Examining Multi-Legend Change Detection in Amazon with Pixel and Region Based Methods

Flooding is a natural disaster due to rivers that are no longer able to accommodate excessive rainwater so that water overflows and inundates the surrounding area. During the rainy season, many areas in Indonesia experience flooding, one of which is the Lamongan Regency. In early 2022, seasonal flooding occurred due to runoff from Bengawan Jero which caused many houses, agricultural land and access roads to be submerged in water. To improve disaster mitigation activities, it is necessary to identify flooding areas using remote sensing. The distribution area of flood inundation was identified using change detection and threshold methods. The change detection method is carried out by using ratio images from Sentinel-1 image data. The results of land cover in Lamongan Regency resulted in 9 land cover classes. Where is dominated by agricultural class land cover with an area of 1057.94 km2 with a percentage of the total area of Lamongan Regency is 60.53%. While the smallest land cover area is the mangrove class covering an area of 101.237 km2 with a percentage of the total area of 0.058%. Extraction of the inundation area was carried out with two different threshold values obtained from equations and statistical calculations. The flood inundation area generated on January 31, 2022, for the first threshold value is 54.932 km2 with an overall accuracy of 97% with a kappa coefficient is 0.94. While the flood inundation area with the second threshold value is 90.330 km2 with an overall accuracy of 94% and a kappa coefficient is 0.88.

In mountainous area,many factors such as the high spatial heterogeneity in land cover conditions and the topographic effect on remote sensing data acquirement significantly constraint the change detection by using remote sensing method. It has become a great challenge to develop an effective method to accurately map the land cover changes for complexterrain areas. In the traditional change detection methods,the spectral features are the major information. Due to the lack of the consideration of other information from structure, topography and other parameters, many false changes will be introduced in the final detection results with a big uncertainty. To avoid the false change and overcome the influence from terrain shadow and phenology differences, an object-oriented change detection method by combining multi-scale segmentation and decision tree algorithm for complex terrain condition is proposed in this study. In the new method,firstly,a series of spectrum information( i.e. reflectance,change vector intensity,NDVI),shape information( i. e. area,lengthwidth ratio,shape index) and terrain parameters( i.e. elevation,slope,aspect) are calculated for change detection rules construction. Secondly,the unchanged surfaces which are usually detected as the false changes due to the influence of terrain shadow and phenology difference are extracted as training sample. Finally,the change detection rules are built automatically by C5.0 decision tree algorithm. the proposed method is applied to Landsat-TM images acquired in 1989 and2009 at Panxi region,the detection results indicate:( 1) Over the mountainous area,the method can effectively reduce the influence from topographic effect,and has a big improvement on detection accuracy with the overall accuracy of 93.57% and Kappa coefcient of 0.8706 by validating ground reference samples and stratified random samples.( 2) The C5.0 decision tree algorithm performs well in terms of robustness and adaptability,which serves well in land cover classification with types only labeled as "changed "and "unchanged "and highly heterogeneous training samples. Considering the false changes result from topographic and phenological as "unchanged "training sample,the accuracy of change detection is improved effectively.( 3) Spectral features are the predominant information in analyzing TM images,and the NDVI differences between the reference image and detected image is an effective indicator for change detection of land cover pattern in vegetation coverage region.( 4) The changes of land cover pattern in Panxi region from 1989 to 2009 were closely associated with human activities. The typical driving forces of pattern change include returning farmland to forest policy,water conservancy project,mining,urban sprawl and so on. The changed area amounted to 740.2 km2,accounting for 2.49% of the total detected area.

The representation of a scene at different levels of detail is necessary to achieve real-time rendering. In aerial views, only the part of the scene that is close to the viewing point needs to be displayed with a high level of detail, while more distant parts can be displayed with a low level of detail. However, when a sequence of images is generated and displayed in real-time, the transition between different levels of detail causes noticeable temporal aliasing. In this paper, we propose a method, based on object blending, that visually softens the transition between two levels of Delaunay triangulation. We present an algorithm that establishes, in an off-line process, a correspondence between two given polygonal objects. The correspondence enables on-line blending between two representations of an object, so that one representation (level of detail) progressively evolves into the other.

The purpose of this study was to examine the influence of image properties on the accuracy of remote sensing change detection methods. Spectral class separability, radiometric normalization and image band correlation were evaluated through experiments with simulated data. The experimental results were then evaluated as a tool for predicting the relative accuracy of change detection results obtained from Landsat TM satellite image pairs of three U.S. cities: Las Vegas, Nevada; Phoenix, Arizona; and Atlanta, Georgia. The change detection methods used were post-classification comparison, direct classification, image differencing, principal component analysis, and change vector analysis. Results of the simulated experiments confirmed that the relative accuracy of the change detection methods varied with changes in image properties. For the class separability experiments, post-classification comparison, direct classification, image differencing, and PCA with a large number of the principal components, were found generally to have higher accuracies than CVA and PCA with a small number of the principal components. For classes with very good separability, image differencing is an excellent method; for classes with poor spectral separability, image differencing was found to have the lowest accuracy. The influence of the error in radiometric normalization on the accuracy of change detection techniques varied greatly with different degrees of class separability. This can be seen particularly well with image differencing, which showed the highest sensitivity to large changes in radiometric error and very poor class separability. Image differencing and PCA were also found to be more sensitive to band correlation. The classification of the real change detection data from the Landsat pairs showed complex and varying patterns, depending on whether complete (mapping all unchanged and changed transitions) or partial (grouping all unchanged transitions into a single class) change analysis was conducted. However, image differencing was relatively consistent in producing good results for the real data. Also, PCA produced satisfactory results for all three cities. On the other hand, the CVA was found to have amongst the lowest accuracies of the partial change detection methods, though this was higher than most of the complete change accuracy. The variation of accuracy results obtained from the change detection methods used in this study suggests that the contradictory results found in previous change detection studies is likely at least partially a result of varying image properties.

Land use land cover (LULC) classification is an important research criterion for urban growth modelling. In this study, five different parametric classification algorithms (Maximum Likelihood Classifier, Mahalanobis Distance, Minimum Distance, Spectral Angle Mapper, Spectral Correlation Mapper) are used for the LULC classification of the Barrackpore subdivision of West Bengal state in India. Two different Landsat datasets (Landsat5 and Landsat8) are used for 2005 and 2020. Various algorithms have shown good accuracy for different LULC feature classes. Minimum Distance and Maximum Likelihood Classifier (MLC) has given the highest overall accuracy in 2005 and 2020, respectively. The best classification results are used for the change detection in the LULC classes over fifteen years (2005-2020). The classification result will help to choose a suitable algorithm for a regional level LULC study in further research. The change detection result indicates the need for a good growth pattern in the region.

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

The availability of Landsat data allows improving the monitoring and assessment of large-scale areas with land cover changes in rapid developing regions. Thus, we pretend to show a combined methodology to assess land cover changes (LCCs) in the Hamoun Wetland region (Iran) over a period of 30-year (1987-2016) and to quantify seasonal and decadal landscape and land use variabilities. Using the pixel-based change detection (PBCD) and the post-classification comparison (PCC), four land cover classes were compared among spring, summer, and fall seasons. Our findings showed for the water class a higher correlation between spring and summer (R2 = 0.94) than fall and spring (R2 = 0.58) seasons. Before 2000, ~ 50% of the total area was covered by bare soil and 40% by water. However, after 2000, more than 70% of wetland was transformed into bare soils. The results of the long-term monitoring period showed that fall season was the most representative time to show the inter-annual variability of LCCs monitoring and the least affected by seasonal-scale climatic variations. In the Hamoun Wetland region, land cover was highly controlled by changes in surface water, which in turn responded to both climatic and anthropogenic impacts. We were able to divide the water budget monitoring into three different ecological regimes: (1) a period of high water level, which sustained healthy extensive plant life, and approximately 40% of the total surface water was retained until the end of the hydrological year; (2) a period of drought during high evaporation rates was observed, and a mean wetland surface of about 85% was characterized by bare land; and (3) a recovery period in which water levels were overall rising, but they are not maintained from year to year. After a spring flood, in 2006 and 2013, grassland reached the highest extensions, covering till more than 20% of the region, and the dynamics of the ecosystem were affected by the differences in moisture. The Hamoun wetland region served as an important example and demonstration of the feedbacks between land cover and land uses, particularly as pertaining to water resources available to a rapidly expanding population.

Characterization of shaking intensity distribution and seismic assessment of RC buildings for the Kashmir (Pakistan) earthquake of October 2005

Simplification of feature-based 3D CAD assembly data of ship and offshore equipment using quantitative evaluation metrics

Process mining techniques have been receiving great attention in the literature for their ability to automatically support process (re)design. The output of these techniques is a concrete workflow schema that models all the possible execution scenarios registered in the logs, and that can be profitably used to support further-coming enactments. In this paper, we face process mining in a slightly different perspective. Indeed, we propose an approach to process mining that combines novel discovery strategies with abstraction methods, with the aim of producing hierarchical views of the process that satisfactorily capture its behavior at different level of details. Therefore, at the highest level of detail, the mined model can support the design of concrete workflows; at lower levels of detail, the views can be used in advanced business process platforms to support monitoring and analysis. Our approach consists of several algorithms which have been integrated into a systems architecture whose description is accounted for in the paper as well.KeywordsInput SchemaSchema TaxonomyAbstraction MethodImplied ActivityProcess Mining TechniqueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

ABSTRACT
 Forest Deforestation Analysis is based on land cover, where land cover is divided into two classes of forest land cover and non-forest land cover class. Deforestation is a forested area that turns into a non-forest area. Land cover uses high resolution imagery with manual image interpretation (digit on screen) method. This study aims to determine the extent of deforestation and the rate of deforestation every year so that it is known how much carbon is lost from the impact of deforestation. The method used in this research is quantitative descriptive with carbon calculation which has been determined by the unit of weight for several classes of land cover. The results of research on forest deforestation were 319.92 ha with a deforestation rate of 38.57 ha per year, causing loss of 81.724.7 tons of forest carbon C. Accuracy test of images carried out using confusion matrix (comparison of image interpretation with field conditions) with an accuracy of 92,31%.
 
 Keywords: Manual Image Interpretation, Deforestation Rate, and Accuracy Test.

Systems approaches for agricultural development can be realized at different levels of detail which are associated with different data needs. For soils five levels were defined, ranging from farmer’s knowledge and expert systems to use of complex simulation models. Any problem to be studied should be analyzed thoroughly beforehand and the most appropriate level of study should be established for each discipline. Field monitoring of soil physical conditions, which is crucial for model calibration and validation, becomes increasingly important at higher levels of detail. Monitoring should be increased, paying due attention to soil profile characteristics during installation of equipment. Measurements of physical parameters needed for simulation should preferably be made in situ, using methods that are relatively simple, accurate and low-cost, such as the crust-test infiltrometer. Pedotransfer functions, which relate available soil data to parameters needed for simulation, are a potentially important source of basic soil data and should be further developed. One type of pedotransfer function uses pedogenic soil horizons from soil surveys as ‘carriers’ of data. This approach is illustrated for both a small scale and a large scale soil survey carried out in the Philippines. Field work is needed to better characterize ‘non-ideal’ soil behaviour due to soil heterogeneity, which is quite common and is as yet not covered by existing models which assume soils to be homogeneous. Soil input is not only relevant for obtaining point data but also to assist in obtaining results that are representative for areas of land. Geostatistics can make contributions towards developing efficient sampling schemes which base the number of observations on spatial heterogeneity and not on the scale of the map to be made. Geostatistics was used successfully to interpolate point data to areas of land, including estimates of accuracies, as is illustrated for a case study at the International Rice Research Institute.

Systems approaches for agricultural development can be realized at different levels of detail which are associated with different data needs. For soils five levels were defined, ranging from farmer’s knowledge and expert systems to use of complex simulation models. Any problem to be studied should be analyzed thoroughly beforehand and the most appropriate level of study should be established for each discipline. Field monitoring of soil physical conditions, which is crucial for model calibration and validation, becomes increasingly important at higher levels of detail. Monitoring should be increased, paying due attention to soil profile characteristics during installation of equipment. Measurements of physical parameters needed for simulation should preferably be made in situ, using methods that are relatively simple, accurate and low-cost, such as the crust-test infiltrometer. Pedotransfer functions, which relate available soil data to parameters needed for simulation, are a potentially important source of basic soil data and should be further developed. One type of pedotransfer function uses pedogenic soil horizons from soil surveys as ‘carriers’ of data. This approach is illustrated for both a small scale and a large scale soil survey carried out in the Philippines. Field work is needed to better characterize ‘non-ideal’ soil behaviour due to soil heterogeneity, which is quite common and is as yet not covered by existing models which assume soils to be homogeneous. Soil input is not only relevant for obtaining point data but also to assist in obtaining results that are representative for areas of land. Geostatistics can make contributions towards developing efficient sampling schemes which base the number of observations on spatial heterogeneity and not on the scale of the map to be made. Geostatistics was used successfully to interpolate point data to areas of land, including estimates of accuracies, as is illustrated for a case study at the International Rice Research Institute.

Understanding grassland habitat dynamics in space and time is crucial for evaluating the effectiveness of protection measures and developing sustainable management practices, specifically within the Natura 2000 network and in light of the European Biodiversity Strategy. Land cover maps, derived from remote sensing data, are essential for understanding long-term changes in vegetation cover and land use and assessing the impact of land use changes on grassland ecosystems. In this study, we conducted a 20-year land cover analysis of grassland landscapes in Umbria, Italy, using Random Forest classifications of Landsat data in Google Earth Engine. Our analysis was based on the years 2000, 2010, and 2020. We integrated harmonic modeling, Gray-Level Co-occurrence Matrix (GLCM) textural analysis, statistical image and gradient analysis, and other spectral and Digital Terrain Model (DTM)-derived indices to enhance the classification capabilities. The LandTrendr (LT) algorithm was used in GEE to collect ground control points in no-change areas automatically. We used a method based on Multilayer Perceptron-Artificial Neural Networks (MLP-ANNs) to forecast 2040 land cover. Our land cover classifications and the scenario model validation achieved an overall accuracy of over 90%. However, the classification of shrublands proved challenging due to their mixed composition and unique spatial patterns, resulting in lower accuracies. Feature importance analysis demonstrated the value of the enhanced map composition, and applying the LandTrendr algorithm simplified the diachronic land use and land cover (LULC) classification and change analysis by supporting automatic training data collection. Results support the interpretation of grassland dynamics in Umbria over the past two decades and identify areas affected by encroachment from shrubs, woody plants, or those with reduced green biomass. The forecasting method along with the selection of spatial drivers to predict land cover change, demonstrated high efficiency compared to other studies. A specific analysis was developed to identify areas where conservation measures related to the Natura 2000 network have been more or less effective in preserving grasslands. Overall, the research provides a scientific foundation for a methodology helpful in informing policy decisions and defining spatially explicit management strategies to enhance grassland conservation inside and outside Natura 2000 areas.

The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. The current research focuses on demarcating the spatiotemporal LULC changes, NDVI and change detections maps. These effects directly affect the ecosystem, land resources, cropping pattern and agriculture. LULC assessment and surveillance are essential for long-term planning and sustainable use of natural resources. However, we have developed the soft computing machine learning algorithm for mapping land use and land cover based on the Google earth engine (GEE) platform and change detection mapping done by SAGA GIS software. It is significantly used for ecological safety and planning under various climate variations. To accurately describe the land use and land cover classes with changes are identified in the area. This area exclusively uses the multitemporal Landsat-5 (30 m) and Sentinel-2 (10 m) imageries in LULC mapping. The GEE is a cloud-computing platform with the prevailing classification ability of random forest (RF) models to make five-year interval LULC maps for 2010, 2015 and 2020. To unique multiple RF models established as a classifier in the algorithm created by JavaScript and GEE. SAGA GIS has provided the best platform for detecting changes in land use and land cover classes. NDVI maps are created based on the cloud-based platform. These maps value ranges between −0.68 to −0.15, 0.76 to −0.29 and 0.66 to −0.11 in 2010, 2015 and 2020. Experimental outcomes indicate five classes such as water bodies, built up, barren, cropland and fallow land during 2010, 2015 and 2020. The overall accuracy of User and Producer for 2010, 2015 and 2019 years in between 86.23%, 88.34%, 85.53% and 92.51%, 94.34% and 91.54%, respectively. We have observed that (2010, 2015 − 2020) agriculture and built-up land increased by 1040.76 ha, 1246.32 ha, 1500.93 ha and 34.96 ha, 37.08 ha, 42.58 ha, respectively. Other side degraded land, fallow land, waterbodies areas (953.19 ha, 679.23 ha, 937.24 ha and 1385.73 ha, 1513.53 ha, 991.08 ha and 32.85 ha, 21.33 ha, 25.66 ha) are increased during the year of 2010, 2015 and 2020, respectively. While results have been done by GEE cloud platform and remote sensing data, this developed algorithm easily classified the land use maps from Landsat-5 and Sentinel-2 TM imagery in the machine learning approach. The determined 30-m and 10-m three-year LULC maps are made-up to deliver vital data on the changes, monitoring and understanding of which types of LULC classes and changes have occupied a place in the Rahuri area.