A comparative analysis of forest area differences between statistics information and spatial thematic maps

Park, Jeong Mook; Lee, Yong Kyu; Lee, Jungsoo

doi:10.1080/21580103.2022.2072406

Cited by 1 publication

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References 9 publications

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“…Outputs from indirect monitoring methods, such as maps of forest attributes, provide key advantages compared to traditional surveys by delivering information on hard-to-reach forest areas and territory-wide spatial patterns [13][14][15]. Understanding the spatial patterns of forest attributes is particularly crucial for decision-making in forest management and conservation [16,17] since it enables the identification of biomass and biodiversity hot spots and rare and endangered habitats and species.…”

Section: Introductionmentioning

confidence: 99%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

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Understanding the spatial variations of forest features and their underlying drivers is crucial for managing and preserving forest ecosystems. Remote sensing techniques, due to their vast spatial coverage, are proving an essential tool for mapping and analyzing the complex spatial patterns of forests characteristics. In the current study, we use high-resolution Gaofen 1 satellite imagery to map features, such as biomass, biodiversity, and habitat type, of the subtropical forests in the Nanling area, a major biodiversity hot spot in the south of China; for subsequently analyzing the association between forest features and environmental drivers. We based our modeling approach on a semi-supervised fully convolutional neural network consisting of a ResNet architecture and a masking and aggregation module for computing predictions for forest survey plots of varied sizes. The fitted convolutional models yielded very high performance statistics, with test R2 values above 0.90 for basal area, aboveground biomass and Shannon diversity index regressions, slightly lower values for the regression of species richness (R2=0.83), and average test classification accuracy of forest types of 0.89, with lower performance for infrequent forest types, such as dwarf forests. The association analysis between predicted maps of forest features and environmental drivers revealed significant correlations being the strongest drivers the altitude, nitrogen content and climate variables, such as mean annual precipitation and temperature. Altitude was strongly associated with dwarf forests and high basal area and high aboveground biomass locations. Basal area and aboveground biomass were also positively influenced by annual rainfall and soil nitrogen and carbon, and negatively by temperature and radiation variables. Species richness was associated with high nitrogen and carbon contents, while the Shannon diversity index showed overall mild correlations with most drivers, with no remarkable patterns. We concluded that our semi-supervised convolutional approach using Gaofen 1 imagery proved useful for modeling the spatial distribution of the subtropical forests in Nanling; however, more research and data collection is necessary to improve the performance of species richness modeling and segmentation of infrequent forest types. Moreover, the strong associations found between climate, and predicted maps of biomass and forest types, provided key insights into the potential repercussions of climate change on Nanling forests.

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Section: Introductionmentioning

confidence: 99%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

View full text Add to dashboard Cite

show abstract

A comparative analysis of forest area differences between statistics information and spatial thematic maps

Cited by 1 publication

References 9 publications

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

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