Shan Guo

Climate and Vegetation: An ERA-Interim and GIMMS NDVI Analysis

AbstractTo complement geographical presentation of remote sensing vegetation information, the authors apply Budyko’s physical state space diagram to analyze functional climate relations. As an example, the authors use Interim ECMWF Re-Analysis (ERA-Interim) global weather data to provide the statistics (1982–2006) of climate states in a two-dimensional state space spanned by water demand (net radiation N) versus water/energy limitation (dryness ratio D of net radiation over precipitation). Embedding remote sensing–based Global Inventory Modeling and Mapping Studies (GIMMS) data [normalized difference vegetation index (NDVI) > 0.1] shows the following results: (i) A bimodal frequency distribution of unit areas (pixels) is aligned near D ~ 1 but separated meridionally, associated with higher and lower net radiation. (ii) Vegetation states are represented as (N, D, NDVI) triplets that reveal temperate and tropical forests crossing the border (D ~ 1) separating energy- and water-limited climates but unexpecte...

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...

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.

Mapping Plant Functional Types over Broad Mountainous Regions: A Hierarchical Soft Time-Space Classification Applied to the Tibetan Plateau

Research on global climate change requires plant functional type (PFT) products. Although several PFT mapping procedures for remote sensing imagery are being used, none of them appears to be specifically designed to map and evaluate PFTs over broad mountainous areas which are highly relevant regions to identify and analyze the response of natural ecosystems. We present a methodology for generating soft classifications of PFTs from remotely sensed time series that are based on a hierarchical strategy by integrating time varying integrated NDVI and phenological information with topography: (i) Temporal variability: a Fourier transform of a vegetation index (MODIS NDVI, 2006 to 2010). (ii) Spatial partitioning: a primary image segmentation based on a small number of thresholds applied to the Fourier amplitude. (iii) Classification by a supervised soft classification step is based on a normalized distance metric constructed from a subset of Fourier coefficients and complimentary altitude data from a digital elevation model. Applicability and effectiveness is tested for the eastern Tibetan Plateau. A classification nomenclature is determined from temporally stable pixels in the MCD12Q1 time series. Overall accuracy statistics of the resulting classification reveal a gain of about 7% from 64.4% compared to 57.7% by the MODIS PFT products.

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.

Land-cover change of the wuda coal fire area

Coal fire generates a number of environmental problems and results in disorderly changes of landcover. Detecting the change of Land-cover is an important scientific issue of the land evaluation and the eco-environmental change forecasting. The temporal land cover maps with high accuracy make it possible to explore the eco-environmental changes of coal fire area. In thispaper, the multi-layer segmentation-based classification approach, Markov Transition Matrix methodology and Dynamic indexesby using Landsat TM data was carried out. The results reveal that coal mine and resident change are mostly in recent decades among all land cover types. Private coal mining exploitation and government administrative measures are the deriving factors.

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.