Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests

Yanbin Jiang, C.A.J.M. de Bie, Tiejun Wang, A.K. Skidmore, Xuehua Liu, Shanshan Song, Xiaoming Shao

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)

Abstract

Question: Is there, at the macro-habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI-based indicator that is derived from time-series of SPOT-VGT imagery? Location: Southern China. Methods: Applying the ISODATA algorithm, we classified SPOT-VGT NDVI time-series imagery and produced an NDVI map at 1-km2 resolution containing 128 NDVI classes. That map and the National Land Cover Map of China (2000) were used to prepare a scheme for field sampling. For 537 1-km2 areas, located in 19 blocks of 100 km × 100 km, field data were collected. They represented 51 preselected NDVI classes that were assumed to contain evergreen forests. Data on the fractions of forest and evergreen forest, the fraction of evergreen forest in forest present and presence probability of epiphyllous liverworts were regressed against different NDVI class-specific indicators by means of weighted least-squares regression (WLSR). Results: The SPOT-VGT NDVI for March was found to best explain the variation between 1-km2 areas in the presence probability of epiphyllous liverworts (R2 = 0.64; linear relationship; RMSE = 0.38) as the area fraction (%) of evergreen forest (R2 = 0.90; exponential relationship; RMSE = 6.1%). Epiphyllous liverworts were only found within 1-km2 areas when the SPOT-VGT NDVI value for March was more than 0.50 (model estimate: 0.43 ± 0.20) and the fraction of evergreen forest in 1 km2 was above 14% (model estimate: 5 ± 35%). The estimation errors (with 95% confidence interval) of these two relationships were calculated using a bootstrap resampling method with 1000 replications; they were, respectively, 0.49-0.76 and 0.85-0.94 for R2, and 0.11-0.23 and 5.2-11.9% for RMSE. Other positive relationships were found between the presence probability of epiphyllous liverworts and the fraction of evergreen forest (R2 = 0.64, linear relationship; RMSE = 0.38) and between the fraction of evergreen forest and forest within 1-km2 areas (R2 = 0.80, linear relationship; RMSE = 29%). Conclusion: We show that for southern China, the fraction of evergreen forest and the presence probability of epiphyllous liverworts can directly be inferred by making use of SPOT-VGT NDVI imagery. The findings are fully consistent with earlier reported hotspots for epiphyllous liverworts. At the macro-habitat scale, the presence probability of epiphyllous liverworts proved quantitatively related to the fraction of evergreen forest. This suggests causal relationships with patch size and/or rainfall patterns. We believe that the derived maps may serve as a foundation for a range of further studies. © 2012 International Association for Vegetation Science.
Original languageEnglish
Pages (from-to)214-226
JournalJournal of vegetation science
Volume24
Issue number2
DOIs
Publication statusPublished - 2013

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liverwort
evergreen forest
remote sensing
mosses and liverworts
NDVI
SPOT
imagery
time series
China
time series analysis
bootstrapping
patch size
habitat
confidence interval
land cover
habitats
least squares

Cite this

@article{62104bf275174c8dbe25c4417eb31ba4,
title = "Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests",
abstract = "Question: Is there, at the macro-habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI-based indicator that is derived from time-series of SPOT-VGT imagery? Location: Southern China. Methods: Applying the ISODATA algorithm, we classified SPOT-VGT NDVI time-series imagery and produced an NDVI map at 1-km2 resolution containing 128 NDVI classes. That map and the National Land Cover Map of China (2000) were used to prepare a scheme for field sampling. For 537 1-km2 areas, located in 19 blocks of 100 km × 100 km, field data were collected. They represented 51 preselected NDVI classes that were assumed to contain evergreen forests. Data on the fractions of forest and evergreen forest, the fraction of evergreen forest in forest present and presence probability of epiphyllous liverworts were regressed against different NDVI class-specific indicators by means of weighted least-squares regression (WLSR). Results: The SPOT-VGT NDVI for March was found to best explain the variation between 1-km2 areas in the presence probability of epiphyllous liverworts (R2 = 0.64; linear relationship; RMSE = 0.38) as the area fraction ({\%}) of evergreen forest (R2 = 0.90; exponential relationship; RMSE = 6.1{\%}). Epiphyllous liverworts were only found within 1-km2 areas when the SPOT-VGT NDVI value for March was more than 0.50 (model estimate: 0.43 ± 0.20) and the fraction of evergreen forest in 1 km2 was above 14{\%} (model estimate: 5 ± 35{\%}). The estimation errors (with 95{\%} confidence interval) of these two relationships were calculated using a bootstrap resampling method with 1000 replications; they were, respectively, 0.49-0.76 and 0.85-0.94 for R2, and 0.11-0.23 and 5.2-11.9{\%} for RMSE. Other positive relationships were found between the presence probability of epiphyllous liverworts and the fraction of evergreen forest (R2 = 0.64, linear relationship; RMSE = 0.38) and between the fraction of evergreen forest and forest within 1-km2 areas (R2 = 0.80, linear relationship; RMSE = 29{\%}). Conclusion: We show that for southern China, the fraction of evergreen forest and the presence probability of epiphyllous liverworts can directly be inferred by making use of SPOT-VGT NDVI imagery. The findings are fully consistent with earlier reported hotspots for epiphyllous liverworts. At the macro-habitat scale, the presence probability of epiphyllous liverworts proved quantitatively related to the fraction of evergreen forest. This suggests causal relationships with patch size and/or rainfall patterns. We believe that the derived maps may serve as a foundation for a range of further studies. {\circledC} 2012 International Association for Vegetation Science.",
author = "Yanbin Jiang and {de Bie}, C.A.J.M. and Tiejun Wang and A.K. Skidmore and Xuehua Liu and Shanshan Song and Xiaoming Shao",
year = "2013",
doi = "10.1111/j.1654-1103.2012.01453.x",
language = "English",
volume = "24",
pages = "214--226",
journal = "Journal of vegetation science",
issn = "1100-9233",
publisher = "Wiley-Blackwell",
number = "2",

}

Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests. / Jiang, Yanbin; de Bie, C.A.J.M.; Wang, Tiejun; Skidmore, A.K.; Liu, Xuehua; Song, Shanshan; Shao, Xiaoming.

In: Journal of vegetation science, Vol. 24, No. 2, 2013, p. 214-226.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests

AU - Jiang, Yanbin

AU - de Bie, C.A.J.M.

AU - Wang, Tiejun

AU - Skidmore, A.K.

AU - Liu, Xuehua

AU - Song, Shanshan

AU - Shao, Xiaoming

PY - 2013

Y1 - 2013

N2 - Question: Is there, at the macro-habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI-based indicator that is derived from time-series of SPOT-VGT imagery? Location: Southern China. Methods: Applying the ISODATA algorithm, we classified SPOT-VGT NDVI time-series imagery and produced an NDVI map at 1-km2 resolution containing 128 NDVI classes. That map and the National Land Cover Map of China (2000) were used to prepare a scheme for field sampling. For 537 1-km2 areas, located in 19 blocks of 100 km × 100 km, field data were collected. They represented 51 preselected NDVI classes that were assumed to contain evergreen forests. Data on the fractions of forest and evergreen forest, the fraction of evergreen forest in forest present and presence probability of epiphyllous liverworts were regressed against different NDVI class-specific indicators by means of weighted least-squares regression (WLSR). Results: The SPOT-VGT NDVI for March was found to best explain the variation between 1-km2 areas in the presence probability of epiphyllous liverworts (R2 = 0.64; linear relationship; RMSE = 0.38) as the area fraction (%) of evergreen forest (R2 = 0.90; exponential relationship; RMSE = 6.1%). Epiphyllous liverworts were only found within 1-km2 areas when the SPOT-VGT NDVI value for March was more than 0.50 (model estimate: 0.43 ± 0.20) and the fraction of evergreen forest in 1 km2 was above 14% (model estimate: 5 ± 35%). The estimation errors (with 95% confidence interval) of these two relationships were calculated using a bootstrap resampling method with 1000 replications; they were, respectively, 0.49-0.76 and 0.85-0.94 for R2, and 0.11-0.23 and 5.2-11.9% for RMSE. Other positive relationships were found between the presence probability of epiphyllous liverworts and the fraction of evergreen forest (R2 = 0.64, linear relationship; RMSE = 0.38) and between the fraction of evergreen forest and forest within 1-km2 areas (R2 = 0.80, linear relationship; RMSE = 29%). Conclusion: We show that for southern China, the fraction of evergreen forest and the presence probability of epiphyllous liverworts can directly be inferred by making use of SPOT-VGT NDVI imagery. The findings are fully consistent with earlier reported hotspots for epiphyllous liverworts. At the macro-habitat scale, the presence probability of epiphyllous liverworts proved quantitatively related to the fraction of evergreen forest. This suggests causal relationships with patch size and/or rainfall patterns. We believe that the derived maps may serve as a foundation for a range of further studies. © 2012 International Association for Vegetation Science.

AB - Question: Is there, at the macro-habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI-based indicator that is derived from time-series of SPOT-VGT imagery? Location: Southern China. Methods: Applying the ISODATA algorithm, we classified SPOT-VGT NDVI time-series imagery and produced an NDVI map at 1-km2 resolution containing 128 NDVI classes. That map and the National Land Cover Map of China (2000) were used to prepare a scheme for field sampling. For 537 1-km2 areas, located in 19 blocks of 100 km × 100 km, field data were collected. They represented 51 preselected NDVI classes that were assumed to contain evergreen forests. Data on the fractions of forest and evergreen forest, the fraction of evergreen forest in forest present and presence probability of epiphyllous liverworts were regressed against different NDVI class-specific indicators by means of weighted least-squares regression (WLSR). Results: The SPOT-VGT NDVI for March was found to best explain the variation between 1-km2 areas in the presence probability of epiphyllous liverworts (R2 = 0.64; linear relationship; RMSE = 0.38) as the area fraction (%) of evergreen forest (R2 = 0.90; exponential relationship; RMSE = 6.1%). Epiphyllous liverworts were only found within 1-km2 areas when the SPOT-VGT NDVI value for March was more than 0.50 (model estimate: 0.43 ± 0.20) and the fraction of evergreen forest in 1 km2 was above 14% (model estimate: 5 ± 35%). The estimation errors (with 95% confidence interval) of these two relationships were calculated using a bootstrap resampling method with 1000 replications; they were, respectively, 0.49-0.76 and 0.85-0.94 for R2, and 0.11-0.23 and 5.2-11.9% for RMSE. Other positive relationships were found between the presence probability of epiphyllous liverworts and the fraction of evergreen forest (R2 = 0.64, linear relationship; RMSE = 0.38) and between the fraction of evergreen forest and forest within 1-km2 areas (R2 = 0.80, linear relationship; RMSE = 29%). Conclusion: We show that for southern China, the fraction of evergreen forest and the presence probability of epiphyllous liverworts can directly be inferred by making use of SPOT-VGT NDVI imagery. The findings are fully consistent with earlier reported hotspots for epiphyllous liverworts. At the macro-habitat scale, the presence probability of epiphyllous liverworts proved quantitatively related to the fraction of evergreen forest. This suggests causal relationships with patch size and/or rainfall patterns. We believe that the derived maps may serve as a foundation for a range of further studies. © 2012 International Association for Vegetation Science.

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