Mapping and Monitoring Forest Cover

Congalton, Russell G.

doi:10.3390/f12091184

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…We observed that most natural landscapes consist of one to three components: water, plants, and/or rocks and minerals (Figure 1). Water covers over 70% of the earth's surface, and plants occupy over 30% of the land (Congalton, 2021). Plants are largely limited to regions with favorable climatic conditions, water, or sunlight; otherwise, water, rock or minerals are dominant.…”

Section: Types Of Natural Landscapes and Health Benefitsmentioning

confidence: 99%

Beyond “bluespace” and “greenspace”: A narrative review of possible health benefits from exposure to other natural landscapes

Browning

Rigolon

et al. 2023

Science of The Total Environment

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Section: Types Of Natural Landscapes and Health Benefitsmentioning

confidence: 99%

Beyond “bluespace” and “greenspace”: A narrative review of possible health benefits from exposure to other natural landscapes

Browning

Rigolon

et al. 2023

Science of The Total Environment

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“…Satellite-based RS methods offer great opportunities to collect comprehensive data about the current state of health and temporal dynamics of forests, with a high temporal (from 2 to 5 days up to intraday) and spatial (from 10 m to submeter) resolution [10]. In addition to abiotic and biotic damage, satellite images detect anthropogenic activities affecting forest health, such as forest management with clearcuts, selective cutting, and reforestation, which makes them ideal for large-area forest mapping and monitoring of all disturbance types [11].…”

Section: Introductionmentioning

confidence: 99%

Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning

Molnár,

Király

2024

J. Imaging

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Forest damage has become more frequent in Hungary in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A combined approach was developed to utilise high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing and field-based forest inventory data. Maps and charts were derived from vegetation indices (NDVI and Z∙NDVI) of satellite images to detect forest disturbances in the Hungarian study site for the period of 2017–2020. The NDVI maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reactions to damage between tree species were visible on the index maps; therefore, a random forest machine learning classifier was applied to show the spatial distribution of dominant species. An accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 99.1% producer, 71% user, and 71% total accuracies for forest damage and 81.9% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor all of Hungary in a faster, more accurate way using systematically collected field-data, the latest satellite imagery, and artificial intelligence.

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“…The satellites described in this article are equipped with optical sensors that provide data in near-real time, which is ideal for large-area forest mapping and monitoring all disturbance types [9].…”

Section: Introductionmentioning

confidence: 99%

Forest Monitoring Based on Sentinel-2 Satellite Imagery, Google Earth Engine Cloud Computing, and Machine Learning

Molnár¹,

Király²

2023

Preprint

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Forest damage has become more frequent in Europe in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A novel approach was developed to utilize high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing. The processing, analysing, and visualization of vegetation and water index (NDVI, NDVIch, Z NDVI, EVI, NDWI) maps and charts derived from satellite images took place in the cloud to ensure the detection of forest disturbances in the Hungarian study site for the period 2017-2020. The index maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reaction to damage between tree species were visible in the index values, therefore a Random Forest Machine Learning classifier was applied to show the spatial distribution of dominant species. Accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 71% Total Accuracy for forest damage and 76% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor the entire Hungary.

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Mapping and Monitoring Forest Cover

Abstract: Our Earth consists of approximately 70 percent water and 30 percent land [...]

Cited by 6 publications

References 6 publications

Beyond “bluespace” and “greenspace”: A narrative review of possible health benefits from exposure to other natural landscapes

Beyond “bluespace” and “greenspace”: A narrative review of possible health benefits from exposure to other natural landscapes

Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning

Forest Monitoring Based on Sentinel-2 Satellite Imagery, Google Earth Engine Cloud Computing, and Machine Learning

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