Spatial-Temporal Dynamics of China’s Terrestrial Biodiversity: A Dynamic Habitat Index Diagnostic

Cai, Danlu; Guo, Shan; Guan, Yanning; Fraedrich, Klaus; Nie, Yueping; Liu, Xuying; Bian, Xiaofeng

doi:10.3390/rs8030227

Cited by 15 publications

(18 citation statements)

References 61 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DHI, including Cumulative Annual Productivity (DHI-cum), Minimum Annual Apparent Cover (DHI-min), and Seasonal Variation of Greenness (DHI-sea), is a composite vector deduced from the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time-series, representing the vegetation dynamics. The monthly maximum FAPAR-value is the basic input dataset to compute the three relevant annual indices for the subsequent habitat analysis [51,61]. DHI-cum provides an indication of overall site vegetation productivity.…”

Section: Remote Sensing Datamentioning

confidence: 99%

“…DHI-sea refers to the variation of the vegetative productivity. Further details of the DHI can be found in previous publications [51,[61][62][63]. In this study, DHI was calculated from the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR-FAPAR 3g dataset.…”

Section: Remote Sensing Datamentioning

confidence: 99%

“…Available larger-scale species richness datasets offer a valuable database for the assessment of these remote sensing metrics. Instituto de Pesquisas Ecológicas (Brasil) offers a suite of large-scale species richness data [49], which has been validated in previous studies and exhibits high accuracy [50,51].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Liang

2018

Remote Sensing

View full text Add to dashboard Cite

Many remote sensing metrics have been applied in large-scale animal species monitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). The metrics with a high explained variance on the global scale were mainly in the energy/productivity category. The metrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Three machine learning models (i.e., support vector machine, random forests, and neural networks) were evaluated for metric integration, and the random forest model showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes' species richness on a global scale, with an explained variance that was 20% higher than any of the univariate metrics.

show abstract

Section: Remote Sensing Datamentioning

confidence: 99%

Section: Remote Sensing Datamentioning

confidence: 99%

See 1 more Smart Citation

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Liang

2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Figure 1. Geographical distribution of species richness based on field surveys (number of species/100 km 2 ) of (a) Mammals; (b) Birds; and (c) Amphibians [21][22][23]. Marine islands were not considered owing to a lack of species richness.…”

Section: Species Richness and Remote Sensing Datasetsmentioning

confidence: 99%

Effect of the Long-Term Mean and the Temporal Stability of Water-Energy Dynamics on China’s Terrestrial Species Richness

Cai

Guo

et al. 2017

IJGI

Self Cite

View full text Add to dashboard Cite

Water-energy dynamics broadly regulate species richness gradients but are being altered by climate change and anthropogenic activities; however, the current methods used to quantify this phenomenon overlook the non-linear dynamics of climatic time-series data. To analyze the gradient of species richness in China using water-energy dynamics, this study used linear regression to examine how species richness is related to (1) the long-term mean of evapotranspiration (ET) and potential evapotranspiration (PET) and (2) the temporal stability of ET and PET. ET and PET were used to represent the water-energy dynamics of the terrestrial area. Changes in water-energy dynamics over the 14-year period (2000 to 2013) were also analyzed. The long-term mean of ET was strong and positively (R 2 ∈ ( 0.40 ∼ 0.67), p < 0.05) correlated with the species richness gradients. Regions in which changes in land cover have occurred over the 14-year period (2000 to 2013) were detected from long-term trends. The high level of species richness in all groups (birds, mammals, and amphibians) was associated with relatively high ET, determinism (i.e., predictability), and entropy (i.e., complexity). ET, rather than PET or temporal stability measures, was an effective proxy of species richness in regions of China that had moderate energy (PET > 1000 mm/year), especially for amphibians. In addition, predictions of species richness were improved by incorporating information on the temporal stability of ET with long-term means. Amphibians are more sensitive to the long-term ET mean than other groups due to their unique physiological requirements and evolutionary processes. Our results confirmed that ET and PET were strongly and significantly correlated with climatic and anthropogenic induced changes, providing useful information for conservation planning. Therefore, climate management based on changes to water-energy dynamics via land management practices, including reforestation, should be considered when planning methods to conserve natural resources to protect biodiversity.

show abstract

“…Chadwick and Asner [17] used airborne imaging spectroscopy to map leaf mass area (LMA) and the foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for dominant trees in the Peruvian wet tropics, and McManus et al [15] address the relationships between foliar reflectance spectra and the phylogenetic composition of a tropical forest on Barro Colorado Island, Panama. The paper by Coops et al [24] take into account forest fragmentation and land use with distribution modeling to predict forest species migration in the Pacific Northwest of North America under climate change, while Zhang et al [25] identify a MODIS based Dynamic Habitat Index Analysis using the Photosynthetically Active Radiation (fPAR) product for China that characterizes terrestrial biodiversity, while Barboas et al [26] used imaging spectroscopy data to identify the subcanopy invasive species Psidium cattleianum in Hawaiian forests. The three-dimensional structural complexity and niche diversity in forest habitats are key predictors of biodiversity.…”

mentioning

confidence: 99%

Preface: Remote Sensing of Biodiversity

Ustin¹

2016

Remote Sensing

View full text Add to dashboard Cite

Since the 1992 Earth Summit in Rio de Janeiro, the importance of biological diversity in supporting and maintaining ecosystem functions and processes has become increasingly understood [1]. Biodiversity "connects the web of life," that is, biodiversity represents the diversity of species in an ecosystem, landscape, region and globe. It is their combined interactions, with each other and their environment that alters biogeochemical cycles and the climate system. In recent years, biodiversity has come to broadly include diversity in terms of taxonomic, systematic and genetic attributes, morphological and structural attributes, and their ecological and functional traits. Human actions are driving climate change and land use changes throughout the globe, causing major losses of biodiversity [2,3]. These actions impact the climate, influencing magnitude and the timing of disturbance events, e.g., drought and wildfires, create pollution and contamination in water, air, and soil, and cause many other impacts that accelerate species losses, altering ecosystem functions and their services. The loss of biological diversity impacts ecological processes at scales comparable to other drivers of global environmental change [4] and likely interacts synergistically with climate change [5]. Biodiversity is recognized as a key factor in the maintenance of healthy ecosystems and for the sustainability of conservation efforts. Losses of biodiversity reduce the stability and resilience of ecosystems through the loss of functional traits associated with resource capture and decomposition [1]. The rapid pace of global change requires increased knowledge about species composition, numbers of species, and the states of health and the conditions for global ecosystems in order to respond effectively. Tittensor et al. (2014) [6] show that many of the 20 Aichi indicator targets from the Convention on Biological Diversity are unlikely to be met by 2020. The development of large plant trait databases like TRY [7,8] that have data on thousands of vascular plant species (46,085 species) still remain significantly under-sampled, particularly outside the northern temperate zone [9]. Remote sensing provides the only feasible way to measure and monitor biodiversity changes at the scales necessary. Nonetheless, until recently, there has been little success in monitoring ecologically meaningful aspects of diversity (e.g., alpha, beta, and gamma diversity). Today, there are an increasing number of remote sensing satellites and aircraft instruments that can provide a wide range of observational capability, in terms of spatial, temporal, and spectral resolutions, especially when combined with "big data" computational capacity and in situ monitoring systems. Similarly, significant progress in image processing algorithms has increased the potential for the successful characterization of biodiversity at various scales.We are pleased to present this special issue of 18 state-of-the-science papers [10-27] covering a wide range biological diversity issues ass...

show abstract

Spatial-Temporal Dynamics of China’s Terrestrial Biodiversity: A Dynamic Habitat Index Diagnostic

Cited by 15 publications

References 61 publications

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Effect of the Long-Term Mean and the Temporal Stability of Water-Energy Dynamics on China’s Terrestrial Species Richness

Preface: Remote Sensing of Biodiversity

Contact Info

Product

Resources

About