Land Cover Identification Research Articles

Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral image classification

Hyperspectral image (HSI) classification plays a crucial role in remote sensing (RS) applications, enabling the precise identification of materials and land cover based on spectral information. This supports tasks such as agricultural management and urban planning. While sequential neural models like Recurrent Neural Networks (RNNs) and Transformers have been adapted for this task, they present limitations: RNNs struggle with feature aggregation and are sensitive to noise from interfering pixels, whereas Transformers require extensive computational resources and tend to underperform when HSI datasets contain limited or unbalanced training samples. To address these challenges, Mamba architectures have emerged, offering a balance between RNNs and Transformers by leveraging lightweight, parallel scanning capabilities. Although models like Vision Mamba (ViM) and Visual Mamba (VMamba) have demonstrated improvements in visual tasks, their application to HSI classification remains underexplored, particularly in handling land-cover semantic tokens and multi-scale feature aggregation for patch-wise classifiers. In response, this study introduces the Mamba-in-Mamba (MiM) architecture for HSI classification, marking a pioneering effort in this domain. The MiM model features: (1) a novel centralized Mamba-Cross-Scan (MCS) mechanism for efficient image-to-sequence data transformation; (2) a Tokenized Mamba (T-Mamba) encoder that incorporates a Gaussian Decay Mask (GDM), Semantic Token Learner (STL), and Semantic Token Fuser (STF) for enhanced feature generation; and (3) a Weighted MCS Fusion (WMF) module with a Multi-Scale Loss Design for improved training efficiency. Experimental results on four public HSI datasets—Indian Pines, Pavia University, Houston2013, and WHU-Hi-Honghu—demonstrate that our method achieves an overall accuracy improvement of up to 3.3%, 2.7%, 1.5%, and 2.3% over state-of-the-art approaches (i.e., SSFTT, MAEST, etc.) under both fixed and disjoint training-testing settings.

Characterization and evaluation of environmental units as a management and conservation strategy of the Palmira Desert (Ecuador)

Globally, deserts are considered as fragile and unique biomes. They have the capacity to provide various ecosystem services, and possess great tourism potential and scientific value, but are also susceptible to modifications or alterations in their geomorphology. Despite the strategic importance of these ecosystems at a global level, there is limited specialized research directed towards the study of deserts in Ecuador. The purpose of this research is to determine the susceptibility to degradation and carrying capacity of environmental units to support tourist and recreational activities, thereby offering the community a sustainable resource over time. The delimitation of environmental units was carried out based on the identification of land use and land cover through the supervised classification method. Multicriteria analysis was used to obtain the areas susceptible to degradation, where the environmental units were evaluated through expert evaluation. Once the results of the previous methodologies were obtained, areas with environmental and tourist potential were identified using the hosting capacity matrix, which is based on anthropic activities developed in the area. Subsequently, a proposal for conservation strategies and activities at the national and local levels was formulated. Five categories of land use and land cover were identified, resulting in 11 environmental units represented in spatial data. It was determined that approximately 31% of the territory is more susceptible to degradation, while the remaining 69% is less susceptible. Based on the susceptibility of these areas, 7 activities and 2 tourist routes were designated to help reduce anthropogenic pressure. Additionally, the community was provided with 5 proposals for conservation strategies at the national level, 4 at the local level, and 4 activities to be developed based on the findings identified during the investigation. This highlights the significant potential that the Palmira Desert has to become a conservation area.

Grouping Land Cover Using Orthophoto in Small Islands: An Application of Low-Cost UAV on Mansinam Island

Land cover is crucial for island management, but the lack of accessible and high-resolution remote sensing data has reduced investigations on small islands, including land cover identification. Therefore, this study aimed to investigate land cover using Unmanned Aerial Vehicle (UAV) technology, providing very high-resolution images. Classification and delineation were conducted using automatic segmentation followed by manual reinterpretation and visual verification. The results showed 14 cover classes, consisting of 8 vegetated and six non-vegetated categories. Forest cover on Mansinam island accounted for 75.5% or 302.4 ha, which was evenly distributed. Furthermore, primary forest covered 31.91% or 127.74 ha, and secondary covered 43.63% or 174.68 ha. The classification achieved an overall accuracy of 96% and a kappa coefficient of 0.94. Low-cost UAVs effectively produced high-resolution aerial images of small islands for land cover identification. Therefore, future studies were recommended to consider whether segmentation can reliably distinguish between primary and secondary forests, as well as assess the impact of flight altitude on segmentation accuracy using ground control points. The results were also expected to support spatial planning or sustainable forest and environment management on Mansinam Island.

Assessment of the Accuracy of Various Machine Learning Algorithms for Classifying Urban Areas through Google Earth Engine: A Case Study of Kabul City, Afghanistan

Accurate identification of urban land use and land cover (LULC) is important for successful urban planning and management. Although previous studies have explored the capabilities of machine learning (ML) algorithms for mapping urban LULC, identifying the best algorithm for extracting specific LULC classes in different time periods and locations remains a challenge. In this research, three machine learning algorithms were employed on a cloud-based system to categorize urban land use of Kabul city through satellite images from Landsat-8 and Sentinel-2 taken in 2023. The most advanced method of generating accurate and informative LULC maps from various satellite data and presenting accurate outcomes is the machine learning algorithm in Google Earth Engine (GEE). The objective of the research was to assess the precision and efficiency of various machine learning techniques, such as random forest (RF), support vector machine (SVM), and classification and regression tree (CART), in producing dependable LULC maps for urban regions by analyzing optical satellite images of sentinel and Landsat taken in 2023. The urban area was divided into five classes: built-up area, vegetation, bare-land, soil, and water bodies. The accuracy and validation of all three algorithms were evaluated. The RF classifier showed the highest overall accuracy of 93.99% and 94.42% for Landsat-8 and Sentinel-2, respectively, while SVM and CART had lower overall accuracies of 87.02%, 81.12%, and 91.52%, 87.77%, with Landsat-8 and Sentinel-2, respectively. The results of the present study revealed that in this classification and comparison, RF performed better than SVM and CART for classifying urban territory for Landsat-8 and Sentinel-2 using GEE. Furthermore, the study highlights the importance of comparing the performance of different algorithms before selecting one and suggests that using multiple methods simultaneously can lead to the most precise map.

The reptile optimized deep learning model for land cover classification of the uppal earth region in telangana state using satellite image fusion

This study addresses challenges in land use and cover identification using remote sensing (RS) imagery, focusing on the Uppal region. By leveraging deep learning models, particularly an optimized ResNext-50 architecture, we aim to enhance efficiency and accuracy in classifying land features. Our approach integrates Landsat-8 and hyper-spectral satellite data, utilizing preprocessing techniques like dark subtraction, stacking, merging, and spectral enhancement. Principal Component Analysis (PCA) is applied to streamline high-dimensional feature sets obtained from pre-processed spectral data. We further employ hybrid NSCT-FDCT fusion for integrating Landsat-8 and hyperspectral images. The resulting fused image is fed into our classification process, utilizing the modified ResNext50 (Deep Learning Architecture) model with Reptile Search Optimization for weight link optimization. Notably, our proposed method achieves impressive outcomes: 97% accuracy, 96% sensitivity, 99% specificity, 3% error, 97% precision, and a 95% Matthew Correlation Coefficient. This demonstrates the efficacy of our approach in predicting diverse land covers within the Uppal region, showcasing the potential of Landsat-8 and Hyper-spectral data for accurate land use and cover identification.

ENDAU ROMPIN NATIONAL PARK LAND USE LAND COVER CHANGES USING REMOTE SENSING APPROACH

Identification of land use and land cover in forest areas can be challenging due to various land cover types within a forest can be similar, making it hard to differentiate between them using remote sensing techniques. We hypothesized that random forest classification (RF) would outperform maximum likelihood (ML) in the classification of land use and land cover (LULC) in forest areas compared to maximum likelihood (ML). To verify this hypothesis, we conducted a comparative analysis, assessing the accuracy of RF and ML in the classification of LULC within the Taman Negara Endau-Rompin (TNER) region, utilizing Landsat 8 imagery. An accuracy assessment demonstrated that the RF classifier (overall accuracy: 87% kappa coefficient: 0.778, performed better than ML classifying land cover (overall accuracy 77% with kappa accuracy: 0.473) Our results suggest that both methods are able to classify land cover of forest area, but RF is more accurate than ML. From the classification result of RF classification, we calculate the land cover changes of TNER from 2013 to 2022. results showed that there are small changes of forest area were found in TNER. The total forest area decreases from 163250.089 ha to 144765.46 ha during 2013 to 2022. This finding suggests that the effectiveness of the protected area in mitigating deforestation in its surrounding regions may be somewhat limited, as indicated by the observed minor changes.

Evaluation of optical multi-spectral satellite data for crop type and land cover identification in Marathwada, India: a disaster management perspective

Evaluation of optical multi-spectral satellite data for crop type and land cover identification in Marathwada, India: a disaster management perspective

Using a Novel Green Index to Support Ecosystem Services in a Megacity

We present a novel and efficient approach that enables the evaluation of environmental quality in cities worldwide using high-resolution satellite imagery, based on a new green index (GI) through multivariate analysis, to compare the proportion of urban green spaces (UGSs) with built and impervious surfaces. High-resolution images were used to perform a supervised classification of 25 districts in the city of São Paulo, Brazil. Only 11 districts showed higher urban forests, green spaces, green index, and green vs. built values, and impervious surface proportions with lower impervious and built spaces. On the other hand, the remaining districts had higher population densities and unfavorable conditions for urban ecosystem development. In some cases, urban green spaces were three-times smaller than the built and impervious surfaces, and none of the districts attained a high green quality index (0.75 to 1). Artificial intelligence techniques improved the precise identification of land cover, particularly vegetation, such as trees, shrubs, and grasses. The development of a novel green index, using multivariate statistical analysis, enhanced positive interactions among soil cover classes, emphasizing priority areas for enhancing environmental quality. Most of them should be prioritized by decision makers due to the low environmental quality, as identified by the low green index and worse ecosystem services, well-being, and health outcomes. The method can be employed in many other cities to enhance urban ecosystem quality, well-being, and health. The green index and supervised classification can characterize pastures, degraded forest fragments, and guide forest restoration techniques in diverse landscapes.

Local Climate Zone Classification Using Daytime Zhuhai-1 Hyperspectral Imagery and Nighttime Light Data

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city’s physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification’s highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification.

Modeling LULC using Multi-Layer Perceptron Markov change (MLP-MC) and identifying local drivers of LULC in hilly district of Manipur, India

Identification and prediction of future land use and land cover (LULC) changes and their drivers are required for land resources management, formulation of better policy, practicing sustainable management strategies, and modeling future LULC. The present study has focused on the prediction of future LULC and assessment of local drivers of LULC change in the hill agroecosystem Senapati district of Manipur, Northeast India. The prediction of future LULC for 2029 was achieved based on LULC data of 1999, 2009, and 2019 satellite data using multi-layer perceptron Markov change (MLP-MC) of Land Change Modeler (LCM). The socio-economic survey and field observation were performed to identify local drivers of LULC changes in the study area. The total area covered by open and dense forests in 2029 was 69.06% of the total geographical area of the district, a slight increase from the 2019 assessment of 61.01 km2. The drivers of LULC were categorized by assigning rank. The proximate drivers directly influence land use dynamics, such as increases in the settlement area, firewood collection, and construction. The underlying drivers-population growth and poverty-have also indirectly influenced LULC change in the district. It was observed that the livelihoods of local communities depend on forest products and agriculture. Therefore, the study finding will help create a future LULC information database and identification of local drivers of LULC. It will encourage the communities to participate in proper land resource management and create awareness for the sustainable use of the land.

Performance of Deep Learning in Land Use Land Cover Classification of Indian Remote Sensing (IRS) LISS – III Multispectral Data

Identification of land use land cover is a very important task. However, methods existing for the above mention purpose are labor incentives, time-consuming, and costly. Remote sensing plays very important role in the mappings. classification of land cover features and offers very noteworthy and sensed information. The present study shows the semantic segmentation of Indian remote sensing (IRS) LISS-III multispectral image and the comparison of three algorithms U-Net, Deeplabv3+and Tiramisu. The deep neural network was used to perform the study. We present total 3 innovative datasets, built on these LISS-III images that has 4 different spectral bands (Band – 2 (Blue), Band-3 (Green), Band-4(Red), and Band-5 (Nearly Infrared), FCC (false color composite) images and the ground truth mask images. Dataset has 13500 labelled images. A fully-convolutional network (FCN) with skip connections is trained to take an input image of size 128 X 128 X 3 and outputs a matrix of shape 128 X 128 X 4 i.e., a one-hot encoded version of the mask. The experiment identifies 4 classes successfully (Water Bodies, Vegetation, Uncultivated Land, and Residential areas). The experiment showed that the U-Net algorithm has a very good capability for the classification of LISS -III images for land use land cover class detection then Tiramisu and Deeplabv3+. U-Net achieved accuracy 84%, Deelabv3+ achieved 29% whereas Tiramisu achieved accuracy 33%.

IDENTIFICATION OF LAND COVER OF SURFACE TEMPERATURE IN KOTA PARIAMAN USING LANDSAT IMAGERY 8-OLI

Remote sensing is a technology that can overcome data measurement problems for fast and accurate information. This research was carried out in Pariaman City using Landsat 8-OLI imagery with the aim of 1) knowing the identification of land cover based on the classification SNI:7645 (2014) based on Landsat 8-OLI imagery in the Pariaman City area in 2020 2) knowing the identification of surface temperature in the Pariaman City area in 2020. The research was conducted in serval stages, namely pre-processing of image data, selecting band combinations, cutting area studies, image classification, and testing accuracy. At surface temperature using Landsat 8-OLI imagery in 2020, surface temperature values ​​are obtained from the results of thermal band processing. The results of the classification of Landsat 8-OLI images in Pariaman City produce 5 land cover classes, namely water bodies, rice fields, settlements, mixed gardens, and shrubs. This research conducted a classification accuracy test using a confusion matrix accuracy test table. Land cover supervised maximum likelihood method the overall accuracy value is 86.66 %. The results of the surface temperature value in Pariaman City in 2020 obtained the highest temperature value of 30ºC and the lowest surface temperature of 23ºC.

Monitoring the Land Use, Land Cover Changes of Roorkee Region (Uttarakhand, India) Using Machine Learning Techniques

Satellite images play an important role for capturing Earth's surface. Using satellite images land cover monitoring could be done through which the modification or changes on land surface could be identified. Comparison can be made on the basis of past satellite image analysis, which helps to identify the changes that are occurring or have already occurred. Although there exist many techniques for land cover monitoring, proper land cover identification and detection of changes on the land cover is still a challenge. In the recent years, machine learning techniques have been utilized in distinct areas of image analysis and resulted in positive outcomes. Hence, in this paper, four supervised machine learning algorithms (i.e., support vector machine [SVM]), neural network [NN], maximum likelihood [MLC], and parallelepiped [PP] algorithms) have been utilized for land cover identification and detecting the amount of changes that have occurred in the individual land cover classes.

Joint Classification of Hyperspectral and LiDAR Data Using a Hierarchical CNN and Transformer

The joint use of multisource remote-sensing (RS) data for Earth observation missions has drawn much attention. Although the fusion of several data sources can improve the accuracy of land-cover identification, many technical obstacles, such as disparate data structures, irrelevant physical characteristics, and a lack of training data, exist. In this article, a novel dual-branch method, consisting of a hierarchical convolutional neural network (CNN) and a transformer network, is proposed for fusing multisource heterogeneous information and improving joint classification performance. First, by combining the CNN with a transformer, the proposed dual-branch network can significantly capture and learn spectral–spatial features from hyperspectral image (HSI) data and elevation features from light detection and ranging (LiDAR) data. Then, to fuse these two sets of data features, a cross-token attention (CTA) fusion encoder is designed in a specialty. The well-designed deep hierarchical architecture takes full advantage of the powerful spatial context information extraction ability of the CNN and the strong long-range dependency modeling ability of the transformer network based on the self-attention (SA) mechanism. Four standard datasets are used in experiments to verify the effectiveness of the approach. The experimental results reveal that the proposed framework can perform noticeably better than state-of-the-art methods. The source code of the proposed method will be available publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zgr6010/Fusion_HCT.git</uri> .

ANALYSIS THE ENVIRONMENTAL SUPPORT CAPACITY BASED ECOSYSTEM SERVICES TO BASIC DIRECTIONS FOR ADJUSTING THE SPACE PATTERN IN WACO REGENCY

Wajo Regency ecosystem has an abundance of natural and ecological resources with great potential. However, the rapid pace of development activities followed by rapid land conversion has put the sustainability of the supporting ecosystems under pressure. Therefore, it is necessary to map the carrying capacity of the environment based on ecosystem services in Wajo Regency in order to identify the fit between environmental carrying capacity and development in Wajo Regency. Research is at Wajo Regency, through the identification of land cover. ecosystem service-based land capability based analysis is conducted using an Analysis Hierarchy Process to determine the value of ecosystem services from four classes of environmental services, namely water supply, food supply, natural disaster prevention and runoff, and flood management. Instructions for completing spatial patterns in Wajo Regency are performed by analyzing the overlap of the ecosystem service-based bearing capacity map with the Wajo Regency spatial pattern map to obtain a class of spatial pattern alignment. The orientation class is based on the carrying capacity of the land capability based based on ecosystem services to optimize land cover in Wajo Regency. Results of this study show that the land capability based of the four examined ecosystem services in Wajo Regency is dominated by high and low classes. The results of the evaluation of the RTRW by Wajo Regency show that an area of 42,048.97 ha corresponds to the carrying capacity based on ecosystem services. An area of 199,454.11 hectares of land is less oriented, but most of it is retained based on the direction of the spatial pattern. In terms of environmental sustainability based on ecosystem services, the most important land accesses are in the designated settlement areas, other protected areas and local protected areas.

Identification of land use and land cover using the image Landsat 8 in upstream Lematang sub-watershed by support vector machine and random trees methods

Accuracy of data and information on various types of land use and land cover (LULC) is crucial in watershed management planning. The development of remote sensing technology in predicting LULC has been widely used, such as the Support Vector Machine (SVM) and Random Trees (RT) methods. Both methods are part of the model of machine learning and belong to the category of supervised learning. This method can predict, study, and recognize data patterns from the results of training data. The aim of this research is the use of SVM and RT methods for the identification of LULC. Based on the SVM method analysis results, the forest area is 211.61 km2, coffee plantations are 32.92 km2, shrubs are 16.85 km2, built-up/bare land is 3.40 km2, the water body is 3.17 km2, and cloud/shadow is 6.79 km2. While the RT method obtained a forest area of 215.51 km2, coffee plantations covering an area of 29.32 km2, shrubs are covering an area of 16.32 km2, built-up/bare land surrounding an area of 7.22 km2, water body covering an area of 2.62 km2, and cloud/shadow covering an area of 3.75 km2. Overall, the percentage accuracy of the SVM method of 87%, and the RT of 86%. The success rate of modeling SVM (AUC = 0.73) and RT (AUC = 0.75) based on the results of data validation using the area under the receiver operating characteristic curve (AUC) was considered fair good.

Land Use and Land Cover Change Detection in the Bhavani Basin of India through Remote Sensing Approach

Using remote sensing data to detect changes in land use and land cover (LULC) is a valuable source of information for various decision support systems. Land use and land cover identification data was used to analyse land conservation, sustainable development, and water resource management. This research aims to determine how the Bhavani basin land use and land cover have changed over the period of time. Land cover changes were detected using Landsat Thematic Mapper (TM) 30 m resolution images in the GIS environment and with image processing techniques for the four years 1999, 2007, 2014, and 2020. The differences in the landuse and land cover classes are described using ERDAS imagine version 2015 and ARC GIS software. The four land cover classes viz. water body, built-up land, barren land, and vegetation were used to classify the region. The accuracy evaluation was assessed separately using the kappa coefficient after carefully examining the image pre-processing and classification. The overall accuracy in the basin was found to be 83.23%, 86.45%, 85.83%, and 88.75 % with a kappa coefficient of 0.79, 0.81, 0.87, 0.85 for the years 1999, 2007, 2014, and 2020 respectively. The Bhavani basin is mostly covered by barren and vegetation. According to the findings, the basin's built-up area has risen by 1.5 percentage to 3.5 percentage in the last 20 years. The increase in the vegetation area and reduction in the barren area may lead to low soil erosion.

Investigating anthropogenically transformed landscapes with remote sensing

The assessment of the current state and spatio-temporal changes in natural landscapes is crucial, among others, for the agro-industrial and agricultural sectors of every country, including Ukraine. The main objective of this paper is to estimate the state and spatio-temporal changes in landscapes with land-cover identification using remote sensing data processing and geographic information technologies. The generalized scheme of the present study consists in the analysis of natural and anthropogenic changes of Ukraine's capital city of Kiev landscapes using satellite data. The processing involved step-by-step decipherment of Kiev's landscapes using cluster analysis, namely supervised and unsupervised classification based on a series of satellite images from Landsat missions 5, 7, 8 (1985–2020) and Sentinel-2 MSI (2015–2020). Spectral signatures were determined, and their quality was estimated based on the classification. The Normalized Difference Vegetation Index (NDVI) was calculated for vegetated landscapes. The following landscapes were identified: water bodies, vegetation, urban areas, and bare soil. Two land cover maps were compiled from Sentinel-2 MSI images for 2015 and 2020 with an overall accuracy of 89.76% and 82.05%, respectively. Two maps of anthropogenic transformations of Kiev's landscapes were also compiled. According to Landsat data (spatial resolution 30 m), the area of urban landscapes increased by about 41% from 1985 to 2020, and according to Sentinel-2 MSI data (spatial resolution 10 m), it grew around 5% from 2015 to 2020.

Planning tsunami vertical evacuation routes using high-resolution UAV digital elevation model: case study in Drini Coastal Area, Java, Indonesia

Tsunami evacuation is a short process that must be carried out in minutes. Determining the evacuation routes and safe areas is equally important in tsunami evacuation planning. It can be established from least cost distance (LCD) anisotropic model. We use the coastal of Drini, Gunungkidul, Java, Indonesia, as the study area, which is located in a tropical coastal karst region, with a typical karst cone formation. The topographic condition of the karst cone could be an ideal location for tsunami vertical evacuation (TVE). High-resolution orthomosaic images and digital elevation model (DEM) generated from the structure from motion (SfM) process were used as the main data. The tsunami inundation model was calculated based on neighboring operations using raster calculator from the elevation value in the DEM. Land cover identification from high-scale orthomosaic images showed the variation in speed conservation values (SCVs). The slope value was also used as a surface cost that will affect the travel time to the TVE shelters. The results of the model show that TVE is the main alternative in tsunami evacuation planning beside horizontal evacuation. They provide an evaluation of the location of the assembly points and evacuation routes provided by the government, which have been considered ineffective because of the inappropriate model. The results of the study provide an overview of determining the evacuation routes and shelters. Optimum locations can minimize travel time, provide adequate capacity, and be safe from inundation.

Proposed Methods for Enhancement in Land Use Land Cover (LULC) Identification Using Various Existing and Novel Band Arithmetic Approaches

This study presents the band arithmetic approach to identify land cover types such as vegetation, bare land, water, and built-up area in the area of Nanded district and the Purna region of Parbhani district, India. Rather than boosting intelligence to the classifier end, the principal purpose of the proposed research work is to produce various formulations for the feature enhancement. The classifier could classify the formulae transformed images and enhance the accuracy of classification. These formulations are the estimated arithmetic between the various bands of the multispectral satellite imagery of the Sentinel-2. We carried out estimation using knowledge of the spectral reflectance curve. Different arithmetic formulations among the bands of the same date scene covered by satellite are proposed and applied at the pixel level. We have tested the efficacy of the proposed methods using a random forest (RF) classifier. Proposed methods provide enhanced results in terms of accuracy. The overall accuracy reaches up to 95 percent with a kappa coefficient 0.93 for the Nanded site and 91 percent with a kappa coefficient 0.88 for the Purna site.