Gaohuan Liu

Determination of snow cover from MODIS data for the Tibetan Plateau region

This paper addresses a snow-mapping algorithm for the Tibetan Plateau region using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Accounting for the effects of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in the rugged Tibetan Plateau region, the surface reflectance is retrieved from the TOA reflectance after atmospheric and topographic corrections. To reduce the effect of the misclassification of snow and cloud cover, a normalized difference cloud index (NDCI) model is proposed to discriminate snow/cloud pixels, separate from the MODIS cloud mask product MOD35. The MODIS land surface temperature (LST) product MOD11_L2 is also used to ensure better accuracy of the snow cover classification. Comparisons of the resulting snow cover with those estimated from high spatial-resolution Landsat ETM+ data and obtained from MODIS snow cover product MOD10_L2 for the Mount Everest region for different seasons in 2002, show that the MODIS snow cover product MOD10_L2 overestimates the snow cover with relative error ranging from 20.1% to 55.7%, whereas the proposed algorithm estimates the snow cover more accurately with relative error varying from 0.3% to 9.8%. Comparisons of the snow cover estimated with the proposed algorithm and those obtained from MOD10_L2 product with in situ measurements over the Hindu Kush-Himalayan (HKH) region for December 2003 and January 2004 (the snowy seasons) indicate that the proposed algorithm can map the snow cover more accurately with greater than 90% agreement

Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance

Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data has been widely used in the fields of crop and rice classification. The cloudy and rainy weather characteristics of the monsoon season greatly reduce the likelihood of obtaining high-quality optical remote sensing images. In addition, the diverse crop-planting system in Vietnam also hinders the comparison of NDVI among different crop stages. To address these problems, we apply a Dynamic Time Warping (DTW) distance-based similarity measure approach and use the entire yearly NDVI time series to reduce the inaccuracy of classification using a single image. We first de-noise the NDVI time series using S-G filtering based on the TIMESAT software. Then, a standard NDVI time-series base for rice growth is established based on field survey data and Google Earth sample data. NDVI time-series data for each pixel are constructed and the DTW distance with the standard rice growth NDVI time series is calculated. Then, we apply thresholds to extract rice growth areas. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice-cropping map reveal a high mapping accuracy at the national scale between the statistical data, with the corresponding R2 being as high as 0.809; however, the mapped rice accuracy decreased at the provincial scale due to the reduced number of rice planting areas per province. An analysis of the results indicates that the 500-m resolution MODIS data are limited in terms of mapping scattered rice parcels. The results demonstrate that the DTW-based similarity measure of the NDVI time series can be effectively used to map large-area rice cropping systems with diverse cultivation processes.

A River Basin over the Course of Time: Multi-Temporal Analyses of Land Surface Dynamics in the Yellow River Basin (China) Based on Medium Resolution Remote Sensing Data

The Yellow River Basin is one of China’s most densely-populated, fastest growing and most dynamic regions, with abundant natural resources and intense agricultural production. Major land policies have recently resulted in remarkable landscape modifications throughout the basin. The availability of precise regional land cover change information is crucial to better understand the prevailing dynamics and underlying factors influencing the current processes in such a complex system and can additionally serve as a valuable component for modeling and decision making. Such comprehensive and detailed information is lacking for the Yellow River Basin so far. In this study, we derived land cover characteristics and dynamics from the complete last decade based on optical high-temporal MODIS Normalized Differenced Vegetation Index (NDVI) time series for the whole Yellow River Basin. After filtering and smoothing for noise reduction with the use of the adaptive Savitzky–Golay filter, the processed time series was used to derive a large variety of phenological and annual metrics. The final classifications for the basin (2003 and 2013) were based on a random forest classifier, trained by reference samples from very high-resolution imagery. The accuracy assessment for all 18 thematic classes, which was based on a 30% reference data split, yielded an overall accuracy of 87% and 84% for 2003 and 2013, respectively. Major land cover and land use changes during the last decade have occurred on the Loess Plateau, where land and conservation reforms triggered large-scale recovery of grassland and shrubland habitat that had been previously covered by agriculture or sparse vegetation. Agricultural encroachment and urban area expansion are other processes influencing the dynamics in the basin. The necessity for regionally-adapted land cover maps becomes obvious when our land cover products are compared to existing global products, where thematic accuracy remains low, particularly in a heterogeneous landscape, such as the Yellow River Basin. The basin-wide novel land cover and land use products of the Yellow River Basin hold a large potential for climate, hydrology and biodiversity modelers, as well as river basin and regional governmental authorities and will be shared upon request.

Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images

Parameters for the tasselled cap transformation (TCT) have been derived for many sensors since 1976. There have been concerns about the comparability of TCT brightness (TCTB), greenness (TCTG), and wetness (TCTW) from different sensors because the number and bandwidth of spectral bands of the different sensors are not exactly the same and the derivation methods vary. In this research, comparisons between the TCT components derived from different combination images with a different number of spectral bands are considered. First, a new TCT based on a new data set from Landsat 8 Operational Land Imager top of atmosphere (OLI TOA) reflectance was developed. Then, a case study of the Yellow River Delta, China, demonstrated that TCT parameters derived from a Landsat 8 OLI TOA reference image from May 2013 were probably applicable to clear and nearly cloud-free images for spring, summer, and autumn over the Yellow River Delta. Finally, we compared the TCT components derived from selected bands of Landsat 8 OLI TOA reflectance images and those derived from images from other well-known moderate-resolution worldwide remote-sensing data by statistical characteristics, correlation coefficients, the optimum index factor (OIF), and classification with a support vector machine. The result supports our conclusion (a) that two new shortwave bands (Bands 1 and 9) of the Landsat 8 OLI have little effect on derivations of the TCT components and their ability to classify land cover type and (b) that Bands 4–7 in Landsat multispectral scanner, Bands 1–4 in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Bands 1–4 in Systeme Probatoire d’Observation dela Tarre 5 are sufficient for deriving TCTB, TCTG, and TCTW components and mapping land cover.

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

An object-oriented approach to map wetland vegetation:a case study of yellow river delta

Remote sensing is an important and effective tool for mapping and monitoring wetland vegetation condition and change. Because providing more spatial characteristics except spectral information, high spatial resolution images have become the main source for resources and environmental management and application. However, the conventional pixel-oriented image analysis techniques have much difficult in extracting information from high resolution images. The paper presents a case study of detecting wetland vegetation information in the Yellow River Delta based on the SPOT-5 image. Going far beyond the methodical limits of per-pixel and manual interpretation approaches, an object-oriented image analysis technique is used for extracting information and classifying the vegetation types. In the process, the SPOT-5 image is segmented into highly homogeneous image objects at multiscale and a network of image objects is generated. Not only spectral information but also spatial and contextual characteristics of image objects are used for classification, which is conducted by fuzzy logic, and image objects are evaluated using membership function classifiers. Membership functions are used to produce class description, which consists of a set of fuzzy expressions from appropriate object features. The results of vegetation information extraction are promising and the classification accuracy is significantly higher than supervised classification.

Recent climate variability and its impact on precipitation, temperature, and vegetation dynamics in the Lancang River headwater area of China

The alpine ecosystem is one of the most fragile ecosystems threatened by global climate change. The impact of climate variability on the vegetation dynamics of alpine ecosystems has become important in global change studies. In this study, spatially explicit gridded data, including the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) product (MOD11A1/A2), the Tropical Rainfall Measuring Mission (TRMM) rainfall product (3B43), and MODIS net primary productivity (NPP) product (MOD17A3), together with meteorological observation data, were used to explore the spatio-temporal pattern of climate variability and its impact on vegetation dynamics from 2000 to 2012 in the Lancang River headwater area. We found that the variation patterns of LST, precipitation, and NPP in the study area showed remarkable spatial differences. From the northwest to the southeast the spatial variation of average annual LST exhibited a decreasing–increasing–decreasing–increasing pattern. At the same time, most of the study area exhibited an increasing LST during the growing season. The annual precipitation increased in the semi-arid northern part, whereas it decreased in the semi-humid southern part. The precipitation variability during the growing season has a pattern similar to the annual precipitation variability. Although the majority of the regions have seen an NPP increase from 2000 to 2012, the responses of the vegetation to the varied climate factors were spatially heterogeneous. The alpine–subalpine meadows in the high-altitude areas were more sensitive to climate variability in the growing season. It is argued that satellite remote-sensing products have great potential in investigating the impact of climate variability on vegetation dynamics at the finer scale, especially for the Lancang River headwater area with complex surface heterogeneity.

Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales

To improve the accuracy of winter wheat yield estimation, the Crop Environment Resource Synthesis for Wheat (CERES-Wheat) model with an assimilation strategy was performed by assimilating measured or remotely-sensed leaf area index (LAI) values. The performances of the crop model for two different assimilation methods were compared by employing particle filters (PF) and the proper orthogonal decomposition-based ensemble four-dimensional variational (POD4DVar) strategies. The uncertainties of wheat yield estimates due to different assimilation temporal scales (phenological stages and temporal frequencies) and spatial scale were also analyzed. The results showed that, compared with the crop model without assimilation and with PF-based assimilation, a better yield estimate performance resulted when the POD4DVar-based strategy was used at the field scale. When using this strategy, root mean square errors (RMSE) of 523 kg·ha−1, 543 kg·ha−1 and 172 kg·ha−1 and relative errors (RE) of 5.65%, 5.91% and 7.77% were obtained at the field plot scale, a pixel scale of 1 km and the county scale, respectively. Although the best yield estimates were obtained when all of the observed LAIs were assimilated into the crop model, an acceptable estimate of crop yield could also be achieved by assimilating fewer observations between jointing and anthesis periods of the crop growth season. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. Thus, it is important to consider reasonable spatio-temporal scales to obtain tradeoffs between accuracy and effectiveness in regional wheat estimates.

A tasseled cap transformation for Landsat 8 OLI TOA reflectance images

The Tasseled Cap Transformation (TCT) has been widely used in the remote sensing community. However, TCT is sensor dependent, so a new sensor requires a reworking of the TCT starting with analysis of data structure of images. The purpose of this paper is to derive the TCT parameters for the Landsat 8 OLI TOA Reflectance images, and compare the differences between the Tasseled Cap Transformation parameters derived from the spring and autumn images. The results from this paper suggest that the TCT parameters derived from the image from October is most appropriate for regional remote sensing applications over the Yellow River Delta, China where atmospheric correction is not feasible.

Using SPOT 5 high spatial resolution image to detect vegetation patches at Gudong oil field

Numbers and areas and locations of vegetation community patch are the important parameters for vegetation function and structure researches. In this paper, vegetation community patches are extracted using SPOT 5 high spatial resolution fusion image based on mathematical morphology. Firstly, vegetation is extracted according to the scatter plot between B3 and B2. Then vegetation community patches are detected based on the criterion of circle and ellipse object. And the centers of the patches are located. Finally, two types of the spatial structure models of vegetation communities are outlined. The experiments at Gudong oil field show that the algorithms for extracting circle and ellipse object based on mathematical morphology are simple and effective for detecting vegetation community patch.