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1.
Capturing the scope and trajectory of changes in land use and land cover (LULC) is critical to urban and regional planning, natural resource sustainability and the overall information needs of policy makers. Studies on LULC change are generally conducted within peaceful environments and seldom incorporate areas that are politically volatile. Consequently, the role of civil conflict on LULC change remains elusive. Using a dense time stack of Landsat Thematic Mapper images and a hybrid classification approach, this study analysed LULC changes in Kono District between 1986–1991, 1991–2002 and 2002–2007 with the overarching goal of elucidating deviations from typical changes in LULC caused by Sierra Leone's civil war (1991–2002). Informed by social survey and secondary data, this study engaged the drivers that facilitated LULC changes during war and non-war periods in a series of spatial regression models in exploring the interface between civil conflict and LULC change.  相似文献   

2.
This paper presents a land use and land cover (LULC) classification approach that accounts landscape heterogeneity. We addressed this challenge by subdividing the study area into more homogeneous segments using several biophysical and socio-economic factors as well as spectral information. This was followed by unsupervised clustering within each homogeneous segment and supervised class assignment. Two classification schemes differing in their level of detail were successfully applied to four landscape types of distinct LULC composition. The resulting LULC map fulfills two major requirements: (1) differentiation and identification of several LULC classes that are of interest at the local, regional, and national scales, and (2) high accuracy of classification. The approach overcomes commonly encountered difficulties of classifying second-level classes in large and heterogeneous landscapes. The output of the study responds to the need for comprehensive LULC data to support ecosystem assessment, policy formulation, and decision-making towards sustainable land resources management.  相似文献   

3.
Accelerated soil erosion, high sediment yields, floods and debris flow are serious problems in many areas of Iran, and in particular in the Golestan dam watershed, which is the area that was investigated in this study. Accurate land use and land cover (LULC) maps can be effective tools to help soil erosion control efforts. The principal objective of this research was to propose a new protocol for LULC classification for large areas based on readily available ancillary information and analysis of three single date Landsat ETM+ images, and to demonstrate that successful mapping depends on more than just analysis of reflectance values. In this research, it was found that incorporating climatic and topographic conditions helped delineate what was otherwise overlapping information. This study determined that a late summer Landsat ETM+ image yields the best results with an overall accuracy of 95%, while a spring image yields the poorest accuracy (82%). A summer image yields an intermediate accuracy of 92%. In future studies where funding is limited to obtaining one image, late summer images would be most suitable for LULC mapping. The analysis as presented in this paper could also be done with satellite images taken at different times of the season. It may be, particularly for other climatic zones, that there is a better time of season for image acquisition that would present more information.  相似文献   

4.
This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.  相似文献   

5.
Abstract

Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) amongst many others. In this study, LULC types of Selangor area were analyzed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. SPOT 5 satellite images with four spectral bands from 2003 and 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5% and 91%) performed better results than the pixel-based DT (68.6% and 68.4%) and object-based SVM (80.6% and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning.  相似文献   

6.
A challenge in land change science is to assess the causes and consequences of LULC change and associated pattern–process relations. Increasingly, land change organizations are examining land use at local to global scales for historical, contemporary and future periods through scenarios that assess population–environment interactions. Spatial analytical tools in GIScience are being used to link people and environment and to search for the distal and proximate factors that affect local to global land use patterns. Spatial simulation models that rely upon complexity theory as the framework and agent-based models as the analytical approach offer the capability to inform through experimentation about land issues important to science and society. Using a stylized landscape where a selected set of key social, geographical and ecological elements are spatially organized, we describe how land dynamics can be examined through agent-based models as educational tools that are useful in the classroom, boardroom and public forums.  相似文献   

7.
Abstract

This study proposes the development of a multi-sensor, multi-spectral composite from Landsat-8 and Sentinel-2A imagery referred to as ‘LSC’ for land use land cover (LULC) characterisation and compared with respect to the hyperspectral imagery of the EO1: Hyperion sensor. A three-stage evaluation was implemented based on the similarity observed in the spectral response, supervised classification results and endmember abundance information obtained using linear spectral unmixing. The study was conducted for two areas located around Dhundi and Rohtak in Himachal Pradesh and Haryana, respectively. According to the analysis of the spectral reflectance curves, the spectral response of the LSC is capable of identifying major LULC classes. The kappa accuracy of 0.85 and 0.66 was observed for the classification results from LSC and Hyperion data for Dhundi and Rohtak datasets, respectively. The coefficient of determination was found to be above 0.9 for the LULC classes in both the datasets as compared to Hyperion, indicating a good agreement. Thus, these three-stage results indicated the significant potential of a composite derived from freely available multi-sensor multi-spectral imagery as an alternative to hyperspectral imagery for LULC studies.  相似文献   

8.
Effect of canal on land use/land cover using remote sensing and GIS   总被引：3，自引：0，他引：3  
The monitoring of land use/land covers (LULCs) is an indispensable exercise for all those involved in executing policies to optimize the use of natural resources and minimize the ill impacts on the environment. The study here aims at analyzing the changes that occurred in LULC over a time span from 1990 to 2005 using multi date data of a part of Punjab. The digital data consisted of two sets of Landsat Thematic Mapper (TM) data and one set of IRS-1C data. Utilizing hybrid classification technique for interpretation and on field validation, it has been found that canal irrigation leads to changes in LULC as there is a change in cropping pattern as well as increase in water logged area.  相似文献   

9.
Soil is a vital part of the natural environment and is always responding to changes in environmental factors, along with the influences of anthropogenic factors and land use changes. The long-term change in soil properties will result in change in soil health and fertility, and hence the soil productivity. Hence, the main aim of this paper focuses on the analysis of land use/land cover (LULC) change pattern in spatial and temporal perspective and to present its impact on soil properties in the Merawu catchment over the period of 18?years. Post classification change detection was performed to quantify the decadal changes in historical LULC over the periods of 1991, 2001 and 2009. The pixel to pixel comparison method was used to detect the LULC of the area. The key LULC types were selected for investigation of soil properties. Soil samples were analysed in situ to measure the physicochemical soil properties. The results of this study show remarkable changes in LULC in the period of 18?years. The effect of land cover change on soil properties, soil compaction and soil strength was found to be significant at a level of <0.05.  相似文献   

10.
This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification.  相似文献   

11.
由于地理国情林地数据不包含实地面积小于400 m2的树木或四旁单排林,若仅利用地理国情的林地数据统计区域森林覆盖率,将对四旁树面积较大地区的林地统计结果产生较大误差。为提取区域内准确的林地覆盖与空间分布状况,本文借助地理国情地表覆盖数据,提出了一种基于北京二号高分辨率遥感影像的林地提取方法。首先,根据遥感影像光谱特征,将研究区按植被、道路、铁路、建筑用地进行地类划分,并基于遥感影像进行各地类的样本提取,通过可分离检验的样本利用最大似然分类提取研究区内植被覆盖范围;然后,借助地理国情地表覆盖数据,使用叠置分析剔除误分、错分地类,得到区域林地的空间分布。试验结果表明：(1)研究区内林地覆盖率为20.3%,尚未满足北京新一轮林地规划需求;(2)地理国情地表覆盖数据内林地面积占提取林地总面积的54.03%,说明在部分地区使用本文方法对地理国情林地数据进行补充是有必要的。通过将试验结果与遥感影像进行目视比对并结合外业调查结果发现,提取的林地空间分布情况与实际分布基本相符。本文为地理国情的应用提供了一种新方法,研究结果可辅助区域的绿色发展规划,有助于构建科学的生态空间格局。  相似文献   

12.
Coastal zones are most vulnerable for landuse changes in this rapid industrialization and urbanization epoch. It is necessary to evaluate land use — land cover (LULC) changes to develop efficient management strategies. The main objective of this paper is to evaluate and quantity Abu Dhabi coastal zone LULC changes from 1972 to 2000 using multi-temporal LANDSAT satellite data and digital change detection techniques. Supervised classification coupled with expert visual interpretation techniques were used to produce LULC classified images with an accuracy of 88%. Change detection process was achieved by applying post-classification comparison techniques in ENVI software. From this study it has been observed that the important coastal landuse types of Abu Dhabi coast .i.e. wetlands and woody Vegetation (Mangrove, represented by a single species,Avicennia marina) have been reduced drastically in their extent due to reclamation, dredging, tipping and other anthropogenic activities along the coastal zone. However, it has been observed that there is rapid increase in the man-made plantation and managed vegetation from 1990 to 2000 due to the Abu Dhabi government initiation. This study has given good insight into Abu Dhabi coastal zone changes during last 3 decades.  相似文献   

13.
Abstract

An integrated Markov Chain and Cellular Automata modelling (CA MARKOV), multicriteria evaluation techniques have been applied to produce transition probability. The unsupervised method was employed to classify the satellite images of year 1985, 1995, 2005 and 2015 to meet the magnitude of LULC change. Results showing the spatial pattern of the sub-basin is largely influenced by the biophysical and socio-economic drivers leading to growth of agricultural lands and built-up area in the basin. Simulated plausible future LULC changes for 2025 which is based on a CA MARKOV that integrates Markovian transition probabilities computed from satellite-derived LULC maps and a CA contiguity spatial filter (5 × 5). Further, the fragmentation analysis was performed to check the fragmentation scenario in the year 2025. The result for year 2025 with reasonably good accuracy will be useful to the planners, policy- and decision-makers.  相似文献   

14.
With increasing resolution of the remotely sensed data the problems of images contaminated by mixed pixels arc frequent. Conventional classification techniques often produce erroneous results when applied to images dominated by mixed pixels. This may load to unrealistic representation of land cover, thereby, affecting efficient planning, management and monitoring of natural resources. Consequently, soft classification techniques providing sub-pixel land cover information may have to be utilised. From a range of soft classification techniques, the present study focuses on the utility of conventional maximum likelihood classifier and linear mixture modelling for sub-pixel. land cover classifications. The accuracy of the soft classifications has been assessed using distance measures and correlation co-efficient. The results show that linear mixture modelling has produced accuracies comparable to maximum likelihood classifier. Besides this the correlations between actual land cover proportions and proportions from linear mixture modelling, though not strong, arc statistically significant at 95% level of confidence. It has also been observed that the normalised likelihoods of maximum likelihood classifier also show strong correlations with the actual land cover proportions on ground and therefore has the potential to be used as a soft classification technique.  相似文献   

15.
‘Watershed Management’ has assumed urgency for planned development of land and water resources and to arrest land degradation process to preserve environment and ecological balance. Decision support to such management planning requires scientific knowledge of resources information, expected runoff and sediment yield, priority classification of watersheds for conservation planning, monitoring of watershed for environmental impact assessment and technologies of GIS for data base creation, scenario development and appropriate decision making. Remote sensing technique is ideally suited to evolve such a management strategy. Scientific basis of this approach is explained.  相似文献   

16.
Recently, object-oriented classification techniques based on image segmentation approaches are being studied using high-resolution satellite images to extract various thematic information. In this study different types of land use/land cover (LULC) types were analysed by employing object-oriented classification approach to dual TerraSAR-X images (HH and HV polarisation) at African Sahel. For that purpose, multi-resolution segmentation (MRS) of the Definiens software was used for creating the image objects. Using the feature space optimisation (FSO) tool the attributes of the TerraSAR-X image were optimised in order to obtain the best separability among classes for the LULC mapping. The backscattering coefficients (BSC) for some classes were observed to be different for HH and HV polarisations. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value 0.82 was resulted from the classification scheme, while accuracy differences among the classes were kept minimal. Finally, the results highlighted the importance of a combine use of TerraSAR-X data and object-oriented classification approaches as a useful source of information and technique for LULC analysis in the African Sahel drylands.  相似文献   

17.
The groundwater occurrence and movement within the flow systems are governed by many natural factors like topography, geology, geomorphology, lineament structures, soil, drainage network and land use land cover (LULC). Due to complex natural geological/hydro-geological regime a systematic planning is needed for groundwater exploitation. It is even more important to characterize the aquifer system and delineate groundwater potential zones in different geological terrain. The study employed integration of weighted index overlay analysis (WIOA) and geographical information system (GIS) techniques to assess the groundwater potential zones in Krishna river basin, India and the validation of the result with existing groundwater levels. Different thematic layers such as geology, geomorphology, soil, slope, LULC, drainage density, lineament density and annual rainfall distribution were integrated with WIOA using spatial analyst tools in Arc-GIS 10.1. These thematic layers were prepared using Geological survey of India maps, European Digital Archive of Soil Maps, Bhuvan (Indian-Geo platform of ISRO, NRSC) and 30 m global land cover data. Drainage, watershed delineation and slope were prepared from the Shuttle Radar Topography Mission digital elevation model of 30 m resolution data. WIOA is being carried out for deriving the normalized score for the suitability classification. Weight factor is assigned for every thematic layer and their individual feature classes considering their significant importance in groundwater occurrence. The final map of the study area is categorized into five classes very good, good, moderate, poor and very poor groundwater potential zones. The result describes the groundwater potential zones at regional scale which are in good agreement with observed ground water condition at field level. Thus, the results derived can be very much useful in planning and management of groundwater resources in a regional scale.  相似文献   

18.
ABSTRACT

Data on land use and land cover (LULC) are a vital input for policy-relevant research, such as modelling of the human population, socioeconomic activities, transportation, environment, and their interactions. In Europe, CORINE Land Cover has been the only data set covering the entire continent consistently, but with rather limited spatial detail. Other data sets have provided much better detail, but either have covered only a fraction of Europe (e.g. Urban Atlas) or have been thematically restricted (e.g. Copernicus High Resolution Layers). In this study, we processed and combined diverse LULC data to create a harmonised, ready-to-use map covering 41 countries. By doing so, we increased the spatial detail (from 25 to one hectare) and the thematic detail (by seven additional LULC classes) compared to the CORINE Land Cover. Importantly, we decomposed the class ‘Industrial and commercial units’ into ‘Production facilities’, ‘Commercial/service facilities’ and ‘Public facilities’ using machine learning to exploit a large database of points of interest. The overall accuracy of this thematic breakdown was 74%, despite the confusion between the production and commercial land uses, often attributable to noisy training data or mixed land uses. Lessons learnt from this exercise are discussed, and further research direction is proposed.  相似文献   

19.
Land use is changing at accelerated rates in Taiwan, and illegal land use change practices (ILP) are regularly observed within conservation areas. For this reason, we map high-potential areas of ILP within the Soil and water conservation zone (SWCZ) as an aid for effective land management and conducted an exploratory analysis of explanatory variables to evaluate their variability within ILP hot spots. We used variables relevant to hot spots to develop a logistic regression model and identified seven statistically significant variables. We re-applied the logistic regression approach to produce spatially explicit predictions of ILP. High probability areas are distributed along the coastal regions, covering 26% of the SWCZ, and their major drivers are related to accessibility and topography. The results from this research provide relevant information on the major drivers of ILP and high-potential areas, which can support officials in monitoring efforts for better planning and governance within the SWCZ.  相似文献   

20.
Land use and land cover (LULC) changes in northern Nayarit, Mexico were estimated using post-classification change detection methods and a Markov chain model. Three thematic maps were generated by classifying Landsat images from 1973, 1900, and 2000, which were then overlaid to generate three change-detection matrices to assess the intensity and direction of changes. Between 25% and 30% of the region displayed LULC changes, attributable to a stochastic behavior that can be modeled with a first-order Markov chain. The steady-state distribution estimates indicate that the LULC patterns in the region have not yet reached equilibrium and predict the expansion of the agricultural boundaries.  相似文献   

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