Satellite-derived vegetation indices contribute significantly to the prediction of epiphyllous liverworts

Jiang, Yanbin; Wang, Zhihui; Bie, C.A.J.M. de; Liu, Xuehua; Song, Shanshan; Zhang, Li; Jian, Wang; Shao, Xingling

doi:10.1016/j.ecolind.2013.10.024

Cited by 38 publications

(38 citation statements)

References 55 publications

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“…Natural forest had the highest NDVI value during all periods (mainly consisting of trees) while unstocked forest consisting of grasses and shrubs had slightly lower NDVI values, but the curves followed the same general form. In the hillside fields, the crops were planted more densely than in the flat fields [56] with resultant NDVI values higher overall in comparison to flatland fields. Natural forest showed the highest NDVI value, followed by unstocked forest, plateau vegetation, and hillside fields, followed by the flatland fields and paddies.…”

Section: Classification Algorithm Using Rf and Validationmentioning

confidence: 94%

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Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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Abstract:Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas.

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Section: Classification Algorithm Using Rf and Validationmentioning

confidence: 94%

“…It reflects surface canopy moisture and forest humidity [55,56]. The NDWI has been used for monitoring water stress [57], and for mapping burnt areas in boreal forest [58].…”

Section: Normalized Indicesmentioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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“…In order to eliminate the potential effects caused by artificial boundaries, only 80 species endemic to China were used in this study ( Figure 1). For each species, 70% of the occurrence data was used for model training and 30% for validation (Williams et al 2009, Kumar 2012, Jiang et al 2014. For each selected rhododendron species, four models using different suites of input data (climatic, topographic, edaphic and all variables combined) were generated (the number of samples used for training and evaluation in the full model are given in the Appendix, Table A1).…”

Section: Species Distribution Modellingmentioning

confidence: 99%

Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants

Groen

Wang

et al. 2016

International Journal of Geographical Information Science

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2016): Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plantsABSTRACTUnderstanding the environmental factors determining the distribution of species with different range sizes can provide valuable insights for evolutionary ecology and conservation biology in the face of expected climate change. However, little is known about what determines the variation in geographical and elevational ranges of alpine and subalpine plant species. Here, we examined the relationship between geographical and elevational range sizes for 80 endemic rhododendron species in China using Spearman's rank-order correlation. We ran the species distribution modelmaximum entropy modelling (MaxEnt) -with 27 environmental variables. The importance of each variable to the model prediction was compared for species groups with different geographical and elevational range sizes. Our results showed that the correlation between geographical and elevational range sizes of rhododendron species was not significant. Climate-related variables were found to be the most important factors in shaping the distributional ranges of alpine and subalpine plant species across China. Species with geographically and elevationally narrow ranges had distinct niche requirements. For geographical ranges, the narrowranged species showed less tolerance to niche conditions than the wide-ranged species. For elevational ranges, compared with the wide-ranged species, the narrow-ranged species showed an equivalent niche breadth, but occurred at different niche position along the environmental gradient. Our findings suggest that over large spatial extents the elevational range size can be a complementary trait of alpine and subalpine plant species to geographical range size. Climatic niche breadth, especially the range of seasonal variability, can explain species' geographical range sizes. Changes in climate may influence the distribution of rhododendrons, with the effects likely being felt most by species with either a narrow geographical or narrow elevational range. ARTICLE HISTORY

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“…The ability to remotely and non-invasively measure indicators of environmental and biological change have been a driving force in ecological research over the past decade from satellite imagery of deforestation to non-invasive DNA testing of various animal populations using shed materials (Caruana, 2011;Garshellis, 2006;Jha and Bawa, 2006;Jiang et al, 2014;Mucci and Randi, 2007;Vina et al, 2004). A promising remotely-collected integrative ecosystem indicator is provided by aerosolized proteins, or bioaerosols (Castillo et al, 2012;Huffman et al, 2012;Pauliquevis et al, 2012;Santarpia et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Ground level environmental protein concentrations in various ecuadorian environments: Potential uses of aerosolized protein for ecological research

Staton

Woodward

Castillo

et al. 2015

Ecological Indicators

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Satellite-derived vegetation indices contribute significantly to the prediction of epiphyllous liverworts

Cited by 38 publications

References 55 publications

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants

Ground level environmental protein concentrations in various ecuadorian environments: Potential uses of aerosolized protein for ecological research

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