Multispectral Scanner Research Articles

Automatic Fine Co-Registration of Datasets from Extremely High Resolution Satellite Multispectral Scanners by Means of Injection of Residues of Multivariate Regression

Automatic Fine Co-Registration of Datasets from Extremely High Resolution Satellite Multispectral Scanners by Means of Injection of Residues of Multivariate Regression

Precision Phenotyping of Wild Rocket (Diplotaxis tenuifolia) to Determine Morpho-Physiological Responses under Increasing Drought Stress Levels Using the PlantEye Multispectral 3D System

The PlantEye multispectral scanner is an optoelectrical sensor automatically applied to a mechatronic platform that allows the non-destructive, accurate, and high-throughput detection of morphological and physiological plant parameters. In this study, we describe how the advanced phenotyping platform precisely assesses changes in plant architecture and growth parameters of wild rocket salad (Diplotaxis tenuifolia L. [DC.]) under drought stress conditions. Four different irrigation supply levels from moderate to severe, required to keep 100, 70, 50, and 30% of the water-holding capacity, were adopted. Growth rate and plant architecture were recorded through the digital measure of biomass, leaf area, Canopy Light Penetration Depth, five convex hull traits, plant height, Surface Angle Average, and Voxel Volume Total. Vegetation color assessments included hue, lightness, and saturation. Vegetation and senescence indices were calculated from canopy reflectance in the red (620–645 nm), green (530–540 nm), blue (peak wavelength 460–485 nm), near-infrared (820–850 nm), and 3D laser (940 nm) ranges. The temperature, relative humidity, and solar radiation of the environment were also recorded. Overall, morphological parameters, color, multispectral data, and vegetation indices provided over 7200 data points through daily scans over three weeks of cultivation. Although a general decrease in growth parameters with increasing stress severity was observed, plants were able to maintain the same morpho-physiological performances as the control during the early growth stages, keeping both 70% and 50% of the total water-holding capacity. Among indices, the Normalized Differential Vegetation Index (NDVI) contributed the most to the differentiation between different stress levels during the cultivation cycle. Across the 3 weeks of growth, statistically significant differences were observed for all traits except for the Saturation Average. Comparisons with respect to the control highlighted the strong impact of drought stress on morphological plant traits. This study provided meaningful insights into the health status of wild rocket salad under increasing drought stress.

Analysis of methods for assessing the degree of water pollution

The most important modern methods for the monitoring of water bodies, their state of pollution and the assessment of water quality are considered. Commonly accepted, standard-ized methods for assessing water quality in water bodies include ground-based sampling, which is untimely and expensive under certain conditions. Best suited for practical use in large areas is the remote approach, which combines ground and satellite measurements to classify changes and detect pollution in closed water bodies. Satellite monitoring is a reliable tool for obtaining operational information on the state of water bodies and their changes with a certain periodicity, thanks to the use of a large set of heterogeneous data. And the availabil-ity of archived data becomes a significant advantage, opening up the possibility of comparing the results obtained and analyzing the factors influencing the state of water bodies and the environment. The paper describes the main methods and datasets that can be used for the ap-plication of remote monitoring methods based on machine learning. The use of Landsat mul-tispectral sensors, including TM (Thematic Mapper), MSS (Multispectral Scanner), ETM (Enhanced Thematic Mapper), and OLI (Operational Land Imager), is proposed as the main data set. Examples of the application of the remote sensing approach to the assessment of wa-ter pollution over large areas will be analyzed. Possible machine learning methods for solv-ing an applied problem are presented.

Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

The definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasi-continuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m2, where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is, m:n fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion.

Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks

Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small grain cereals worldwide. Developing FHB-resistant cultivars is critical but requires field and greenhouse disease assessment which are typically laborious and time-consuming. In this work, we developed automated applications based on 3-dimensional (3D) convolutional neural networks (CNN) that detect FHB symptoms expressed in wheat, estimate the total number of spikelets versus the total number of infected spikelets on a wheat head, and subsequently calculate FHB severity index. Such tools are an important step toward the creation of automated and efficient phenotyping methods. The data used to generate the results are 3D point clouds consisting of four color channels – red, green, blue (RGB), and near-infrared (NIR) – collected using a multispectral 3D scanner. Our 3D CNN models for FHB detection achieved 100% accuracy. The influence of the multispectral information on performance was evaluated; the results showed the dominance of the RGB channels over both the NIR (720 nm peak wavelength) and the NIR plus RGB channels combined. Our best 3D CNN models for estimation of total and infected number of spikelets achieved mean absolute errors (MAE) of 1.13 and 1.56, respectively. Our best 3D CNN models for FHB severity estimation achieved 8.6 MAE. A linear regression analysis between the visual FHB severity assessment and the FHB severity predicted by our 3D CNN showed a significant correlation.

Monitoring root rot in flat-leaf parsley via machine vision by unsupervised multivariate analysis of morphometric and spectral parameters

Use of vertical farms is increasing rapidly as it enables year-round crop production, made possible by fully controlled growing environments situated within supply chains. However, intensive planting and high relative humidity make such systems ideal for the proliferation of fungal pathogens. Thus, despite the use of bio-fungicides and enhanced biosecurity measures, contamination of crops does happen, leading to extensive crop loss, necessitating the use of high-throughput monitoring for early detection of infected plants. In the present study, progression of foliar symptoms caused by Pythium irregulare-induced root rot was monitored for flat-leaf parsley grown in an experimental hydroponic vertical farming setup. Structural and spectral changes in plant canopy were recorded non-invasively at regular intervals using a 3D multispectral scanner. Five morphometric and nine spectral features were selected, and different combinations of these features were subjected to multivariate data analysis via principal component analysis to identify temporal trends for early segregation of healthy and infected samples. Combining morphometric and spectral features enabled a clear distinction between healthy and diseased plants at 4–7 days post inoculation (DPI), whereas use of only morphometric or spectral features allowed this at 7–9 DPI. Minimal datasets combining the six most effective features also resulted in effective grouping of healthy and diseased plants at 4–7 DPI. This suggests that selectively combining morphometric and spectral features can enable accurate early identification of infected plants, thus creating the scope for improving high-throughput crop monitoring in vertical farms.

The Role of Man on Land Cover Dynamics in the Jakiri Area, N.W. Region of Cameroon

The Jakiri Plateau is an epitome of sub-Saharan agglomerations where man has had an untold impact on the land cover dynamics. This research thus stood to decipher and view in detail the role of man in determining landcover change. The study relies on the collection of data from secondary and primary sources. Questionnaires were administered to 310 Farmers and 84 Pastoralists in 17 villages. Information from Focus Group Discussions and Interviews was beefed up with facts based on measurements and Observations from the Field. The Topographic Map of Nkambe NB32XVII, at the scale: 1:200 000, was exploited. Data from satellite images were derived using the Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper plus (ETM +). ASTER images for 2007 were used. Data were treated with the use of SPSS, ArcGIS and ERDAS IMAGINE software. The study reveals that anthropogenic actions are predominant in degradation; meanwhile, physical factors only contribute to the degradation process that rendered the Jakiri plateau susceptible to soil erosion and mass movement. The Normalized Different Vegetation Index (NDVI) revealed a decrease in NDVI values from +0.92 in 1988, +0.74 in 2001 and +0.5 in 2020, showing a vegetation decrease. Meanwhile, the Land Cover Classification revealed a change in forest/land cover, from 24.4% in 1988 to 9.83% in 2020, with a decrease in forest cover of 14.5%. On the contrary, built-up areas, grassland (savannah) and bare surfaces have witnessed an increase in land cover classes from 34.4%, 10%, and 31.2% in 1988 to 37.7%, 14%, and 38% in 2020, respectively. Our study equally identified population pressure, deforestation, rudimentary agricultural activities, overgrazing and forest exploitation Practices that transformed the landscape of Jakiri. The solutions that have been proposed focus on landscape restoration through improved farming systems such as agroforestry and also the application of certain technical methods. The proposed solutions are those that will take care of the needs of the present generation without jeopardizing that of the future generation.

Thermodynamical Parametrs of Khubsugul Mountain Forests (Khordol-Sardag, Mongolia)

Presented results of using a thermodynamic approach to study the functioning of mountain forest biogeocenoses based on Landsat 8 OLI TIRS multispectral scanner survey for the landscapes of the northwestern Khubsugul region. Using the example of a section of the ridge. Khordol-Sardag considers the spatiotemporal variation of thermodynamic characteristics calculated within the framework of the nonequilibrium Tsallis thermodynamics model. The order parameters of the thermodynamic system (invariant states) are identified based on the principal component method. For each invariant, we assessed the contribution of relief and the leading morphometric characteristics that determine its spatial variation identified. The vertical temperature gradient in different seasons estimated. Shown that in conditions of a sharply continental climate, the thermodynamic system of mountain forests has three functional subsystems responsible for evaporation, thermal field and productivity. Shown that an increase in the mass of woody vegetation increases energy absorption and evapotranspiration, but reduces the disequilibrium of solar energy conversion.

Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem services. Although previous studies have focused on predicting future changes, there is a growing need to determine past scenarios using new assessment tools. This study proposes a methodology for LULC past scenario generation based on transition analysis. Aiming to hindcast LULC scenario in 1970 based on the transition analysis of the past 35 years (from 1985 to 2020), two machine learning algorithms, multilayer perceptron (MLP) and similarity weighted (SimWeight), were employed to determine the driver variables most related to conversions in LULC and to simulate the past. The study focused on the Aristida spp. grasslands in the Uruguayan savannas, where native grasslands have been extensively converted to agricultural areas. Land use and land cover data from the MapBiomas project were integrated with spatial variables such as altimetry, slope, pedology, and linear distances from rivers, roads, urban areas, agriculture, forest, forestry, and native grasslands. The accuracy of the predicted maps was assessed through stratified random sampling of reference images from the Multispectral Scanner (MSS) sensor. The results demonstrate a reduction of approximately 659 934 ha of native grasslands in the study area between 1985 and 2020, directly proportional to the increase in cultivable areas. The MLP algorithm exhibited moderate performance, with notable errors in classifying agriculture and grassland areas. In contrast, the SimWeight algorithm displayed better accuracy, particularly in distinguishing grassland and agriculture classes. The modeled map using SimWeight accurately represented the transitions between grassland and agriculture with a high level of agreement. By modeling the 1970s scenario using the SimWeight model, it was estimated that the Aristida spp. grasslands experienced a substantial reduction in grassland coverage, ranging from 9982.31 to 10 022.32 km2 between 1970 and 2020. This represents a range of 60.8%-61.07% of the total grassland area in 1970. These findings provide valuable insights into the driving factors behind land use change in the Aristida spp. grasslands and offer useful information for land management, conservation, and sustainable development in the region. The study's main contribution lies in the hindcasting of past LULC scenarios, utilizing a tool used primarily for forecasting future scenarios. Integr Environ Assess Manag 2024;20:1140-1155. © 2023 SETAC.

Tracking succession by means of 3D scans of plant communities in a glacier forefield to infer assembly processes

In primary successions, assembling plant communities are key for ecosystem functioning and stability. Often, plant successions are described on a taxonomic, functional and/or phylogenetic level, where species' identities, traits or evolutionary histories are considered. In this study, we exploited community features characterizing whole plant assemblages to capture emerging properties only available at the community level. Next to features aggregating over multiple plant species, we used a customized multispectral 3D plant scanner extracting digital community features as proxies for the frequency distribution of traits, productivity, prevalence of plant competition, and plant strategies and functions. Using these community features, we provide a comprehensive description of plant successions along glacier forefield. Additionally, we used a community feature‐based framework assessing the covariance of community feature dissimilarities and environmental dissimilarity to explore the mechanisms underlying plant community assemblies. Our data indicate a shift from fast‐growing and acquisitive, to slow‐growing and conservative plant strategies; increased productivity; higher competition between plant species; and an increasing contribution to the biogeomorphic stability along the successional gradient. Our results further indicate that stochastic processes dominate in early succession, whereas communities are mainly shaped by deterministic processes including environmental filtering and species interactions at late succession. We conclude that assessments of plant community features, facilitated by the use of field‐ready 3D scanners, provide complementary information to trait‐based approaches on the species level with implications for ecological processes.

SPATIO-TEMPORAL ANALYSIS OF LAND COVER CHANGES OF DHAKA CITY IN BANGLADESH

The objective of this study was to identify the Spatio-temporal variation in landcover of Dhaka city, the capital of Bangladesh. The study was conducted to highlight the issues of changing land-cover of mega cities of Bangladesh and its future environmental consequences. Land cover changes and temporal maps for the selected study areas were developed by using multi-temporal information data sets by Multispectral Scanner (MSS), Thematic Map (TM), Enhanced Thematic Mapper (ETM), Enhanced Thematic Mapper Plus (ETM+),or Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the years 1993, 2000, 2010 and 2015. The results of this study highlighted the rapid urbanization in Dhaka which caused significant decline in vegetation cover and increase of built-up or paved surfaces. The land-cover classified images highlighted, that in 1993 only 22% per cent area of the study area was covered with built-up surface. However, in 2015, it increased up to 48%. This substantial decline in vegetation cover and upsurge in built-up area of Dhaka city may put petrifying effect on local environments.

Effects of Mesoscale Stirring on Phytopigment Determinations in the Photic Water Layer from Multispectral Ocean Color Data (The Case of the Tasman Sea)

The inconstancy of phytopigment composition during intensive mesoscale mixing of the Tasman Sea photic layer was investigated using MODIS images of its surface. To do this, each pixel of such an image is assigned a WRM index equal to the sum of the wavelengths of the minima in the reflectance spectrum of the water surface within the boundaries of a pixel on the ground (Spectral Indexing of Pixels, or SIP approach). WRM is acceptable as an indicator of phytopigment composition variability in the water column, since the attenuation of light by water as a solvent and by its admixtures of other nature is inferior to light absorption by phytopigments in spectral selectivity, while the composition of phytopigments in the aquatic environment depends on the species composition of local phytoplankton. A co-analysis of WRM distributions and characteristics of Tasman Sea waters showed that with increased mesoscale variability in open ocean waters, phytopigment content in the near-surface layer reaches levels at which minimums of pigment origin at 400–550 nm, discernible by multispectral ocean color scanners, occur in the backscattered solar radiation spectrum. This phenomenon is ignored by common algorithms for chlorophyll determination based on the data of multispectral ocean color scanners (band-ratio algorithms) and, apparently, is one of the reasons for the known tendency of such algorithms to overestimate chlorophyll concentration relative to its real content in the water column. The conclusion is applicable to any ocean basins if they, like the Tasman Sea, are not affected by external sources of optically significant admixtures in water.

Detecting individual dead trees using airborne laser scanning

The global crises – climate change and biodiversity loss – have created a need for precise and wide-scale information of forests. Airborne laser scanning (ALS) provides a means for collecting such information, as it enables mapping large areas efficiently with a resolution sufficient for object-level information extraction. Deadwood is an important component of the forest environment, as it stores carbon and provides a habitat for a wide variety of species. Mapping deadwood provides information about the valuable areas regarding biodiversity, which can be used in, e.g., conservation and restoration planning. The aim of this thesis was to develop automated methodology for detecting individual fallen and standing dead trees from ALS data. Studies I and II presented a line detection based method for detecting fallen trees and evaluated its performance on a moderate-density ALS dataset (point density approx. 15 points/m2) and a high point density unmanned aerial vehicle borne laser scanning (ULS) dataset (point density approx. 285 points/m2). In addition, the studies inspected the dataset, methodology, and forest structure related factors affecting the performance of the method. The studies found that the length and diameter of fallen trees significantly impact their detection probability, and that the majority of large fallen trees can be identified from ALS data automatically. Furthermore, study I found that the amount and type of undergrowth and ground vegetation, as well as the size of surrounding living trees determine how accurately fallen trees can be mapped from ALS data. Moreover, study II found that increasing the point density of the laser scanning dataset does not automatically improve the performance of fallen tree detection, unless the methodology is adjusted to consider the increase in noise and detail in the point cloud. Study III inspected the feasibility of high-density discrete return ULS data for mapping individual standing dead trees. The individual tree detection method developed in the study was based on a three-step process consisting of individual tree segmentation, feature extraction, and machine learning based classification. The study found that, while some of the large standing dead trees could be identified from the ULS dataset, basing detection on discrete return data and the geometrical properties of trees did not suffice for acquiring applicable deadwood information. Thus, spectral information acquired with multispectral laser scanners or aerial imaging, or full-waveform laser scanning is necessary for detecting individual standing dead trees with a sufficient accuracy. The findings of this thesis contribute to the existing deadwood detection methodology and improve the understanding of factors to take into account when utilizing ALS for detecting dead trees at a single-tree-level. Although remotely sensed deadwood mapping is still far from a resolved topic, these contributions are a step towards operationalizing remotely sensed biodiversity monitoring.

Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment

Thermal infrared (TIR) satellite imagery collected by multispectral scanners is important to map land surface temperature on a global scale. However, the TIR spectral bands are typically available in coarser spatial resolution than other multispectral bands of shorter wavelengths. Therefore, the spatial resolution of the derived land surface temperature (LST) is limited to around 100 m. This constrains the applications of such thermal satellite sensors in which finer detail of LST spatial pattern is relevant, especially in an urban environment where the land cover structure is complex. Among the missions deployed on the Earth’s orbit, NASA’s TIRS sensor onboard Landsat 8 and Landsat 9, and ASTER onboard Terra provide the highest spatial resolution of the thermal band. On the other hand, ESA’s Sentinel-2 multispectral imagery is collected at a higher spatial resolution of 10 m with a 5-day temporal resolution, but scanning in the TIR band is not available. This study makes use of the known relationship between LST and land cover metrics, such as the normalized difference vegetation index (NDVI), built-up index (NDBI), and water index (NDWI). We define a multiple linear regression model based on the spectral indices and LST derived from Landsat 8 data to inform the same model in which the equivalent spectral indices derived from Sentinel-2 are used to predict LST at 10 m resolution. Results of this approach are demonstrated in a case study for Košice city, Slovakia, where the multiple linear model based on Landsat 8 data achieved an R2 of 0.642. The correlation between the observed Landsat 8 LST and predicted LST from Sentinel-2 aggregated to the same resolution as the observed LST was high (r = 0.91). Despite the imperfections of the downscaling model, the derived LST at 10 m resolution provides a better perception of the LST field that can be easily associated with land cover features present in urban environment. The LST downscaling approach was implemented into Google Earth Engine. It provides a user-friendly online application that can be used for any city or urban region for generating a more realistic spatial pattern of LST than can be directly observed by contemporary Earth observation satellites. The tool aids in urban decision making and planning on how to mitigate overheating of cities to improve the life quality of their citizens.

New techniques for old fires: Using deep learning to augment fire maps from the early satellite era

Disturbances such as fire play a critical role in forest ecosystems. However, anthropogenic fires can profoundly impact forests to the point of destabilizing ecosystems. In addition, fires have legacy effects on environments which may be observed in forests for decades after the fire is extinguished. Thus, understanding the extent of historic fires in a landscape is vital to understanding current forest structure and ecological processes (e.g., carbon sequestration capacity and provision of habitat) and, therefore, essential for informing land-management decisions. However, little work has been done to map forest fires pre 1980s due to the challenges of interpreting imagery from the 1970s-era Landsat Multispectral Scanner (MSS) platform. MSS imagery is distinguished from recent satellite missions through lower temporal, spatial, and spectral resolutions. Recent advances in image processing have brought the goal of high-quality MSS classifications within reach. In this study, we use deep learning, specifically UNet (a fully convolutional neural network (CNN)), to detect historic forest fires in MSS imagery for the forest-dominated regions of Quebec, Canada. While other studies have applied deep learning to present-day satellite data for land cover classification, hardly any work has specifically applied deep learning to MSS data for fire detection. We trained our UNet model on 206 MSS images that were labelled by applying thresholds to the Burned Area Index inside polygons drawn by the authors around burned areas. We then used the trained model to label burns in 5104 MSS images that were compiled to generate annual burned area maps. Our results identified (with a 95% confidence interval) 3503.95 ± 484.90 km2 of burns not previously reported in any database; this represents a 35.30 ± 3.94% increase in the total known burned area across the forest-dominated regions of Quebec between 1973 and 1982.

Detection and Delineation of Agricultural Land Losses in Minna, Niger State, Nigeria

This study assessed the losses in agricultural lands and enrichment of other land usages in agricultural area in Minna and environs in the state capital of Niger state, Nigeria. Data captured using Landsat Multispectral Scanner (MSS) (1990), Thematic Mapper (TM) (2000-2010) and Enhanced Thematic Mapper plus (ETM+) (2000, 2010 and 2020) used to quantify LULC changes. A post-classification matrix prepared to assess the modification and conversion in LULC from 1990 to 2020. The significant modification in LULC observed as agricultural lands and vegetation underwent noticeable decline by 714.39km2 and 578.94km2, respectively whereas built up area increased substantially to a value of 96.91km2. About 12.29km2 agricultural land lost to barren land and 12.83km2 converted to water bodies. Finally, the encroachments of vegetation and built up area in agricultural area caused substantial decreased the available land for agricultural activities. Remote sensing data provide useful for estimations of agricultural land loss on a regional scale.

A Framework of Generating Land Surface Reflectance of China Early Landsat MSS Images by Visibility Data and Its Evaluation

The Landsat time-series dataset is one of the most widely used datasets for land surface research due to its long time-series and Land Surface Reflectance (LSR) product. Though the United States Geological Survey (USGS) provides Landsat LSR products for later Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI), no early Landsat 1–5 Multispectral Scanner System (MSS) LSR product is generated currently, limiting the research traced back to the 1970s. Atmospheric correction is one of the necessary preprocesses for generating LSR products. However, it is challenging for MSS images, not only because the image quality is lower and bands are different compared with the current sensors, but also because of the multiple effects of other preprocesses, such as radiometric calibration. Based on the Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) model, we propose a novel framework for generating Landsat 1–5 MSS LSR data of China. Ground-based visibility records are introduced to replace the images-based aerosol optical depth (AOD) to effectively generate MSS LSR data of the 1970s. We evaluate the generated MSS LSR data by the cross-validation of the simultaneous observation of MSS and TM sensors in Landsat 4 and Landsat 5 using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) surface reflectance product as the truth value. The evaluation result shows that the generated MSS LSR data is comparable with the later Landsat TM LSR product, with slightly larger uncertainties. In addition, it shows that the non-atmospheric factors (e.g., the difference of relative spectral responses of TM and MSS, the georegistration errors, the radiometric calibration uncertainty, and image noises) bring larger uncertainties than the atmospheric factors (e.g., the AOD retrieval method by visibility) to the cross-validation results. We apply the MSS LSR data generated by the proposed framework on time series analysis in the regions of interest (ROIs) of the spectral-stable land cover in China for all the MSS sensors. The application demonstrates the potential and promise of the MSS LSR data generated by the proposed framework.

Urban space change and future prediction of Kanpur Nagar, Uttar Pradesh using EO data

Urban Land use changes, measurements, and the analysis of rate trends of growth would help in resources management and planning, etc. In this study, we analyze the urban change dynamics using a support vector machine model. This method derives the urban and rural land-use change and various components, such as population growth, built-up areas, and other utilities. Urban growth increases rapidly due to exponential growth of population, industrial growth, etc. The population growth also affects the availability of various purposes in its spatial distribution. In this present study, we carried out using multi-temporal satellite remote sensing data Landsat MSS (Multispectral scanner), ETM+ (Enhanced thematic mapper), OLI (Operational land imager) for the analysis of urban change dynamics between years 1980-1990, 1990-2003, 2012-2020 in Kanpur Nagar city in the state of Uttar Pradesh in India. In our study, we used SVM (Support Vector Machine) Model to analyze the urban change dynamics. A support vector machine classification technique was applied to generate the LULC maps using Landsat images of the years 1980, 1990, 2003, and 2020. Envi and ArcGIS software had used to identify the land cover changes and the applying urban simulation model (CA- Markov model) in Idrisi selva edition 17.0 software. The LULC maps of 2003 and 2020 were used to simulate the LULC projected map for 2050 using (Cellular automata) CA- Markov based simulation model.

Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection

The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR data is a problem to be solved. Therefore, we propose a land cover classification method based on multi-scale spatial and spectral feature selection. The public data set of Tobermory Port collected by the Optech Titan multispectral airborne laser scanner was used as research data, and the data was manually divided into eight categories. The method flow is divided into four steps: neighborhood point selection, spatial–spectral feature extraction, feature selection, and classification. First, the K-nearest neighborhood is used to select the neighborhood points for the multispectral LiDAR point cloud data. Additionally, the spatial and spectral features under the multi-scale neighborhood (K = 20, 50, 100, 150) are extracted. The Equalizer Optimization algorithm is used to perform feature selection on multi-scale neighborhood spatial–spectral features, and a feature subset is obtained. Finally, the feature subset is input into the support vector machine (SVM) classifier for training. Using only small training samples (about 0.5% of the total data) to train the SVM classifier, 91.99% overall accuracy (OA), 93.41% average accuracy (AA) and 0.89 kappa coefficient were obtained in study area. Compared with the original information’s classification result, the OA, AA and kappa coefficient increased by 15.66%, 8.7% and 0.19, respectively. The results show that the constructed spatial–spectral features and the application of the Equalizer Optimization algorithm for feature selection are effective in land cover classification with Titan multispectral LiDAR point data.

Four decadal urban land degradation in Pakistan a case study of capital city islamabad during 1979–2019

The study of Land Use Land Cover (LULC) changes and their patterns enhance our understanding about various man caused phenomenon on earth surface. The Capital City of Pakistan, Islamabad has expereinced high rate of urbanization and population increase in the last 3-4 decades and as a result, the LULC changes have occurred in an unsustainable way. The accurate and reliable data of LULC changes for the study period is not available. The main aim of this study was to provide compatible data on LULC change during the last 40 years for the functional city Islamabad, Pakistan. LULC statistics were extracted from Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) images. For each of the target years (1979, 1989, 1999, 2009 and 2019) LULC information was obtained through a supervised classification algorithm. The study area was classified into five major landscape categories, namely built-up, agriculture, forest, water bodies and barren land. The results have indicated that the urban expansion has been increasing due to mass migration and uncontrolled urbanization in the city. The urban sprawl analysis revealed that built-up area and agricultural land has expanded by 41.7% and 5.20% respectively, during 1979–2019. On the other hand, the forest and water bodies declined by 9.03% and 1.21% respectively. The land use changes, have forced the planners to revise the original master plan of the city and including the agricultural sector in the housing area during 2019.