Land use change and its ecological effect in Qian'an County of Jilin Province

Based on GIS and statistical methods, with the help of searching historical literatures and calculating the landscape indices, the land use changes of Qian'an County in both spatial and temporal aspects from 1945 to 1996 has been analyzed in this paper. And the driving forces of land use changes and their ecological effects are discussed too. The main findings of this study are as follows: (1) Land use changed greatly in Qian'an during 1945-1996, characterized by a decrease in grassland, wetland and water bodies, and an increase in cultivated land, saline-alkali land, and the land for housing and other construction purposes. Grassland decreased by 175,828.66 ha, and cultivated land increased by 102,137.23 ha over the half century. Accordingly, the main landscape type changed from a steppe landscape to a managed agricultural ecosystem. (2) Results of correlation analysis show that the land use change in the study area was mainly driven by the socioeconomic factors.　(3) The ecological effects of land use change in the area are characterized by serious salinization, degression of soil fertility and the weakening of landscape suitability.     

Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data — where do the severely degraded areas occur?

The Tibetan Plateau in Western China is the world’s largest alpine landscape, sheltering a rich diversity of native flora and fauna. In the past few decades, the Tibetan Plateau was found to suffer from grassland degradation processes. Grassland degradation is assumed to not only endanger biodiversity but also to increase the risk for natural hazards in other parts of the country which are ecologically and hydrologically connected to the area. However, the mechanisms behind the degradation processes remain poorly understood due to scarce baseline data and insufficient scientific research.We argue that remote sensing data can help to better understand degradation processes and patterns by: (1) identifying the distribution of severely degraded areas and (2) comparing the patterns of key spatial attributes of the identified areas (altitude above sea level, aspect, slope, administrative districts) with existing theories on degradation drivers. Therefore, we applied four Landsat 8 images covering large portions of the three counties Jigzhi, Baima and Darlag in the Eastern Tibetan Plateau. The dates of the Landsat scenes were selected to cover differing phenological stages of the ecosystem. Reference data were collected with a remotely piloted aircraft and a standard consumer RGB camera. To exploit the phenological information in the Landsat data as well as deal with the problem of cloud cover in multiple images, we developed a straightforward PCA-based procedure to merge the Landsat scenes. The merged Landsat data served as input to a supervised support vector machine classification which was validated with an iterative bootstrap procedure and an additional independent validation set. The considered classes were “high-cover grassland”, “grassland (including several stages of grassland vitality)”, “(severely) degraded grassland”, “green shrubland”, “grey shrubland”, “urban areas” and “water bodies”. Kappa accuracies ranged between 0.84 and 0.93 in the iterative procedure, while the independent validation led to a kappa accuracy of 0.76. Mean producer’s and user’s accuracies for all classes were higher than 80%, and confusion mainly occurred between the two shrubland classes and between the three grassland classes.Analysis of the slope, aspect and altitude values of the vegetation classes revealed that the degraded areas mostly occurred at the higher altitudes of the study area (4300–4600 m), with no strong connection to any specific slope or aspect. High-cover grassland was mostly located on sunny slopes at lower altitudes (less than 4300 m), while shrubland preferred shady, relatively steep slopes across all altitudes. These observations proved to be stable across the examined counties, while the proportions of land-cover classes differed between the examined regions. Most counties showed 5–7% severely degraded land cover. Darlag, the county located at the edge of the permafrost zone, and featuring the highest average altitude and lowest annual temperature and precipitation, was found to suffer from larger areas of severe degradation (14%).Therefore, our findings support a strong connection between degradation patterns and climatic as well as altitudinal gradients, with an increased degradation risk for high altitude areas and areas in colder and drier climatic zones. This is relevant information for pastoral management to avoid further degradation of high altitude pastures.     

基于MTFC的图像复原结果对比分析

地物在成像过程中,由于受到光学成像系统作用、地物周边环境等影响,会使得到的地物影像存在噪声、模糊等各种影像质量下降的问题,这种现象就是图像退化。根据影像退化机制,利用调制传递函数补偿(Modulation Transfer Function Compensation,MTFC)原理复原图像,提高航空面阵成像系统的成像质量,使航空面阵影像更便于判读解译。采用调制传递函数补偿(MTFC)原理进行图像复原。复原图像与原始影像相比在一定程度上边缘更加清晰,更利于判读解译,但在复原过程中也不可避免的添加了噪声。通过复原图像评价指标对比发现,利用航线方向MTF得到的复原图像效果较差,这与成像系统航线方向成像性能偏低有关。     

Determination of land degradation causes in Tongyu County,Northeast China via land cover change detection

Tongyu County in Northeast China is highly prone to land degradation due to its fragile physical settings characterized by a flat topography, a semi-arid climate, and a shallow groundwater table. This study aims to determine the causes of land degradation through detecting the long-term trend of land cover changes. Degraded lands were mapped from satellite images recorded in 1992 and 2002. These land cover maps revealed that the area subject to land degradation in the form of soil salinization, waterlogging and desertification increased from 2400 to 4214 km², in sharp contrast to most severely degraded land that decreased by 122.5 km². Newly degraded land stems from productive farmland (263 km²), harvested farmland (551 km²), and grassland (468 km²). Therefore, the worsened degradation situation is attributed to excessive reclamation of grassland for farming, over cultivation, overgrazing, and deforestation. Mechanical, biological, ecological and engineering means should be adopted to rehabilitate the degraded land.     

基于知识推理的土地退化程度评价模型

引入知识推理技术构建了一种新的土地退化程度评价模型(KR_LDAM)。模型集成了可变精度粗集和模糊神经网络两种智能化推理方法进行土地退化程度评价因子提取和退化程度分级,系统地消除了土地退化程度评价过程中专家经验知识主观不一致性以及客观数据不确定性的影响,解决了退化程度划分的模糊性和渐进性问题。选取黄土高原风沙侵蚀区域陕西省横山县为研究区域进行实例研究和模型验证。     

一种基于级联滤波算法的SAR图像噪声抑制方法

提出了一种基于偏微分方程与增强Lee滤波级联算法的SAR图像相干斑抑制方法,利用偏微分方程的边界检测特性和增强Lee滤波方法的分区域处理特性,在保持图像细节的基础上有效抑制图像中的相干斑噪声。     

长江黄河源区高寒草甸退化研究进展

长江黄河源区内高寒草甸退化是当前研究的热点问题之一。但多数研究中涉及的源区范围、高寒草甸的分类、高寒草甸的退化原因及恢复治理措施等方面,各学者的看法并不相同,难以实现各项研究结果的比较研究。从源区范围、高寒草甸的分类、高寒草甸的退化原因及恢复治理措施等方面阐述了各学者研究的差异,指出:长江黄河源区高寒草甸的进一步深入研究有必要对源区的范围进行界定;高寒草甸的分类常常因研究目的不同而有所差异,以退化程度进行的分类能够反映高寒草甸的动态变化过程,但需要定量指标对退化的不同程度进行界定;遥感影像解译的结果由于遥感影像本身的不确定性和主观因素的影响而难以统一;退化的原因目前主要集中于自然因素和人为因素两方面的研究,针对这些因素采取的控制措施在局部区域取得了一定效果,但并没有抑制高寒草甸在总体上的进一步退化,寻找一种综合的有效的治理措施还是今后的研究目标。     

楚科奇海和加拿大海盆表层沉积物中好气异养细菌的地理分布

本文用MPN法测定楚科奇海和加拿大海盆表层沉积物中好气异养细菌(GAB)的含量,并对其地理学分布进行分析。结果表明,研究区GAB的检出率高达100%。4℃时GAB的含量范围、平均含量为4.00×102-2.40×106个.g-1、1.71×106个.g-1。25℃时GAB的含量范围、平均含量为2.40×105-2.40×107个.g-1、1.10×107个.g-1。无论是含量范围,还是平均含量,均是25℃时的培养结果大于4℃时的培养结果。GAB含量的地理分布趋势是随纬度增高,含量呈降低趋势。在经度变化上则为由东向西,含量呈降低趋势。对沉积物所处水深的分析,表明其在4℃培养得出来的GAB含量随水深增加有下降的趋势,25℃培养出来的GAB含量受环境影响较小,与水深变化关系不明显。     

Validation of MODIS Vegetation Continuous Fields for monitoring deforestation and forest degradation: two cases in Mexico

This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.     

自动确定图像二值化最佳阈值的新方法

在详细分析二值化处理原理及当前技术的前提下，综合考虑图像的各种实际情况，提出了自动确定图像二值化阈值的增强大津法和边缘检测阈值法。多次实验表明，根据这两种方法确定的阈值对图像进行二值化处理，不仅可有效地解决图像灰度随地特征变化的图像处理问题，同时对于同一信号源的图像也有相当理想的处理效果，该方法可进一步应用于图像分割等。