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Detecting Anomalies and Tracking Biodiversity for Forest Monitoring

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Dr. Marco Bascietto

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Guest Editor

Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing (CREA-IT), 00186 Rome, Italy
Interests: biodiversity; forest ecology; forest management; ecosystems

Dr. Alessandro Alivernini

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Guest Editor

Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing (CREA-IT), 00186 Rome, Italy
Interests: remote sensing; proximal sensing; ecophysiology; decision support systems; programming

Dr. Sofia Bajocco

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Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing (CREA-IT), 00186 Rome, Italy
Interests: vegetation phenology dynamics; landscape disturbance; fire spatio-temporal behavior; land cover change processes; remotely sensed data analysis; geoprocessing techniques; multivariate statistical methods
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Special Issue Information

Dear Colleagues,

Spaceborne active and passive sensors potential for monitoring temporary or permanent land cover changes and biodiversity indicators in forested areas is getting more and more feasible due the availability of imagery with high resolution in space (3–30 metres) and time (3–10 days).

Remote sensing techniques and products depend on the different spectral, spatial and temporal resolutions of the input datasets used, in the wide variability of disciplines, processing protocols and accuracy and resolution of results. The extent of the contribution that remote sensing may provide to standardized monitoring of forests and to the conservation status assessment of forest natural habitats (e.g., European Natura 2000 framework), is still uncertain at the country/regional scale.

We invite a wide range of contributions from applied and multi-disciplinary research to answer the need for continuous monitoring, reporting and verification systems that countries/regions have on their forested territories in order to support data-driven decisions for better governance and policy-making. We aim to publish papers that deal with providing operational tools allowing near-real time forest monitoring for the detection and quantification of anomalies (such as forest fires, summer droughts, and late frosts), monitoring land cover change dynamics (such as legal/illegal forest logging), and tracking biodiversity-related aspects by using environmental indicators as proxies, especially in protected areas and natural habitats.

Dr. Marco Bascietto
Dr. Alessandro Alivernini
Dr. Sofia Bajocco
Guest Editors

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Keywords

forest
continuous monitoring
change detection
forest anomalies
fire
drought
frost
logging
Monitoring, Reporting and Verification (MRV) systems
habitat quality
Natura 2000
conservation
biodiversity indicators

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Research

21 pages, 5972 KiB

Open AccessArticle

Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City

by Paola Andrea Mejia-Zuluaga, León Dozal and Juan C. Valdiviezo-N.

Remote Sens. 2022, 14(3), 801; https://doi.org/10.3390/rs14030801 - 08 Feb 2022

Cited by 4 | Viewed by 2548

Abstract

The mistletoe Phoradendron velutinum (P. velutinum) is a pest that spreads rapidly and uncontrollably in Mexican forests, becoming a serious problem since it is a cause of the decline of 23.3 million hectares of conifers and broadleaves in the country. The lack of adequate phytosanitary control has negative social, economic, and environmental impacts. However, pest management is a challenging task due to the difficulty of early detection for proper control of mistletoe infestations. Automating the detection of this pest is important due to its rapid spread and the high costs of field identification tasks. This paper presents a Genetic Programming (GP) approach for the automatic design of an algorithm to detect mistletoe using multispectral aerial images. Our study area is located in a conservation area of Mexico City, in the San Bartolo Ameyalco community. Images of 148 hectares were acquired by means of an Unmanned Aerial Vehicle (UAV) carrying a sensor sensitive to the R, G, B, red edge, and near-infrared bands, and with an average spatial resolution of less than 10 cm per pixel. As a result, it was possible to obtain an algorithm capable of classifying mistletoe P. velutinum at its flowering stage for the specific case of the study area in conservation area with an Overall Accuracy (OA) of 96% and a value of fitness function based on weighted Cohen’s Kappa (

k_{w}

) equal to 0.45 in the test data set. Additionally, our method’s performance was compared with two traditional image classification methods; in the first, a classical spectral index, named Intensive Pigment Index of Structure 2 (SIPI2), was considered for the detection of P. velutinum. The second method considers the well-known Support Vector Machine classification algorithm (SVM). We also compare the accuracy of the best GP individual with two additional indices obtained during the solution analysis. According to our experimental results, our GP-based algorithm outperforms the results obtained by the aforementioned methods for the identification of P. velutinum. Full article

(This article belongs to the Special Issue Detecting Anomalies and Tracking Biodiversity for Forest Monitoring)

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