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Geographies
Special Issues
Applying Remotely Sensed Imagery in Natural Resource Management
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Applying Remotely Sensed Imagery in Natural Resource Management

A special issue of Geographies (ISSN 2673-7086).

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 22109

Special Issue Editor

Prof. Dr. Russell G. Congalton

E-Mail Website1 Website2
Guest Editor
Department of Natural Resources & the Environment, University of New Hampshire, 56 College Road, 114 James Hall, Durham, NH 03824, USA
Interests: remote sensing; geospatial analysis; spatial data uncertainty; validation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The benefits of applying remotely sensed imagery in natural resource management are increasing every day. This Special Issue is dedicated to demonstrating the many innovative ways that remotely sensed data are being used to more efficiently and effectively collect and determine the information needed for managing our vital natural resources. Imagery can range anywhere from publicly available digital imagery to commercial data and even to unmanned aerial systems (UAS). Natural resource management includes not only information about the fauna and flora of our forests and rangelands, but also any information that is in any way related to our effective management and use of these resources.

Prof. Dr. Russell G. Congalton
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. Geographies is an international peer-reviewed open access quarterly 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

  • mapping natural resources
  • change analysis
  • vegetation characterization
  • vegetation measurements
  • vegetation monitoring

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Published Papers (6 papers)

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Research

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25 pages, 6831 KiB  
Article
Forest Type Differentiation Using GLAD Phenology Metrics, Land Surface Parameters, and Machine Learning
by Faith M. Hartley, Aaron E. Maxwell, Rick E. Landenberger and Zachary J. Bortolot
Geographies 2022, 2(3), 491-515; https://doi.org/10.3390/geographies2030030 - 15 Aug 2022
Cited by 3 | Viewed by 4140
Abstract
This study investigates the mapping of forest community types for the entire state of West Virginia, United States, using Global Land Analysis and Discovery (GLAD) Phenology Metrics, Analysis Ready Data (ARD) derived from Landsat time series data, and digital terrain variables derived from [...] Read more.
This study investigates the mapping of forest community types for the entire state of West Virginia, United States, using Global Land Analysis and Discovery (GLAD) Phenology Metrics, Analysis Ready Data (ARD) derived from Landsat time series data, and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study was to explore the use of globally consistent ARD for operational forest type mapping over a large spatial extent. Mean overall accuracy calculated from 50 model replicates for differentiating seven forest community types using only variables selected from the 188 GLAD Phenology Metrics used in the study resulted in an overall accuracy (OA) of 54.3% (map-level image classification efficacy (MICE) = 0.433). Accuracy increased to a mean OA of 64.8% (MICE = 0.496) when the Oak/Hickory and Oak/Pine classes were combined into an Oak Dominant class. Once selected terrain variables were added to the model, the mean OA for differentiating the seven forest types increased to 65.3% (MICE = 0.570), while the accuracy for differentiating six classes increased to 76.2% (MICE = 0.660). Our results highlight the benefits of combining spectral data and terrain variables and also the enhancement of the product’s usefulness when probabilistic predictions are provided alongside a hard classification. The GLAD Phenology Metrics did not provide an accuracy comparable to those obtained using harmonic regression coefficients; however, they generally outperformed models trained using only summer or fall seasonal medians and performed comparably to those trained using spring medians. We suggest further exploration of the GLAD Phenology Metrics as input for other spatial predictive mapping and modeling tasks. Full article
(This article belongs to the Special Issue Applying Remotely Sensed Imagery in Natural Resource Management)
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18 pages, 2470 KiB  
Article
Performance Evaluation of Multiple Pan-Sharpening Techniques on NDVI: A Statistical Framework
by Daniel Beene, Su Zhang, Christopher D. Lippitt and Susan M. Bogus
Geographies 2022, 2(3), 435-452; https://doi.org/10.3390/geographies2030027 - 13 Jul 2022
Cited by 4 | Viewed by 2763
Abstract
Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is [...] Read more.
Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram–Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application. Full article
(This article belongs to the Special Issue Applying Remotely Sensed Imagery in Natural Resource Management)
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13 pages, 2936 KiB  
Article
Fully Polarimetric L-Band Synthetic Aperture Radar for the Estimation of Tree Girth as a Representative of Stand Productivity in Rubber Plantations
by Bambang H. Trisasongko, Dyah R. Panuju, Amy L. Griffin and David J. Paull
Geographies 2022, 2(2), 173-185; https://doi.org/10.3390/geographies2020012 - 24 Mar 2022
Cited by 2 | Viewed by 2131
Abstract
This article explores a potential exploitation of fully polarimetric radar data for the management of rubber plantations, specifically for predicting tree circumference as a crucial information need for sustainable plantation management. Conventional backscatter coefficients along with Eigen-based and model-based decomposition features served as [...] Read more.
This article explores a potential exploitation of fully polarimetric radar data for the management of rubber plantations, specifically for predicting tree circumference as a crucial information need for sustainable plantation management. Conventional backscatter coefficients along with Eigen-based and model-based decomposition features served as the predictors in models of tree girth using ten regression approaches. The findings suggest that backscatter coefficients and Eigen-based decomposition features yielded lower accuracy than model-based decomposition features. Model-based decompositions, especially the Singh decomposition, provided the best accuracies when they were coupled with guided regularized random forests regression. This research demonstrates that L-band SAR data can provide an accurate estimation of rubber plantation tree girth, with an RMSE of about 8 cm. Full article
(This article belongs to the Special Issue Applying Remotely Sensed Imagery in Natural Resource Management)
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19 pages, 3065 KiB  
Article
Spatial and Temporal Change of Land Cover in Protected Areas in Malawi: Implications for Conservation Management
by Daniel Kpienbaareh, Evans Sumabe Batung and Isaac Luginaah
Geographies 2022, 2(1), 68-86; https://doi.org/10.3390/geographies2010006 - 12 Feb 2022
Cited by 2 | Viewed by 3182
Abstract
Protected areas (PAs) transform over time due to natural and anthropogenic processes, resulting in the loss of biodiversity and ecosystem services. As current and projected climatic trends are poised to pressurize the sustainability of PAs, analyses of the existing perturbations are crucial for [...] Read more.
Protected areas (PAs) transform over time due to natural and anthropogenic processes, resulting in the loss of biodiversity and ecosystem services. As current and projected climatic trends are poised to pressurize the sustainability of PAs, analyses of the existing perturbations are crucial for providing valuable insights that will facilitate conservation management. In this study, land cover change, landscape characteristics, and spatiotemporal patterns of the vegetation intensity in the Kasungu National Park (area = 2445.10 km2) in Malawi were assessed using Landsat data (1997, 2008 and 2018) in a Fuzzy K-Means unsupervised classification. The findings reveal that a 21.12% forest cover loss occurred from 1997 to 2018: an average annual loss of 1.09%. Transition analyses of the land cover changes revealed that forest to shrubs conversion was the main form of land cover transition, while conversions from shrubs (3.51%) and bare land (3.48%) to forest over the two decades were comparatively lower, signifying a very low rate of forest regeneration. The remaining forest cover in the park was aggregated in a small land area with dissimilar landscape characteristics. Vegetation intensity and vigor were lower mainly in the eastern part of the park in 2018. The findings have implications for conservation management in the context of climate change and the growing demand for ecosystem services in forest-dependent localities. Full article
(This article belongs to the Special Issue Applying Remotely Sensed Imagery in Natural Resource Management)
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17 pages, 5805 KiB  
Article
Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA
by Abdullah Al Saim and Mohamed H. Aly
Geographies 2022, 2(1), 31-47; https://doi.org/10.3390/geographies2010004 - 30 Jan 2022
Cited by 11 | Viewed by 4766
Abstract
Fire susceptibility modeling is crucial for sustaining and managing forests among many other valuable land resources. With 56% of its area covered by forests, Arkansas is known as the “natural state”. About 1000 wildfires occurred and burned more than 10,000 acres each year [...] Read more.
Fire susceptibility modeling is crucial for sustaining and managing forests among many other valuable land resources. With 56% of its area covered by forests, Arkansas is known as the “natural state”. About 1000 wildfires occurred and burned more than 10,000 acres each year during 1981–2018. In this paper, we use remote-sensing-based machine learning methods to address the natural and anthropogenic factors influencing wildfires and model fire susceptibility in Arkansas. Among the 15 explored variables, potential evapotranspiration, soil moisture, Palmer drought severity index, and dry season precipitation were recognized as the most significant factors contributing to the fire density. The obtained R-squared values are significant, with 0.99 for training the model and 0.92 for the validation. The results show that the Ouachita National Forest and the Ozark Forest, in west-central and west Arkansas, respectively, have the highest susceptibility to wildfires. The southern part of Arkansas has low-to-moderate fire susceptibility, while the eastern part of the state has the lowest fire susceptibility. These new results for Arkansas demonstrate the potency of remote-sensing-based random forest in predicting fire susceptibility at the state level that can be adapted to study fires in other states and help with fire preparedness to reduce loss and save the precious environment. Full article
(This article belongs to the Special Issue Applying Remotely Sensed Imagery in Natural Resource Management)
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Review

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38 pages, 1460 KiB  
Review
Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review
by Benjamin T. Fraser, Christine L. Bunyon, Sarah Reny, Isabelle Sophia Lopez and Russell G. Congalton
Geographies 2022, 2(2), 303-340; https://doi.org/10.3390/geographies2020021 - 8 Jun 2022
Cited by 5 | Viewed by 3841
Abstract
Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in [...] Read more.
Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in a range of disciplines. Despite these achievements, only minimal progress has been made in (1) establishing standard operating practices and (2) communicating both the limitations and necessary next steps for future research. In this review of literature published between 2016 and 2022, UAS applications in forestry, freshwater ecosystems, grasslands and shrublands, and agriculture were synthesized to discuss the status and trends in UAS sensor data collection and processing. Two distinct conclusions were summarized from the over 120 UAS applications reviewed for this research. First, while each discipline exhibited similarities among their data collection and processing methods, best practices were not referenced in most instances. Second, there is still a considerable variability in the UAS sensor data methods described in UAS applications in natural resources, with fewer than half of the publications including an incomplete level of detail to replicate the study. If UAS are to increasingly provide data for important or complex challenges, they must be effectively utilized. Full article
(This article belongs to the Special Issue Applying Remotely Sensed Imagery in Natural Resource Management)
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