Danlu Cai

Vegetation Dynamics on the Tibetan Plateau (1982-2006): An Attribution by Ecohydrological Diagnostics

AbstractVegetation greenness distributions [based on remote sensing normalized difference vegetation index (NDVI)] and their change are analyzed as functional vegetation–climate relations in a two-dimensional ecohydrological state space spanned by surface flux ratios of energy excess (U; loss by sensible heat H over supply by net radiation N) versus water excess (W; loss by discharge Ro over gain by precipitation P). An ecohydrological ansatz attributes state change trajectories in (U, W) space to external (or climate) and internal (or anthropogenic) causes jointly with vegetation greenness interpreted as an active tracer. Selecting the Tibetan Plateau with its complex topographic, climate, and vegetation conditions as target area, ERA-Interim weather data link geographic and (U, W) state space, into which local remote sensing Global Inventory Modeling and Mapping Studies (GIMMS) data (NDVI) are embedded; a first and second period (1982–93 and 1994–2006) are chosen for change attribution analysis. The stu...

Spatiotemporal Temperature Variability over the Tibetan Plateau: Altitudinal Dependence Associated with the Global Warming Hiatus

AbstractThe recent slowdown in global warming has initiated a reanalysis of temperature data in some mountainous regions for understanding the consequences and impact that a hiatus has on the climate system. Spatiotemporal temperature variability is analyzed over the Tibetan Plateau because of its sensitivity to climate change with a station network updated to 2014, and its linkages to remote sensing–based variability of MODIS daytime and nighttime temperature are investigated. Results indicate the following: 1) Almost all stations have experienced a notable warming in the time interval 1961–2014, with most obvious warming in winter, which depends on the selected time intervals. 2) There is no clear shift from a predominant warming to a near stagnation during the most recent period (2001–present). 3) Uniform altitudinal dependence of temperature change trends could not be confirmed for all regions, time intervals, and seasons, but sometimes an altitude threshold around 3 km is apparent. 4) Most of the met...

Urbanization and the thermal environment of Chinese and US-American cities

Urbanization induced change of the thermal environment of cities is analyzed using MODIS LST and DMSP/OLS nighttime light data sets (2001-2012) to a) extend previous studies on individual megacities to a city size spectrum; b) investigate the heterogeneous surface thermal environment associated with the urbanization processes in terms of nighttime light intensity and city size; and c) provide insights in predicting how urban ecosystems will respond to urbanization for both a developing and a developed country (China and US-America), and on global scale. The following results are obtained: i) Nighttime light intensities of both countries (and globally) increase with increasing city size. ii) City size dependent annual or seasonal mean temperature tendencies show the urban effect by decreasing daytime and increasing nighttime mean temperatures (particularly in China) while variability can be related to climate fluctuations. iii) Daytime/nighttime seasonal warming tendencies (inferred from regional downscaling within city clusters) show the high light intensity regions to be stable while in low light intensity regions fluctuations prevail.

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

Biodiversity in China is analyzed based on the components of the Dynamic Habitat Index (DHI). First, observed field survey based spatial patterns of species richness including threatened species are presented to test their linear relationship with remote sensing based DHI (2001–2010 MODIS). Areas with a high cumulative DHI component are associated with relatively high species richness, and threatened species richness increases in regions with frequently varying levels of the cumulative DHI component. The analysis of geographical and statistical distributions yields the following results on interdependence, polarization and change detection: (1) The decadal mean Cumulative Annual Productivity (DHI-\(\overline{cum}\) 4) in Southeast China are in a stable (positive) relation to the Minimum Annual Apparent Cover (DHI-\(\overline{min}\)) and is positively (negatively) related to the Seasonal Variation of Greenness (DHI-\(\overline{sea}\)); (2) The decadal tendencies show bimodal frequency distributions aligned near DHI-\(\overline{min}\)~0.05 and DHI-\(\overline{sea}\)~0.5 which separated by zero slopes; that is, regions with both small DHI-min and DHI-sea are becoming smaller and vice versa; (3) The decadal tendencies identify regions of land-cover change (as revealed in previous research). That is, the relation of strong and significant tendencies of the three DHI components with climatic or anthropogenic induced changes provides useful information for conservation planning. These results suggest that the spatial-temporal dynamics of China’s terrestrial species and threatened species richness needs to be monitored by first and second moments of remote sensing based information of the DHI.

Validation of an Ideal Rainfall-Runoff Chain in a GCM Environment

A biased coinflip Ansatz provides a stochastic regional scale land surface climate model of minimum complexity, which represents physical and stochastic properties of an ideal rainfall–runoff chain. The solution yields the empirically derived Schreiber formula as an Arrhenius-type equation of state W = exp(−D). It is associated with two thresholds and combines river runoff Ro, precipitation P and potential evaporation N as flux ratios, which represent water efficiency, W = Ro/P, and vegetation states, D = N/P. This stochastic rainfall–runoff chain is analyzed utilizing a global climate model (GCM) environment. The following results are obtained for present and future climate settings: (i) The climate mean rainfall-runoff chain is validated in terms of consistency and predictability, which demonstrate the stochastic rainfall–runoff chain to be a viable surrogate model for simulating means and variability of regional climates. (ii) Climate change is analyzed in terms of runoff sensitivity/elasticity and attribution measures.

Land-Cover Characterization and Aridity Changes of South America (1982–2006): An Attribution by Ecohydrological Diagnostics

AbstractTo quantify impacts of climate change and anthropogenic activities on land surface dynamics a novel diagnostic tool is introduced, an application to the South American continent is presented, and the results are compared with observational studies. The diagnostics are performed in an ecohydrological state space spanned by surface flux ratios of excess energy U (loss by sensible heat H over supply by net radiation N) versus excess water W [loss by runoff (Ro) over gain by precipitation P]. The attribution of a changing state is deduced by rotating the (U, W) coordinates of its trajectory onto the external (or climate) and internal (or anthropogenic) forcings dependent of the regional state of aridity at the origin of the trajectory of change. Vegetation greenness (NDVI) is included in the attribution analysis as an active tracer. The first and second periods (1982–93 and 1994–2006; ERA-Interim) are chosen for change attribution analysis. 1) State space climates are characterized by a bimodal distri...

Change detection of MODIS time series using a wavelet transform

This paper presents a rapid and easy-to-use approach for change detection of NDVI time series using wavelet transform. Wavelet transform has a long history in signal and image processing field. However, the research on the large-scale remote sensing images by wavelet transform is rarely. The method in this paper is practical by using wavelet analysis on the large scale remote sensing time series. The time series based on Normalized Difference Vegetation Index (NDVI) are fundamental to analyze the dynamic nature of the vegetation. The trend of NDVI time series is extracted by using wavelet transform. For more accurate results, a seeded region growing algorithm is used for the detailed study on the certain areas. Results from the Tibetan Plateau show the wavelet transform in combination with the region growing procedure provides a efficient approach to estimate the areas where vegetation changed.

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

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 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.

Rapid assessment of large scale vegetation change based on multi-temporal phenological analysis

Detecting vegetation change is critical for earth system and sustainability science. The existing methods, however, show several limitations, including inevitable selection of imagery acquisition dates, affection from vegetation related noise on temporal trajectory analysis, and assumptions due to vegetation classification model. This paper presents a multitemporal phenological frequency analysis over a relatively short period (MTPFA-SP) methodology to detect vegetation changes. This MTPFA-SP methodology bases on the amplitude components of fast Fourier transforming (FFT) and is implemented with two steps. First, NDVI time series over two periods are transformed with FFT into frequency domain, separately. Second, amplitude components with phenological information from Step 1 are selected for further change comparison. In this methodology, component selection shows physical meanings of natural vegetation process in frequency domain. Comparisons among those selected components help enhance the ability to rapidly detect vegetation changes. To validate this MTPFA-SP methodology, we detect changes between two periods (2001-2005 and 2006-2010) in the eastern Tibet Plateau area and make two kinds of assessments. The first is for a larger scale, including statistic analysis of altitudinal zonality and latitudinal zonality. The second assessment is for rapid detection of vegetation change location. Landsat TM image were employed to validate the result.