Assessing the reforestation effects of plantation plots in the Thai savanna based on 45 cm resolution true-color images and machine learning

Doi, Ryoichi

doi:10.1088/1748-9326/abcfe3

Cited by 4 publications

(4 citation statements)

References 41 publications

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“…The efficiency of machine learning (ML) to extract patterns from satellite data made it an attractive tool for the Earth observation community. Breakthroughs in mapping woody vegetation in savannas using ML include detecting individual trees [71], identifying them down to the species level [91], and assessing reforestation effects in savannas [212]. The stage is currently set for the study of ecosystem-scale interactions using ML, which has become increasingly popular in ecology [213], in part due to its relatively few a priori assumptions.…”

Section: Exploiting Machine Learning and Big Data Analyticsmentioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.

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Section: Exploiting Machine Learning and Big Data Analyticsmentioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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“…There were two more residential areas, but their LST values were unavailable for some years due to an unknown computer glitch. Among the six areas, residential area 32 was the hottest in 2016 when an extreme weather anomaly was observed in Southeast Asia [20], including Thailand [21]. The 2022 acquisition time was the coolest.…”

Section: -3-investigation Of Deterioration Effectsmentioning

confidence: 99%

Are New Residential Areas Cooler than Older Ones?

Doi

2022

Emerg Sci J

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This study was conducted to investigate if passive cooling technologies have been implemented in commercially supplied new residential areas in Bangkok and to observe if there were significant differences in the land surface temperature (LST) compared to old residential areas. Values of LST were compared among 62 residential areas that differed in completion year. The mean LST for the most recent residential areas completed in 2013 or later was significantly less than that for the other older categories, suggesting that passive cooling effects were significantly better functioning in the new residential areas. A roof treatment on old buildings in a public housing project was still quantitatively effective after 8 years. This suggested the possibility of a deteriorated cooling function in the older categories among the residential areas. The possibility of deterioration was quantitatively investigated. The results stressed the importance of the periodic maintenance of passive cooling functions. As an extension of precise basic studies, this is the first study to quantify the passive cooling effects on commercially supplied residential areas. In terms of spatial extent, this residential area–scale study bridges precise analyses of single buildings/materials and regional observations, mainly relying on satellite data. The study results can aid in the mitigation and prevention of the urban heat island phenomenon. Doi: 10.28991/ESJ-2022-06-06-08 Full Text: PDF

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“…Sin embargo, el seguimiento de la condición de los árboles y arbustos mediante inventarios in situ a grandes escalas demanda largos periodos de tiempo y altos costos económicos (van Leeuwen & Nieuwenhuis, 2010). Para disminuir los tiempos de evaluación en campo se requiere de nuevas metodologías como el uso de la teledetección, una de esas técnicas es la evaluación mediante vehículos aéreos no tripulados (dron, también conocidos como drones) (Doi, 2020). El uso de sensores remotos transportados por vehículos aéreos no tripulados en estudios forestales, ecológicos y de restauración ecológica está aumentando en los últimos años (Anderson & Gaston, 2013;Cao, W.; Wu, J.; Shi, Y.; Chen, 2022; Gallardo-Salazar, J. L., Pompa-García, M., Aguirre-Salado, C. A., López-Serrano, P. M., & Meléndez-Soto, 2020; Goodbody et al, 2017).…”

Section: Introductionunclassified

“…Los drones presentan un enorme potencial para el monitoreo de comunidades forestales y puede ser una herramienta crucial para la toma de decisiones en los proyectos restauración forestal (Almeida et al, 2019;Doi, 2020). Aunque actualmente se ha incrementado el uso de drones, es necesario la generación o adaptación de esta tecnología en diversas comunidades vegetales, por lo que, en esta investigación se exploró el potencial de un sistema transportado por dron para evaluar una plantación forestal de especies mixtas en el matorral espinoso tamaulipeco.…”

Section: Introductionunclassified