基于多尺度分割和决策树算法的山区遥感影像变化检测方法——以四川攀西地区为例

ABSTRACTIn this study, we evaluated the effects of topographic correction and gap filling of Landsat Enhanced Thematic Mapper Plus (ETM+) images on the accuracy of forest change detection through a trajectory-based approach. Four types of Landsat time series stacks (LTSS) were generated. These stacks resulted from combinations of topographically corrected and uncorrected imagery combined with gap-filled and unfilled stacks. These combinations of stacks were then used as input into a trajectory-based change detection. The results of change detection from trajectory-based analysis using these LTSS were compared in order to assess the effects of both topographic correction and gap-filling procedures on the ability to detect forest disturbances. The results showed that overall accuracies of change detection were improved after gap filling (10.5% and 7.5%), but were only slightly improved after topographic correction (3.6% and 0.6%). Although the gap-filling process introduced some uncertainty that might have caused false change detection, the number of pixels whose detection of disturbance was enhanced after gap filling exceeded those detecting false change. The results also showed that the topographic correction did not contribute much to improve the change detection in this study area. However, topographic correction has a potential to increase the accuracy of change detection in areas of more rugged terrain and steep slopes. This is because a direct relationship between the slope of the topography with topographic correction and an enhanced detection of disturbance in pixels from year to year was observed in this study. For robust change detection, we recommend that a gap-filling process should be included in the trajectory-based analysis procedures such as the one used in this study where a single image per year is used to characterize change. We also recommend that in areas of rugged terrain, a topographic correction in the image pre-processing should be implemented.

Deli Serdang is one of the regencies in North Sumatra Province, experiencing relatively rapid development and population. Increasing in demand for the availability of land as living space. Two sub-districts of upstream watershed experienced changes in land cover, namely; Sinembah Tanjung Muda (STM) Hilir and STM Hulu. Monitoring changes in land cover in both sub-districts is essential, given that they are located in the upstream area of the watershed and will impact other areas in the lower watershed. This study aims to analyse land cover changes in both sub-districts over ten years (2009 - 2019). The method used in calculating land changes that occur is change detection. Field surveys were carried out to ensure that the land cover conditions on the land cover maps followed the field’s actual conditions. The research shows the period of 2009 – 2019, land cover that has increased in the area are mining, industry, open land, settlements, livestock and shrubs. The decrease in the area occurred in land cover, including dryland forest, mixed gardens and cultivated land.

A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery

The study of land use/land cover (LULC) changes plays an important guiding role in regional ecological protection and sustainable development policy formulation. Especially, the simulation study of the future scenarios may provide a hypothetical prospect which could help to determine the rationality of current and future development policies. In order to support the ecological protection and high-quality development strategy of the Yellow River Basin proposed by the Chinese government, the Great Yellow River Region (GYRR) is taken as the research area. The multi-period land cover data are used to carry out the analysis of land cover changes. The MOLUSCE (Modules for Land Use Change Simulations) plugin of QGIS software is used to carry out a land cover simulation and prediction study for 2030 on a large regional scale. Finally, the land cover status in the mountainous areas of the GYRR is analyzed thoroughly. The results show a decrease in agricultural land and increase in forest land during the past 25 years from 1995 to 2020, and that this trend would continue to 2030. The landscape pattern index analysis indicates that the land cover in the GYRR has become more and more abundant, and the degree of fragmentation has become higher and higher, while landscape patches were more evenly distributed in the GYRR until 2020. On the other hand, the landscape pattern would tend to achieve a certain degree of stability in 2030. The decrease in farmland and the increase in forest land illustrate the efforts made by the GYRR residents and governments in improving the ecological environment under the policy of returning farmland to forests and grasslands. On the other hand, although the residential areas in the mountainous areas are far away from the mountain hazard historical points because of consideration during construction with the help of the development of disaster prevention and mitigation over the years, there could be problem of rapid and haphazard urbanization. It is worth mentioning here that the harmonious and sustainable development of people and land in the GYRR mountainous areas still requires a large amount of effort.

Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and no-changes have a low contrast. This problem has prevented traditional change detection methods from achieving a higher accuracy using bi-temporal images or NDVI time series. In this paper, a multiple shape parameters-based change detection method is proposed by combining the spectral correlation operator and the shape features of NDVI temporal curves (phase angle cumulant, baseline cumulant, relative cumulation rate, and zero-crossing rate). The change magnitude is derived by integrating all the inter-annual differences of these shape parameters. The change regions are discriminated by an automated threshold selection method known as histogram concavity analysis. The results showed that the mean differences in the change magnitudes of the proposed method between 2100 changed and 2523 unchanged pixels was 32%, the overall accuracy was approximately 88%, and the kappa coefficient was 0.76. A comparative analysis was conducted with bi-temporal image-based methods and NDVI time series-based methods, and we demonstrate that the proposed method is more effective and robust than traditional methods in achieving high-contrast change magnitudes and accuracy.

Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.

Street-side vehicle detection, classification and change detection using mobile laser scanning data

Remote Sensing (RS) images capture spatial-temporal data on the Earth's surface that is valuable for understanding geographical changes over time. Change detection (CD) is applied in monitoring land use patterns, urban development, evaluating disaster impacts among other applications. Traditional CD methods often face challenges in distinguishing between changes and irrelevant variations in data, arising from comparison of pixel values, without considering their context. Deep feature based methods have shown promise due to their content extraction capabilities. However, feature extraction alone might not be enough for accurate CD. This study proposes incorporating spatial-temporal dependencies to create contextual understanding by modelling relationships between images in space and time dimensions. The proposed model processes dual time points using parallel encoders, extracting highly representative deep features independently. The encodings from the dual time points are then concatenated and passed through Long Short Term Memory (LTSM) layers and a decoder. The output from the LSTM is then concatenated with that from the decoder in a space-time feature fusion. This optimizes information representation of spectral, spatial and temporal details, in RS images before further analysis of change. This approach aims to maximize authentic information while reducing noise interference by introducing the context of change. Compared to conventional CD methods, the proposed technique achieves higher overall accuracy of 97.4%, an F1 Score of 89% and an intersection over union (IoU) of 86.7 when evaluated on the EGY-BCD dataset. The results demonstrate the potential of incorporating spatial-temporal dependencies in CD tasks for RS images.

Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work aims to evaluate post-classification comparison change detection results obtained from LANDSAT5/TM data in a region of the Brazilian Amazon, using three legends in different levels of detail and both pixel wise and region based classifiers. A distinctive characteristic of the used approach is that each change mapping is the result of the combination of 100 land cover classifications for each date, obtained using varied training samples. This approach allowed to account for the training samples choice into the methodology, as well as the construction of confidence mappings. We presented and discussed different approaches for evaluating change results, such as the likelihood of land cover transitions occurring within the study area and time gap, the use of rectangular matrices to incorporate the occurrence of impossible or non evaluable changes and classification uncertainty. In general, change mappings obtained from region based classifications showed better results than the ones obtained from pixel based classifications. Globally, the use of region based approaches, in contrast to pixel based ones, led to an increase in accuracy of 15.5% for the change mapping from the most detailed legend, 7.8% for the one with the legend with intermediate level of detail and 3.6% for the less detailed one. In addition, individual transitions between land cover classes were better identified using region based approaches, with the exception of transitions from a non agriculture class to an agricultural one. The proposed quality mappings are useful to help to evaluate the change mappings, mainly in legend levels with higher level of detail and if reference samples are unreliable or unavailable. It was possible to access, in a spatially explicit way, that at least 29.0% of the pixel based change mapping and 21.9% of the region based one from the most detailed legend were erroneous classified, without ground truth information on the evaluated date. These values decreased to 0.5% and 1.4% (respectively the pixel and region based approaches) for results with the legend with the intermediate level of detail and are non existent in the results from the less detailed legend. The more generalized the legend (lower number of classes), the most similar are the accuracy of region and pixel based change mappings. These accuracy values also increase as fewer classes are considered in the legend, since similar classes are assembled during clustering, which reduces the overlap between groups. However, this accuracy is still low for operational purposes in areas with few changes, even considering the very high accuracy of the land cover classifications used to generate the change mappings (land cover classification with Overall Accuracy higher than 0.98 resulted in change mappings with Overall Accuracy around 0.83).

Land cover change detection plays an important role in natural disaster monitoring, tracking urban expansion, and many social benefit areas. The spectral-based direct comparison (SDC) methods are commonly used for change detection, but such methods are vulnerable to the influence of external factors. In general, land changes among different land cover types have different characters of change magnitude. The class probability-based direct comparison (CPDC) methods consider land type information and reduce the influence of external factors, but these methods are strongly dependent on the training samples. To address the above problems, we proposed a novel change detection method that integrates spectral values and class probabilities (SVCP). First, a new change magnitude map based on spectral values and class probabilities is constructed by using the maximum interclass variance and Gaussian mixture model (GMM), which greatly enhances the differentiation between changed and unchanged areas. Second, the Kapur threshold selection method is improved by using the variance of the changed and unchanged areas as well as the class probabilities for adaptive thresholding. The SVCP approach was assessed by two case studies from Landsat 8 Operational Land Imager (OLI) images. The “change/no-change” detection and “from-to” change types were evaluated. The experimental results indicated that the SVCP method is more accurate in the change detection, with lower false and missed detection rates than the traditional methods.

Abstract. Oil palms have large economic value and are grown extensively across Southeast Asia. However, growth of the oil palm industry comes at the expense of the environment as forests are cleared to grow oil palms. Oil palm plantations need to be monitored to balance economic growth and environmental sustainability. Synthetic Aperture Radar (SAR) imagery allows for the cost-effective and frequent mapping of the extent of oil palm plantations over large areas. This paper aims to develop an oil palm plantation mapping model using X, C and L band SAR and compare their relative performance. The models are developed with the Feature Pyramid Network based on annotations acquired over Batu Pahat, Malaysia. X-band has the best Dice Score of 0.9 for oil palm plantations and the highest overall accuracy of 81.78%. Repeat pass satellite images captured 6 months later were then inferred with the 3 models to identify changes to the land cover. X-band also has the best accuracy in change detection as it has the best land cover classification performance overall. The plantation maps add semantic meaning to the land cover changes. This paper successfully developed a model that can generate frequently updated and detailed oil palm plantation maps, which can be used to detect changes in the oil palm plantation extent promptly.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

The Curve Number method is one of the most widely used methods for estimating runoff potential from rainfall events in watershed. In this study, the Curve Number parameter was estimated, spatialized, analyzed and discussed under different land cover and land use conditions. The study area is the Riacho da Consulta watershed, located in the state of Bahia,Semiarid region of Brazil. Land use and land cover data, slope data and spatial data about pedology of the study area were used. The results showed that the Riacho da Consulta watershed has an average Curve Number of 79, evidencing a watershed with high runoff potential, where areas of very high Curve Number are strongly related to land use and land cover, emphasizing the importance of conservation practices.

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences. The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas. The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods (i.e. change vector analysis and principal component analysis-based method). In particular, the proposed sub-pixel change detection approach not only provides the binary change information, but also obtains the characterization about change direction and intensity, which greatly extends the semantic meaning of the detected change targets.