in Remote Sensing have the potential to ameliorate some of these challenges. In particular, the widening availability of LiDAR data, the increasing use of object-based image analysis, and the proliferation of unmanned aircraft systems (UAS) offer promise of improved forest assessment and monitoring.

For this special issue, “Geographic Information Science in Forestry,” we invite high-impact, original research on remote sensing and geospatial analysis of forests. The following topic areas and methods are encouraged:

  • Inventory, structure, and health assessment
  • Forest tree speciation using imagery and/or LiDAR
  • UAS-based assessment and monitoring
  • Very high spatial resolution imagery
  • High density LiDAR point clouds
  • Photogrammetric point clouds
  • High temporal resolution approaches

All manuscripts must include statistically rigorous validation methods. Reliance upon p-values alone to describe modeling results is discouraged. Manuscripts describing applications of known methods in new study areas will not be considered.

Prof. Joseph F. Knight
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. ISPRS International Journal of Geo-Information 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 1700 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

  • forests
  • remote sensing
  • LiDAR
  • UAS
  • point clouds
  • forest inventory
  • forest health

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Published Papers (4 papers)

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Research

18 pages, 4056 KiB  
Article
Methods to Detect Edge Effected Reductions in Fire Frequency in Simulated Forest Landscapes
by Xinyuan Wei and Chris P. S. Larsen
ISPRS Int. J. Geo-Inf. 2019, 8(6), 277; https://doi.org/10.3390/ijgi8060277 - 14 Jun 2019
Cited by 11 | Viewed by 3214
Abstract
Reductions in fire frequency (RFF) are known to occur in the area adjacent to the rigid-boundary of simulated forest landscapes. Few studies, however, have removed those edge effected regions (EERs), and many others may, thus, have misinterpreted their simulated forest conditions within those [...] Read more.
Reductions in fire frequency (RFF) are known to occur in the area adjacent to the rigid-boundary of simulated forest landscapes. Few studies, however, have removed those edge effected regions (EERs), and many others may, thus, have misinterpreted their simulated forest conditions within those unidentified edges. We developed three methods to detect and remove EERs with RFF and applied them to fire frequency maps of 2900 × 2900 grids developed using between 1000 and 1200 fire-year maps. The three methods employed different approaches: scanning, agglomeration, and division, along with the consensus of two and three of those methods. The detected EERs with RFF ranged in mean width from 5.9 to 17.3 km, and occupied 4.9 to 21.3% of the simulated landscapes. Those values are lower than the 40 km buffer width, which occupied 47.5% of the simulated landscape, used in a previous study in this area that based buffer width on length of the largest fire. The maximum width of the EER covaried with wind predominance, indicating it is not possible to prescribe a standard buffer width for all simulation studies. The three edge detection methods differ in their optimality, with the best results provided by a consensus of the three methods. We suggest that future landscape forest simulation studies should, to ensure their results near the rigid boundary are not misrepresentative, simulate an appropriately enlarged study area and then employ edge detection methods to remove the EERs with RFF. Full article
(This article belongs to the Special Issue Geographic Information Science in Forestry)
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19 pages, 112922 KiB  
Article
Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM
by Ping Liu and Xi Chen
ISPRS Int. J. Geo-Inf. 2019, 8(2), 86; https://doi.org/10.3390/ijgi8020086 - 14 Feb 2019
Cited by 14 | Viewed by 4316
Abstract
Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were [...] Read more.
Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were studied. Based on Chinese GF-1 and GF-2 satellite imagery features, this study has developed a comprehensive feature extraction and intercropping classification scheme. Textural features derived from a Gray level co-occurrence matrix (GLCM) and vegetation features derived from multi-temporal GF-1 and GF-2 satellites were introduced and combined into three different groups. The rotation forest method was then adopted based on a Support Vector Machine (RoF-SVM), which offers the advantage of using an SVM algorithm and that boosts the diversity of individual base classifiers by a rotation forest. The combined spectral-textural-multitemporal features achieved the best classification result. The results were compared with those of the maximum likelihood classifier, support vector machine and random forest method. It is shown that the RoF-SVM algorithm for the combined spectral-textural-multitemporal features can effectively classify an intercropping area (overall accuracy of 86.87% and kappa coefficient of 0.78), and the classification result effectively eliminated salt and pepper noise. Furthermore, the GF-1 and GF-2 satellite images combined with spectral, textural, and multi-temporal features can provide sufficient information on vegetation cover located in an extremely complex and diverse intercropping area. Full article
(This article belongs to the Special Issue Geographic Information Science in Forestry)
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21 pages, 6202 KiB  
Article
A Comparison of Standard Modeling Techniques Using Digital Aerial Imagery with National Elevation Datasets and Airborne LiDAR to Predict Size and Density Forest Metrics in the Sapphire Mountains MT, USA
by Robert Ahl, John Hogland and Steve Brown
ISPRS Int. J. Geo-Inf. 2019, 8(1), 24; https://doi.org/10.3390/ijgi8010024 - 11 Jan 2019
Cited by 5 | Viewed by 3358
Abstract
In recent years airborne Light Detection and Ranging (LiDAR) technology has received a great deal of attention. Using airborne LiDAR, analysts have successfully related height measurements to forest characteristics such as tree size, basal area, and number of trees. Similarly, National Agricultural Imagery [...] Read more.
In recent years airborne Light Detection and Ranging (LiDAR) technology has received a great deal of attention. Using airborne LiDAR, analysts have successfully related height measurements to forest characteristics such as tree size, basal area, and number of trees. Similarly, National Agricultural Imagery Program (NAIP) digital aerial imagery in combination with elevation datasets such as the National Elevation Dataset (NED) have been used to estimate similar forest characteristics. Few comparisons, however, have been made between using airborne LiDAR, NAIP, and NED to estimate forest characteristics. In this study we compare airborne LiDAR, NAIP, and NAIP assisted NED based models of forest characteristics commonly used within forest management at the spatial scale of field plots and forest stands. Our findings suggest that there is a high degree of similarity in model fit and estimated values when using LiDAR, NAIP, and NAIP assisted NED predictor variables. Full article
(This article belongs to the Special Issue Geographic Information Science in Forestry)
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20 pages, 41607 KiB  
Article
Single-Tree Detection in High-Resolution Remote-Sensing Images Based on a Cascade Neural Network
by Dong Tianyang, Zhang Jian, Gao Sibin, Shen Ying and Fan Jing
ISPRS Int. J. Geo-Inf. 2018, 7(9), 367; https://doi.org/10.3390/ijgi7090367 - 6 Sep 2018
Cited by 20 | Viewed by 4726
Abstract
Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade [...] Read more.
Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade neural network. In this method, we firstly calibrated the tree and non-tree samples in high-resolution remote-sensing images to train a classifier with the backpropagation (BP) neural network. Then, we analyzed the differences in the first-order statistic features, such as energy, entropy, mean, skewness, and kurtosis of the tree and non-tree samples. Finally, we used these features to correct the BP neural network model and build a cascade neural network classifier to detect a single tree. To verify the validity and practicability of the proposed method, six forestlands including two areas of oil palm in Thailand, and four areas of small seedlings, red maples, or longan trees in China were selected as test areas. The results from different methods, such as the region-growing method, template-matching method, BP neural network, and proposed cascade-neural-network method were compared considering these test areas. The experimental results show that the single-tree detection method based on the cascade neural network exhibited the highest root mean square of the matching rate (RMS_Rmat = 90%) and matching score (RMS_M = 68) in all the considered test areas. Full article
(This article belongs to the Special Issue Geographic Information Science in Forestry)
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