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.     

Environmental assessment and land change analysis in seminatural land covers applicable to land management

The present research is based on the hypertemporal analysis of a set of 212 images from the NDVI index from January 2003 to March 2012 provided by the medium-resolution sensor MODIS TERRA. The study area is located in the center of the Iberian Peninsula (Spain). The specific objectives of the study are to investigate the rhythms of the annual development of the NDVI of each of the classes, determine the classes that are most sensitive to climatic variability and define the interannual sequences of variation in NDVI with an associated trend analysis. The classes situated in lower-altitude areas are strongly dependent on autumn rainfall and present negative temporal tendencies, and those situated at mountaintops and on upper slopes are correlated with spring–summer temperatures and exhibit stable or positive tendencies.     

Spatio-temporal pattern analysis of land use/cover change trajectories in Xihe watershed

Human-induced land use/cover change has been considered to be one of the most important parts of global environmental changes. In loess hilly and gully regions, to prevent soil loss and achieve better ecological environments, soil conservation measures have been taken during the past decades. The main objective of this study is to quantify the spatio-temporal variability of land use/cover change spatial patterns and make preliminary estimation of the role of human activity in the environmental change in Xihe watershed, Gansu Province, China. To achieve this objective, the methodology was developed in two different aspects, that is, (1) analysis of change patterns by binary image of change trajectories overlaid with different natural geographic factors, in which Relative Change Intensity (RCI) metric was established and used to make comparisons, and (2) analysis based on pattern metrics of main trajectories in the study area. Multi-source and multi-temporal Remote Sensing (RS) images (including Landsat ETM+ (30 June 2001), SPOT imagery (21 November 2003 and 5 May 2008) and CBERS02 CCD (5 June 2006)) were used due to the constraints of the availability of remotely sensed data. First, they were used to extract land use/cover types of each time node by object-oriented classification method. Classification results were then utilized in the trajectory analysis of land use/cover changes through the given four time nodes. Trajectories at every pixel were acquired to trace the history of land use/cover change for every location in the study area. Landscape metrics of trajectories were then analyzed to detect the change characteristics in time and space through the given time series. Analysis showed that most land use/cover changes were caused by human activities, most of which, under the direction of local government, had mainly led to virtuous change on the ecological environments. While, on the contrary, about one quarter of human-induced changes were vicious ones. Analysis through overlaying binary image of change trajectories with natural factors can efficiently show the spatio-temporal distribution characteristics of land use/cover change patterns. It is found that in the study area RCI of land use/cover changes is related to the distance to the river line. And there is a certain correlation between RCI and slope grades. However, no obvious correlation exists between RCI and aspect grades.     

Integrating the system dynamic and cellular automata models to predict land use and land cover change

Land use and land cover change (LULCC) is a widely researched topic in related studies. A number of models have been established to simulate LULCC patterns. However, the integration of the system dynamic (SD) and the cellular automata (CA) model have been rarely employed in LULCC simulations, although it allows for combining the advantages of each approach and therefore improving the simulation accuracy. In this study, we integrated an SD model and a CA model to predict LULCC under three future development scenarios in Northern Shanxi province of China, a typical agro-pastoral transitional zone. The results indicated that our integrated approach represented the impacts of natural and socioeconomic factors on LULCC well, and could accurately simulate the magnitude and spatial pattern of LULCC. The modeling scenarios illustrated that different development pathways would lead to various LULCC patterns. This study demonstrated the advantages of the integration approach for simulating LULCC and suggests that LULCC is affected to a large degree by natural and socioeconomic factors.     

Updating land cover automatically based on change detection using satellite images: case study of national forests in Southern California

Observing dynamic change patterns and higher-order complexities from remotely sensed images is warranted, but the main challenges include image inconsistency, plant phenological differences, weather variations, and difficulties of incorporating natural conditions into automatic image processing. In this study, we proposed a new algorithm and demonstrated it by producing 2002–2008 and 2010 land-cover maps in heterogeneous Southern California based on an existing 2009 land-cover map. The new algorithm improves the baseline land-cover map quality by discarding potential bad land-cover pixels and dividing each land-cover type into several subclasses. Time series Landsat images were used to detect changed and unchanged areas between baseline year and target year t. Subsequently, for each individual year t, each pixel that was identified as unchanged inherited the baseline classification. Otherwise, each pixel in the changed areas was classified by a similar surrogate majority classifier. The demonstration results in Southern California showed that the land-cover temporal pattern captured the observed successional stages of the ecosystem very well. The accuracy assessment had an overall classification accuracies ranging from 81% to 86% and overall kappa coefficients ranging from 0.79 to 0.83.     

Linking land cover dynamics with driving forces in mountain landscape of the Northwestern Iberian Peninsula

The mountainous areas of the northwestern Iberian Peninsula have undergone intense land abandonment. In this work, we wanted to determine if the abandonment of the rural areas was the main driver of landscape dynamics in Gerês–Xurés Transboundary Biosphere Reserve (NW Iberian Peninsula), or if other factors, such as wildfires and the land management were also directly affecting these spatio-temporal dynamics. For this purpose, we used earth observation data acquired from Landsat TM and ETM + satellite sensors, complemented by ancillary data and prior field knowledge, to evaluate the land use/land cover changes in our study region over a 10-year period (2000–2010). The images were radiometrically calibrated using a digital elevation model to avoid cast- and self-shadows and different illumination effects caused by the intense topographic variations in the study area. We applied a maximum likelihood classifier, as well as other five approaches that provided insights into the comparison of thematic maps. To describe the land cover changes we addressed the analysis from a multilevel approach in three areas with different regimes of environmental protection. The possible impact of wildfires was assessed from statistical and spatially explicit fire data. Our findings suggest that land abandonment and forestry activities are the main factors causing the changes in landscape patterns. Specifically, we found a strong decrease of the ‘meadows and crops’ and ‘sparse vegetation areas’ in favor of woodlands and scrublands. In addition, the huge impact of wildfires on the Portuguese side have generated new ‘rocky areas’, while on the Spanish side its impact does not seem to have been a decisive factor on the landscape dynamics in recent years. We conclude rural exodus of the last century, differences in land management and fire suppression policies between the two countries and the different protection schemes could partly explain the different patterns of changes recorded in these covers.     

Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)

Land cover changes associated with urbanisation modify microclimate, leading to urban heat islands, whereby cities are warmer than the surrounding countryside. Understanding the factors causing this phenomenon could help urban areas adapt to climate change and improve living conditions of inhabitants. In this study, land surface temperatures (LST) of Aarhus, a city in the high latitudes, are estimated from the reflectance of a thermal band (TIRS1; Band 10; 10.60–11.19 μm) of Landsat 8 on five dates in the summer months (one in 2015, and four in 2018). Spectral indices, modelled on the normalised difference vegetation index (NDVI), using all combinations of the first seven bands of Landsat 8 are calculated and their relationships with LST, analysed. Land cover characteristics, in terms of the percentages of tree cover, building cover and overall vegetation cover are estimated from airborne LiDAR data, building footprints and 4-band aerial imagery, respectively. The correlations between LST, the spectral indices and land cover are estimated.The difference in mean temperature between the rural and urban parts of Aarhus is up to 3.96 °C, while the difference between the warmer and colder zones (based on the mean and SD of LST) is up to 13.26 °C. The spectral index using the near infrared band (NIR; Band 5; 0.85-0.88 μm) and a short-wave infrared band (SWIR2; Band 7; 2.11–2.29 μm) has the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area. This index is the inverse of normalised burn ratio (NBR), which has been used for mapping burnt areas. Spectral indices using different combinations of the infrared bands have stronger correlations with LST than the more widely used vegetation indices such as NDVI. The percentage of tree cover has a higher negative correlation (Pearson’s r: -0.68 to -0.75) with LST than overall vegetation cover (r: -0.45 to -0.63). Tree cover and building cover (r: 0.53 to 0.71) together explain up to 68 % of the variation in LST. Modification of tree and building cover may therefore have the potential to regulate urban LST.     

Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach

An attempt has been made to understand the potential of temporal Advanced Wide Field Sensor (AWiFS) data aboard IRS-P6 (Resourcesat) to generate the land use land cover information along with the net sown area. The temporal data sets were georeferenced, converted to top of atmosphere reflectance and classified using decision tree classifier, See5. Results indicate that the temporal data set could give a better definition of training sites thereby resulting in good overall kappa (kappa = 0.8651) as well as individual classification accuracies. However, co-registration of temporal datasets accuracies also has got a significant influence on the classification accuracy. Temporal variation in cloud infestation and availability of appropriate data sets within the season (before harvest of the crop) has also affected the classification accuracy.     

结合光谱和纹理特征的林地变更检测

开展林地变更调查,能够为森林执法督察、林地"一张图"更新等提供精准的空间信息和属性信息,对于林业资源的监测管理具有重要意义。针对大范围多时相遥感影像人工勾绘变化图斑耗时费力的现状,提出一种结合光谱和纹理特征的林地变更检测方法,并以灵山县东北部为例,利用20171209和20180201两个时期的高分二号遥感影像进行试验。结果表明,该方法在减少人力投入、降低时间成本的基础上,不仅将遥感影像的变化检测效率提高了一半以上,同时能达到77%以上的检测准确率,在森林资源普查中具有一定的应用价值。     

Polarimetric synthetic aperture radar change detection for specific land cover types

This paper presents a supervised polarimetric synthetic aperture radar (PolSAR) change detection method applied to specific land cover types. For each pixel of a PolSAR image, its target scattering vector can be modeled as having a complex multivariate normal distribution. Based on this assumption, the joint distribution of two corresponding vectors in a pair of PolSAR images is derived. Then, a generalized likelihood ratio test statistic for the equality of two likelihood functions of such joint distribution is considered and a maximum likelihood distance measure for specific land cover types is presented. Subsequently, the Kittler and Illingworth minimum error threshold segmentation method is applied to extract the specific changed areas. Experiments on two repeat-pass Radarsat-2 fully polarimetric images of Suzhou, China, demonstrate that the proposed change detection method gives a good performance in determining the specific changed areas in PolSAR images, especially the areas that have changed to water.     

基于GIS的岷江上游流域土地利用／覆被变化研究

以岷江上游流域为对象,选取3期9景TM/+ETM遥感影像,通过多步骤最大似然监督分类、变化检测,结合空间动态分析测算模型,分析近20年土地利用/覆被变化情况。结果表明:从整个流域分析,林地面积减少,主要转化为未利用地、建设用地和耕地;未利用地在前8年以每年3.7%、后8年以每年0.4%的速度增加;建设用地在1994—2002年以每年34%的速度增加,到2002—2010年增长速度减缓;耕地总面积减少54 431hm2;从县域分析,1994—2002年间,松潘和黑水县大面积林地转为未利用地;2002—2010年间,松潘县未利用地转为林地和建设用地,茂县和汶川县未利用地面积大幅增加。该研究结论不仅为国土资源管理部门优化土地利用结构提供依据,亦为当地政府实现生态资源可持续发展提供数据支撑。     

DMC卫星影像在海啸灾情土地覆盖类型变化分析中的应用

利用DMC卫星影像,对在2004年12月26日发生在印度洋海啸中,受灾严重的印度尼西亚亚齐特别自治区进行土地覆盖类型变化比较分析,科学客观的对灾情进行评估,从而阐述了DMC卫星影像在灾情分析及土地覆盖类型监测等方面的应用前景。     

Changing landscape in the Three Gorges Reservoir Area of Yangtze River from 1977 to 2005: Land use/land cover,vegetation cover changes estimated using multi-source satellite data

The eco-environment in the Three Gorges Reservoir Area (TGRA) in China has received much attention due to the construction of the Three Gorges Hydropower Station. Land use/land cover changes (LUCC) are a major cause of ecological environmental changes. In this paper, the spatial landscape dynamics from 1978 to 2005 in this area are monitored and recent changes are analyzed, using the Landsat TM (MSS) images of 1978, 1988, 1995, 2000 and 2005. Vegetation cover fractions for a vegetation cover analysis are retrieved from MODIS/Terra imagery from 2000 to 2006, being the period before and after the rising water level of the reservoir. Several analytical indices have been used to analyze spatial and temporal changes. Results indicate that cropland, woodland, and grassland areas reduced continuously over the past 30 years, while river and built-up area increased by 2.79% and 4.45% from 2000 to 2005, respectively. The built-up area increased at the cost of decreased cropland, woodland and grassland. The vegetation cover fraction increased slightly. We conclude that significant changes in land use/land cover have occurred in the Three Gorges Reservoir Area. The main cause is a continuous economic and urban/rural development, followed by environmental management policies after construction of the Three Gorges Dam.     

Comparative analysis of land use/cover change trajectories and their driving forces in two small watersheds in the western Loess Plateau of China

To prevent soil loss and achieve better ecological environments, soil conservation measures have been taken during the past decades in the western Loess Plateau of China. In this paper, a case study was taken in Luoyu valley and Lver valley, two sub-watersheds of Xihe watershed and comparison was carried out between them. The main object of this study is to monitor land use/cover changes in the two similar small watersheds utilizing SPOT5 imageries by object-oriented human–computer interactive classification method, further develop the method of spatio-temporal analysis of land use/cover change by using pattern metrics of change trajectories and relative land use suitability index (R) in smaller watersheds, and make comparisons between the two similar small watersheds, taking water and soil conservation measures into consideration. Results show that combining GIS and RS, this method can be perfectly applied to make comparisons between different small watersheds with similar geographical backgrounds. And land use/cover spatiotemporal dynamic change characteristics can be preferably expressed by pattern metrics of change trajectories and R values based on topographical data. Different emphases have been laid according to their own geological backgrounds in the two watersheds and human activities have different effects on the landscapes of the two watersheds. The main change pattern is from slope farmland to terrace (322, the largest in Luoyu valley) or to economic fruit forest (344, the largest in Lver valley). R value of every slope grade in both of the two watersheds drops with the rising of slope degree on the whole and it shows that there is still much to do for people in the two watersheds in consideration that all the R values are still lower than 0.7.     

Land cover changed object detection in remote sensing data with medium spatial resolution

Land cover change information is crucial to analyse the process and the change patterns of environments and ecological systems. Recent studies have incorporated object-based image analysis for its ability to generate meaningful geographical objects into studies of change detection. In this research, we developed a systematic methodology to realise multi-type land cover changed object detection with medium spatial resolution remote sensing images in Beijing, China. Optimum index factor (OIF) was applied to determine the best change indicators and the chi-square transformation was carried out to determine the change threshold of the 4 classes of changed object. The clustering change vectors in the feature space were proposed to discriminate the change types. According to the accuracy assessment, the overall accuracy of changed/unchanged object detection was approximately 93.9% with an overall kappa of 0.824, and the change type discrimination also achieved an overall accuracy of 81.67%, indicating the effectiveness of the proposed method.     

Behavior-based aggregation of land categories for temporal change analysis

Comparison between two time points of the same categorical variable for the same study extent can reveal changes among categories over time, such as transitions among land categories. If many categories exist, then analysis can be difficult to interpret. Category aggregation is the procedure that combines two or more categories to create a single broader category. Aggregation can simplify interpretation, and can also influence the sizes and types of changes. Some classifications have an a priori hierarchy to facilitate aggregation, but an a priori aggregation might make researchers blind to important category dynamics. We created an algorithm to aggregate categories in a sequence of steps based on the categories’ behaviors in terms of gross losses and gross gains. The behavior-based algorithm aggregates net gaining categories with net gaining categories and aggregates net losing categories with net losing categories, but never aggregates a net gaining category with a net losing category. The behavior-based algorithm at each step in the sequence maintains net change and maximizes swap change. We present a case study where data from 2001 and 2006 for 64 land categories indicate change on 17% of the study extent. The behavior-based algorithm produces a set of 10 categories that maintains nearly the original amount of change. In contrast, an a priori aggregation produces 10 categories while reducing the change to 9%. We offer a free computer program to perform the behavior-based aggregation.     

A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level

Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level. However, the time-series, multiple bands and their derivations such as NDVI constitute a large volume of data that poses a significant challenge for automated mapping of land cover while optimally utilizing the information it contains. In this study, time-series of 10-day cloud-free MODIS composites and its derivatives – NDVI and vegetation phenology information, are fully assessed to determine the optimal data sets for deriving land cover. Three groups of variable combinations of MODIS spectral information and its derived metrics are thoroughly explored to identify the optimal combinations for land cover identification using a data mining tool.The results, based on the assessment using time-series of MODIS data, show that in general using a longer time period of the time-series data and more spectral bands could lead to more accurate land cover identification than that of a shorter period of the time-series and fewer bands. However, we reveal that, with some optimal variable combinations of few bands and a shorter period of time-series data, the highest possible accuracy of land cover classification can be achieved.     

Independent two-step thresholding of binary images in inter-annual land cover change/no-change identification

Binary images from one or more spectral bands have been used in many studies for land-cover change/no-change identification in diverse climatic conditions. Determination of appropriate threshold levels for change/no-change identification is a critical factor that influences change detection result accuracy. The most used method to determine the threshold values is based on the standard deviation (SD) from the mean, assuming the amount of change (due to increase or decrease in brightness values) to be symmetrically distributed on a standard normal curve, which is not always true. Considering the asymmetrical nature of distribution histogram for the two sides, this study proposes a relatively simple and easy ‘Independent Two-Step’ thresholding approach for optimal threshold value determination for spectrally increased and decreased part using Normalized Difference Vegetation Index (NDVI) difference image. Six NDVI differencing images from 2007 to 2009 of different seasons were tested for inter-annual or seasonal land cover change/no-change identification. The relative performances of the proposed and two other methods towards the sensitivity of distributions were tested and an improvement of ∼3% in overall accuracy and of ∼0.04 in Kappa was attained with the Proposed Method. This study demonstrated the importance of consideration of normality of data distributions in land-cover change/no-change analysis.     

A framework for automated change detection system

To enhance the ability of remote sensing system to provide accurate, timely, and complete geo-spatial information at regional or global scale, an automated change detection system has been and will continue to be one of the important and challenging problems in remote sensing. In this paper, the authors propose a framework for automated change detection system at landscape level using various geo-spatial data sources including multi-sensor remotely sensed imagery and ancillary data layers. In this framework, database is the central part and some associated techniques are discussed. These techniques includes five subsystems: automated feature-based image registration, automated change finding, automated change feature extraction and identification, intelligent change recognition, change accuracy assessment and database updating and visualization.     

A framework for automated change detection system

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