Special Issue "Geographic Information Science in Forestry"

Special Issue Editor

Guest Editor
Prof. Joseph F. Knight

University of Minnesota Twin Cities, Department of Forest Resources, Minneapolis, USA
Website | E-Mail
Phone: 6126255354
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 papers will be 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 1000 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

Published Papers (2 papers)

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Research

Open AccessArticle 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
ISPRS Int. J. Geo-Inf. 2019, 8(1), 24; https://doi.org/10.3390/ijgi8010024
Received: 21 November 2018 / Revised: 26 December 2018 / Accepted: 7 January 2019 / Published: 11 January 2019
PDF Full-text (1148 KB)
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)
Open AccessArticle Single-Tree Detection in High-Resolution Remote-Sensing Images Based on a Cascade Neural Network
ISPRS Int. J. Geo-Inf. 2018, 7(9), 367; https://doi.org/10.3390/ijgi7090367
Received: 17 July 2018 / Revised: 27 August 2018 / Accepted: 31 August 2018 / Published: 6 September 2018
PDF Full-text (41607 KB) | HTML Full-text | XML Full-text
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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