Yaoliang Chen

Mapping the land-cover distribution in arid and semiarid urban landscapes with Landsat Thematic Mapper imagery

Mapping the land-cover distribution in arid and semiarid urban landscapes using medium spatial resolution imagery is especially difficult due to the mixed-pixel problem in remotely sensed data and the confusion of spectral signatures among bare soils, sparse density shrub lands, and impervious surface areas (ISAs hereafter). This article explores a hybrid method consisting of linear spectral mixture analysis (LSMA), decision tree classifier, and cluster analysis for mapping land-cover distribution in two arid and semiarid urban landscapes: Urumqi, China, and Phoenix, USA. The Landsat Thematic Mapper (TM) imagery was unmixed into four endmember fraction images (i.e. high-albedo object, low-albedo object, green vegetation (GV), and soil) using the LSMA approach. New variables from these fraction images and TM spectral bands were used to map seven land-cover classes (i.e. forest, shrub, grass, crop, bare soil, ISA, and water) using the decision tree classifier. The cluster analysis was further used to modify the classification results. QuickBird imagery in Urumqi and aerial photographs in Phoenix were used to assess classification accuracy. Overall classification accuracies of 86.0% for Urumqi and 88.7% for Phoenix were obtained, much higher accuracies than those utilizing the traditional maximum likelihood classifier (MLC). This research demonstrates the necessity of using new variables from fraction images to distinguish between ISA and bare soils and between shrub and other vegetation types. It also indicates the different effects of spatial patterns of land-cover composition in arid and semiarid landscapes on urban land-cover classification.

Detecting fractional land-cover change in arid and semiarid urban landscapes with multitemporal Landsat Thematic mapper imagery

Pixel-based approaches are commonly used for urban land-cover classification and change detection, but the results are often inaccurate in arid and semiarid urban landscapes due to the mixed-pixel problem and similar spectral signatures between impervious surface areas (ISAs) and bare soils. This research proposes a subpixel-based approach to examine land-cover change in Urumqi and Phoenix urban landscapes using multitemporal Landsat Thematic Mapper (TM) imagery. Linear spectral mixture analysis (SMA) was used to unmix TM multispectral imagery into four fractions – high-albedo object, low-albedo object, green vegetation (GV), and soil. ISA was determined from the sum of high-albedo and low-albedo fraction images after removal of non-ISA in both fraction images. The ISA, vegetation abundance, and soil images at different dates were used to examine their change over time. The results indicate that this subpixel-based approach can successfully detect small changes of urban land covers in medium spatial resolution images which pixel-based approaches cannot.

Detection of vegetation abundance change in the alpine tree line using multitemporal Landsat Thematic Mapper imagery

Detection of alpine tree line change using pixel-based approaches on medium spatial resolution imagery is challenging because of very slow tree sprawl without obvious boundaries. However, vegetation abundance or density in the tree line zones may change over time and such a change may be detected using subpixel-based approaches. In this research, a linear spectral mixture analysis (LSMA)-based approach was used to examine alpine tree line change in the Northern Tianshan Mountains located in Northwestern China. Landsat Thematic Mapper (TM) imagery was unmixed into three fraction images (i.e. green vegetation – GV, shade, and soil) using the LSMA approach. The GV and soil fractions at different years were used to examine vegetation abundance change based on samples in the alpine tree line. The results show that Picea schrenkiana abundance around the top of the forested area increased approximately by 18.6% between 1990 and 2010, but remained stable in the central forest region over this period. Juniperus sabina abundance around the top of the forested area, in the central scrub region, and at the top of the scrub region increased approximately by 19.3%, 8.2%, and 15.6%, respectively. The increased vegetation abundance and decreased soil abundance of both P. schrenkiana and J. sabina indicate vegetation sprawl in the alpine tree line between 1990 and 2010. This research will be valuable for better understanding the impacts of climate change on vegetation change in the alpine tree line of central Asia.

Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions

Oilseed rape (Brassica napus L.) is one of the three most important oil crops in China, and is regarded as a drought-tolerant oilseed crop. However, it is commonly sensitive to waterlogging, which usually refers to an adverse environment that limits crop development. Moreover, crop growth and soil irrigation can be monitored at a regional level using remote sensing data. High spatial resolution optical satellite sensors are very useful to capture and resist unfavorable field conditions at the sub-field scale. In this study, four different optical sensors, i.e., Pleiades-1A, Worldview-2, Worldview-3, and SPOT-6, were used to estimate the dry above-ground biomass (AGB) of oilseed rape and track the seasonal growth dynamics. In addition, three different soil water content field experiments were carried out at different oilseed rape growth stages from November 2014 to May 2015 in Northern Zhejiang province, China. As a significant indicator of crop productivity, AGB was measured during the seasonal growth stages of the oilseed rape at the experimental plots. Several representative vegetation indices (VIs) obtained from multiple satellite sensors were compared with the simultaneously-collected oilseed rape AGB. Results showed that the estimation model using the normalized difference vegetation index (NDVI) with a power regression model performed best through the seasonal growth dynamics, with the highest coefficient of determination (R2 = 0.77), the smallest root mean square error (RMSE = 104.64 g/m2), and the relative RMSE (rRMSE = 21%). It is concluded that the use of selected VIs and high spatial multiple satellite data can significantly estimate AGB during the winter oilseed rape growth stages, and can be applied to map the variability of winter oilseed rape at the sub-field level under different waterlogging conditions, which is very promising in the application of agricultural irrigation and precision agriculture.

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

Abstract The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.