Yichen Tian

Crop classification using multi-configuration SAR data in the North China Plain

Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late jointing and flowering periods achieved sufficient classification accuracy, and adding data to the early jointing period had little effect on improving classification accuracy. In addition, texture features of SAR data were also useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring and can become a suitable complementary data source to optical data.

Examining Land Use and Land Cover Spatiotemporal Change and Driving Forces in Beijing from 1978 to 2010

Land use and land cover (LULC) datasets for Beijing in 1978, 1987, 1992, 2000 and 2010 were developed from Landsat images using the object-oriented classification approach. The relationships between social-economic, demographic and political factors and time-series LULC data were examined for the periods between 1978 and 2010. The results showed the effectiveness of using the object-oriented decision tree classification method for LULC classification with time series of Landsat images. Combined with anthropogenic driving forces, our research can effectively explain the detailed LULC change trajectories corresponding to different stages and give new insights for Beijing LULC change patterns. The results show a significant increase in forest and built-up areas, but a decrease in arable lands, due to urbanization and reforestation. Large ecological projects result in an increase of forest areas and population, and economic conditions result in urban expansion. The

Vegetation classification method with biochemical composition estimated from remote sensing data

In this article, a vegetation classification hypothesis based on plant biochemical composition is presented. The basic idea of this hypothesis is that the vegetation species/crops have their own biochemical composition characteristics, which are separable from each other for those co-existing species at a specific region. Therefore, vegetation species can be classified based on the biochemical composition characteristics, which can be retrieved from hyperspectral remote-sensing data. In order to test this hypothesis, an experiment was conducted in north-western China. Field data on the biochemical compositions and spectral responses of different plants and an Earth-observing 1 (EO-1) Hyperion image were simultaneously collected. After analysing the relationship between biochemical composition and spectral data collected from Hyperion, the vegetation biochemical compositions were estimated using sample biochemical data and bands of Hyperion data. The vegetation classification was completed using the biochemical content classifier (BCC) and maximum-likelihood classifier (MLC) with all Hyperion bands (MLC_A) and selected bands (MLC_S), which were used for estimating considered biochemical contents (cellulose and carotenoid). The overall classification accuracy of the BCC (95.2%) was as good as MLC_S (95.2%) and better than MLC_A (91.1%), as was the kappa value (BCC 92.849%, MLC_S 92.845%, MLC_A 86.637%), suggesting that the BCC was a feasible classification method. The biochemical-based classification method has higher vegetation classification accuracy and execution speed, reduces data dimension and redundancy and needs only a few spectral bands to retrieve biochemical contents instead of using all of the spectral bands. It is an effective method to classify vegetation based on plant biochemical composition characteristics.

Spectral Discrimination of Opium Poppy Using Field Spectrometry

Opium is a narcotic obtained from opium poppy and is the raw material of heroin for the illegal drug trade. Monitoring the illegal concentrated cultivation of opium poppy in major regions is critical for the understanding by governments and international communities of the scale of illegal drug trade. This paper investigates whether opium poppy can be discriminated from its coexisting plants using analytical-spectral-device field spectrometer data in the visible to short-wave infrared spectral range. Canopy spectral measurements were conducted during three different growth periods of opium poppy. A synthetic method with three analysis levels was applied to discriminate opium poppy from other species and to select optimal bands for opium poppy discrimination. First, the Mann-Whitney U-test method was used to test the spectral reflectance difference between opium poppy and coexisting crops at each wavelength. Then, the Jeffries-Matusita distance and band correlation analysis were conducted to select the optimal wavebands for discriminating opium poppy using the significant wavebands from the test results. Finally, classification and regression tree analysis was employed to validate the classification accuracy based on the selected optimal wavebands. The results indicated that the spectral reflectance of opium poppy was significantly different from that of coexisting crops in many surveyed wavebands, and opium poppy could be discriminated using a field survey spectrum at canopy level. The best time for discriminating opium poppy from coexisting crops was around flowering time. This paper provided the prerequisite for monitoring opium poppy using satellite remote sensing data in some regions of concern.

Opium poppy monitoring with remote sensing in North Myanmar

BACKGROUND Myanmar has long been a focus of the international community as a major opium poppy cultivation region. METHOD This study used remote sensing technology and ground verification to monitor opium poppy cultivation for three opium poppy growth seasons in North Myanmar. RESULTS The study found that opium poppy cultivation has remained high. In 2005-6, 2006-7 and 2007-8 growing seasons the total areas monitored were 52,482 km(2), 178,274 km(2) and 236,342 km(2) and the total cultivated area of opium poppy was 8959 ha, 18,606 ha and 22,300, respectively. This was significantly less than cultivation levels reported during the 1990s. The major cultivation regions were located in Shan State, producing 88% of total poppy cultivation in North Myanmar in 2007-8. The opium poppy was mainly cultivated in the interlocking regions controlled by the local armed forces in Shan State. The field survey noted that most households in this area were poor and poppy cultivation was a main source of income. There were also differences between our figures on poppy cultivation and those reported by United Nations Office on Drugs and Crime. CONCLUSION Our study shows that although the opium poppy cultivation in North Myanmar has reduced over recent years, it remains a major producer of opium and to which the international community needs to pay attention, especially in those areas controlled by local armed forces.

An effective field method of crop proportion survey in China based on GVG integrated system

With the great agriculture population and limited cropland, it is very important to estimate the output of grain produce in China by remote sensing technology. However, the smallholders of cropland can plant what they like, thus it is difficult to monitor the crop planting proportion with only RS images, even with IKONOS/QUICKBIRD data. In GVG agro-status sampling system, the video camera connected with a notebook by a video capture card and GPS receive card are integrated into the GIS environment. GVG is fixed on a motor and restore the crop pictures and their GPS data when the car is moving along the country road derived from the linear sampling frame in a plantation division unit. A great many of crop pictures along the sample lines are obtained on the field in a limited time, and then all pictures with geographical data are interpreted to calculate the ratio of each type of crop plantation. The crop proportion of plantation division unit is estimated by all pictures plantation ratio of sample line due to this unit. This literature review has demonstrated the GVG systems hardwares constitute, working principle and the case studies. GVG agro-status sampling system not only can be acquire every crops planting proportion of large areas in short time, but also can check up the results, which get from remote sensing crop classification

Spatial pattern of soil and water loss and its affecting factors analysis in the upper basin of Miyun reservoir

By interactive interpretation method with the assistance of GIS and RS, soil and water loss classification information in the upper basin of Miyun reservoir was derived from Landsat Enhanced Thematic Mapper (ETM) images and other relevant datum. The ecological and environmental background database was built in the region, which consists of the controlling factors, such as vegetation fraction, ravine density, isohyet map, to the intensity of soil and water loss. Although these factors, directly or indirectly influencing the process of soil erosion, are the key parameters in the widely applied revised universal soil loss equation (RUSLE), they may have the different contribution to the soil erosion at the diverse spatial scale. The correlation between these environmental factors and soil erosion intensity was researched at a regional scale in this paper. The purpose of the paper is to bring the reference of the case study for the application of the revised universal soil loss equation in the same region, as well as to verify these factors data, and the veracity of the result of RUSLE application

Illicit Vessel Identification In Inland Waters using SAR Image

The paper first introduces the modified two-parameter CFAR algorithm used to detect ship targets in inland waters, and then uses principal curves and neural networks to extract the waterway course. Through comparing the detection results and the extracted waterway course those vessels not complying with water traffic rules and those potential illicit fishing vessels can be easily identified.

A method of estimating crop acreage in large-scale by unmixing of MODIS data

Crop acreage monitoring is basic information necessary for wise management of plant natural resources. Recent developments in remote sensing technologies have created promising opportunities for improving agricultural statistics systems. The Moderate Resolution Imaging Spectroradiometer (MODIS) is one detector board on Terras (EOS-AM1), which was lunched on December 18, 1999 by NASA. It offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop acreage estimating. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation, especially in China. This paper developed and tested an unmixing approach with MODIS data that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. A zone that can get LANDSAT/TM data was chosen to be train dataset in this method. The paper assumes that the crop area estimating from LANDSAT/TM data is correct; in the training zone the crop area based on MODIS data can get from the classification result of LANDSAT/TM data. Then we can extend the result to a large-scale; finally we compare the result to national statistic data. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of crop distributions using MODIS data. I INTRODUCTION

A segmentation and classification approach of land cover mapping using Quick Bird image

The ability to map and monitor the spatial extent of the built environment, and associated temporal changes, has important societal and economic meaning. In this paper, the high spatial resolution of the image - Quick Bird was used to create a detailed land cover maps of Taigu region, Shanxi province, China. Adopting object-oriented image segmentation and classification which is based on fuzzy logic allows the integration of a broad spectrum of different object features, such as spectral values, shape and texture. In this study we use not only image objects attributes, but also the relationship between networked image objects, it can perform sophisticated classification and get satisfied classification result. The aim of this work was to develop an object-oriented segmentation and classification approach for operational land cover mapping