Multispectral Satellite Research Articles

Water quality hotspot identification using a remote sensing and machine learning approach: A case study of the River Ganga near Varanasi

Turbidity (Turb) and Chlorophyll-a (Chl-a) are crucial indicators of water quality because they can reveal the presence of suspended particles and algae, respectively. Understanding the health of rivers and spotting long-term water quality changes can both benefit from monitoring these measures. Traditional methods of monitoring these parameters, like in-situ measurements, is time-consuming, expensive, and inconvenient in some places. Sentinel-2, a multispectral satellite, might offer a more workable and economical option for monitoring water quality, though. This study used 100 in-situ data collected from the Ganga River near Varanasi in the pre-monsoon season (pre-MS) and post-monsoon season (post-MS) in order to create a model for the prediction of optically active water quality parameters by combining Multispectral Instrument (MSI) data and machine learning method (Random Forest). To create spatial distribution maps for Chl-a and Turb, 14 spectral indices and band ratios were employed as independent variables. The results showed that the prediction accuracy for Turb (R2 = 0.91, MAE = 1.13 and MAPE=7.76 % during pre-MS and R2 = 0.93, MAE = 0.88 and MAPE=2.29 % during post-MS) and for Chl-a (R2 = 0.97, MAE = 0.59, and MAPE=2.07 % during pre-MS and R2 = 0.95, MAE = 0.61, and MAPE = 2.71 % during post-MS). The Ganga near Varanasi abruptly turned green due to an increase in algal bloom in May and June 2021. This study not only revealed the reasons behind the green appearance but also identified potential areas of concern or hotspots. In order to identify hotspot locations, drainage networks, point source discharge locations and LU-LC were used.

Spectra-phenology integration for high-resolution, accurate, and scalable mapping of foliar functional traits using time-series Sentinel-2 data

Foliar functional traits are essential for understanding plant adaptation strategies and ecosystem function. Due to limited in-situ observational data, there is a growing interest in upscaling these traits from field sites to regional and global levels. However, limitations persist: (1) global/national scale upscaling that relies on plant functional type (PFT) maps, environmental variables or coarse resolution multispectral images, which fail to capture local-scale trait variability; (2) airborne imaging spectroscopy that enables high-resolution and accurate mapping but is restricted to site scale and is costly; and (3) multispectral satellites like Sentinel-2 that offer global coverage but have limited spectral bands and resolution. While previous research has demonstrated the connection between traits and vegetation phenology, our study seeks to build upon this foundation by further exploring the integration of phenological information for large-scale trait prediction. We examined the integration of Sentinel-2 data with its time series (for phenology information) to map 12 foliar functional traits across 14 National Ecological Observatory Network (NEON) sites in the eastern United States. Our results show that time-series Sentinel-2 models effectively capture the variance in these 12 traits (R2 = 0.60–0.80) when compared with benchmark trait data generated by state-of-the-art airborne imaging spectroscopy. The models adequately capture considerable trait variations observed within sites and PFTs. Our approach outperforms existing methods that rely on environmental variables, or a single Sentinel-2 image as predictors across examined NEON sites in eastern United States. Interestingly, including environmental variables in our models does not significantly improve predictive power. Further analysis reveals that a ‘fast-slow’ principal axis predominantly explains the covariation in Enhanced Vegetation Index amplitude (a proxy for leaf longevity), leaf mass per area, and leaf nitrogen content across PFTs. This finding highlights the importance of incorporating phenological information for trait mapping and suggests a potential mechanism underlying these spectra-based models. Our proposed method, which simultaneously achieves high accuracy, large-scale scalability, and high spatial resolution, represents a promising avenue for future global trait mapping. Validation on a larger scale to fully realize its potential in addressing fundamental ecological questions will be a key future focus.

Evaluating the Efficacy of Sentinel-2B and Landsat-8 for Estimating and Mapping Wheat Straw Cover in Rice–Wheat Fields

Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R2) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R2 = 0.85 and R2 = 0.77, followed by STI, R2 = 0.82 and R2 = 0.80 and NDRI, R2 = 0.69 and R2 = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region.

High Resolution Forest Masking for Seasonal Monitoring with a Regionalized and Colourimetrically Assisted Chorologic Typology

Comparisons of recent global forest products at higher resolutions that are only available annually have shown major disagreements among forested areas in highly fragmented landscapes. A holistic reductionist framework and colourimetry were applied to create a chorologic typology of environmental indicators to map forest extent with an emphasis on large-scale performance, interpretability/communication, and spatial–temporal scalability. Interpretation keys were created to identify forest and non-forest features, and a set of candidate tree cover indices were developed and compared with a decision matrix of prescribed criteria. The candidate indices were intentionally limited to those applying only the visible and NIR bands to obtain the highest possible resolution and be compatible with commonly available multispectral satellites and higher resolution sensors, including aerial and potentially UAV/drone sensors. A new High-Resolution Tree Cover Index (HRTCI) in combination with the Green band was selected as the best index based on scores from the decision matrix. To further improve the performance of the indices, the chorologic typology included two insolation indices, a water index and a NIR surface saturation index, to exclude any remaining spectrally similar but unrelated land cover features such as agriculture, water, and built-up features using a process of elimination. The approach was applied to the four seasons across a wide range of ecosystems in south-eastern Australia, with and without regionalisation, to identify which season produces the most accurate results for each ecoregion and to assess the potential for mitigating the spatial–temporal scaling effects of the Modifiable Spatio-Temporal Unit Problem. Autumn was found to be the most effective season, yielding overall accuracies of 94.19% for the full extent, 95.79% for the temperate zone, and 95.71% for the arid zone. It produced the greatest spatial agreement between two recognised global products, the GEDI forest heights extent and the ESA WorldCover Tree cover class. The performance, transparency, and scalability of the approach should provide the basis for a framework for globally relatable forest monitoring.

Assessing Transferability of Remote Sensing Pasture Estimates Using Multiple Machine Learning Algorithms and Evaluation Structures

Both the vastness of pasturelands and the value they contain—e.g., food security, ecosystem services—have resulted in increased scientific and industry efforts to remotely monitor them via satellite imagery and machine learning (ML). However, the transferability of these models is uncertain, as modelers commonly train and test on site-specific or homogenized—i.e., randomly partitioned—datasets and choose complex ML algorithms with increased potential to overfit a limited dataset. In this study, we evaluated the accuracy and transferability of remote sensing pasture models, using multiple ML algorithms and evaluation structures. Specifically, we predicted pasture above-ground biomass and nitrogen concentration from Sentinel-2 imagery. The implemented ML algorithms include principal components regression (PCR), partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine regression (SVR), and a gradient boosting model (GBM). The evaluation structures were determined using levels of spatial and temporal dissimilarity to partition the train and test datasets. Our results demonstrated a general decline in accuracy as evaluation structures increase in spatiotemporal dissimilarity. In addition, the more simplistic algorithms—PCR, PLSR, and LASSO—out-performed the more complex models RF, SVR, and GBM for the prediction of dissimilar evaluation structures. We conclude that multi-spectral satellite and pasture physiological variable datasets, such as the one presented in this study, contain spatiotemporal internal dependence, which makes the generalization of predictive models to new localities challenging, especially for complex ML algorithms. Further studies on this topic should include the assessment of model transferability by using dissimilar evaluation structures, and we expect generalization to improve for larger and denser datasets.

Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches

Early prediction of crop production by remote sensing (RS) may help to plan the harvest and ensure food security. This study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery. Ground-truth wheat traits were measured at flowering and harvest in a field experiment combining four N and two water levels in central Spain over 2 years. Hyperspectral and thermal airborne images coincident with Sentinel-1 and Sentinel-2 were acquired at flowering. A parametric linear model using all hyperspectral normalized difference spectral indices (NDSI) and two non-parametric models (artificial neural network and random forest) were used to assess their estimation ability combining NDSIs and other RS indicators. The feasibility of using freely available multispectral satellite was tested by applying the same methodology but using Sentinel-1 and Sentinel-2 bands. Yield estimation obtained the highest R2 value, showing that the visible and short-wave infrared region (VSWIR) had similar accuracy to the hyperspectral and Sentinel-2 imagery (R2 ≈ 0.84). The SWIR bands were important in the GPC estimation with both sensors, whereas N output was better estimated using red-edge-based NDSIs, obtaining satisfactory results with the hyperspectral sensor (R2 = 0.74) and with the Sentinel-2 (R2 = 0.62). When including the Sentinel-2 SWIR index, the NDSI (B11, B3) improved the estimation of N output (R2 = 0.71). Ensemble models based on Sentinel were found to be as reliable as those based on hyperspectral imagery, and including SWIR information improved the quantification of N-related traits.

A model of the productive capacity of soils of land cover in Al-Suwayr district using remote sensing technology and geographic information systems

In this research, there is an approach to estimating the quality of soils in an arid environment, as it has an arid and semi-arid climate in southern Iraq, which is Al-Suwayr district of Al-Muthanna Governorate. The technology in the detection of land cover based on the data of the US Geological Survey (earthexplorer.usgs.gov), for the Sentinel satellite and the multispectral satellite (Land sat-OLI TIRS C1) (see attached image 1(a,b,c). ) and working on an analysis of those visuals to monitor and detect land cover changes during several years (2000, 2015, 2021) in terms of changing the area of vegetation, water and urban cover, using the spatial analysis tools in the Spatial Analysis Tool and choosing the Map Algebra- Raster Calculator tool, and that To detect land cover indicators (vegetation index, water index, urban cover index), and soil types will be revealed through the International Union of Soil Sciences working group TSD 2006. Global reference base for soil resources 2 006 from the site (https://soilgrids.org/), see the attachment of the image (2) in order to reach the analysis of soil capacity and its classification in Al-Suwayr district according to the construction of the preferred land cover model according to the analysis of layers (NDWI, NDVI, NDBI), and then Overlay spatial matching and the study concluded that merging the layers of the appropriate land cover model with the layer of soil types and the type of sediment, it was possible to determine the classification of the productive capacity of soil in Al-Suwayr district.

THE ROLE OF GIS IN ADDRESSING ENVIRONMENTAL, SOCIAL AND TOURISTIC CHALLENGES IN MOHAMMEDIA CITY, MOROCCO

Abstract. During the last years, Morocco’s Environmental, Social and Touristic perspectives has improved significantly, but it remains one of the biggest challenges for Mohammedia city. The purpose of this study is to locate an optimal site for the construction of a new Waste Water Treatment Plant (WWTP) in order to help the environment, to select an appropriate site for the construction of a new School that will benefit the social needs of the city and to create a suitable tour plan of touristic sites for tourism. It was carried out by using Geographic Information System (GIS), Remote Sensing (RS), Global Positioning System (GPS) devices, Digital cameras and Multi- criteria Decision Analysis (MCDA).Throughout the process, the used data for both WWTP and School selection include the remote sensing data of multi spectral satellite imageries and the digital elevation model combined with vector data of land use (LU) layers, and for Tour creation the used data include collected photographs and GPS data from the site. According to MCDA, the results presents an optimal site with an area of 48283,02 m2 for a new WWTP, a suitable area with an area of 3200 m² for a new School and a city tour for tourists with a distance of 27 km.

Land Suitability Mapping for Optimum Soybean Production in Konshisha Local Government Area, Benue State, Nigeria.

Land suitability mapping is a pre-requisite for optimum crop production and sustainable use of the land to provide food security. Soya bean is one of the most valuable food and cash crops produced in Konshisha LGA, Benue State, Nigeria. However, the production is constrained by low yield. Thus, the aim of this study is to map land suitable for optimum and sustainable soya bean production in the study area. As an effort toward achieving the Sustainable Development Goal (SDG) target of eradicating hunger by 2030. Multi spectral satellite imageries from Landsat-8 Operational Land Imager, Climate Research Unit (CRU), National Aeronautical Space Agency’s (NASA) Shuttle Radar Topographic Mission (SRTM) and ORLN DAAC soil data were collected using remote sensing and GIS techniques for Multi-criteria evaluation (MCE). GIS analysis and Analytical Hierarchical Process (AHP) were carried out. Result of the multi criteria evaluation shows that climate, slope, land use land cover, and available phosphorus are favorable factors, while soil organic carbon, pH and drainage are limiting factors. The result of the overall suitability mapping shows that 30.4% of the land is highly suitable (S1), 69.5% moderately suitable (S2), and 0.1% marginally suitable (S3) for soya bean production in the study area. Based on the findings, the study concluded that low soil organic carbon, pH and poor soil drainage are the main land limiting factors for optimum soya bean production. Therefore, the study recommends application of mixed organic and inorganic fertilizers, agricultural lime, composite manure, agro-forestry, crop rotation, creation of open deep drains and channelization as supplement of the limiting factors for optimum soya bean production in the study area.

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks

Multispectral satellites that measure the reflected energy from the different regions on the Earth generate the multispectral (MS) images continuously. The following MS image for the same region can be acquired with respect to the satellite revisit period. The images captured at different times over the same region are called multitemporal images. Traditional compression methods generally benefit from spectral and spatial correlation within the MS image. However, there is also a temporal correlation between multitemporal images. To this end, we propose a novel generative adversarial network (GAN) based prediction method called MultiTempGAN for compression of multitemporal MS images. The proposed method defines a lightweight GAN-based model that learns to transform the reference image to the target image. Here, the generator parameters of MultiTempGAN are saved for the reconstruction purpose in the receiver system. Due to MultiTempGAN has a low number of parameters, it provides efficiency in multitemporal MS image compression. Experiments were carried out on three Sentinel-2 MS image pairs belonging to different geographical regions. We compared the proposed method with JPEG2000-based conventional compression methods and three deep learning methods in terms of signal-to-noise ratio, mean spectral angle, mean spectral correlation, and laplacian mean square error metrics. Additionally, we have also evaluated the change detection performances and visual maps of the methods. Experimental results demonstrate that MultiTempGAN not only achieves the best metric values among the other methods at high compression ratios but also presents convincing performances in change detection applications.

Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird’s eye (UAS) perspective

Characterizing the spatial variability of the severity of wildfires is important to assess ecological and economic consequences and to coordinate mitigation strategies. Vegetation indices such as the differenced Normalized Burn Ratio (dNBR) have become a standard tool to assess burn or fire severity across larger areas and are being used operationally. Despite the frequent application of dNBR-like vegetation indices, it is not yet fully understood which variables exactly drive the variability in dNBR observed by multispectral satellites. One reason for this is the lack of high quality prefire information about vegetation structure and composition. Consequently, the influence of prefire vegetation composition and other potentially influential variables such as cast shadows has hardly been examined. Here, we use very high resolution Unmanned Aerial System (UAS) orthoimages collected briefly before and after the large wildfires in Central Chile in the fire season 2016/2017 to derive variables related to the pre- and postfire landscape composition and structure. The variables are used as predictors in Generalized Additive Models (GAM) to explain the spatial variability in dNBR and RdNBR pixel values as observed by Sentinel-2. Our models explain more than 80% and 75% of the variability in dNBR and RdNBR values, respectively, using a sparse set of five predictors. The results suggest that in our study area the largest fraction of variability in Sentinel-2 based dNBR and RdNBR values can be explained by variables related to the fraction of consumed canopy cover while the vegetation composition before the fire does not have a large influence on dNBR and RdNBR.Our results further suggest that cast-shadows of snags and standing dead trees with remaining crown structure have a notable influence on the dNBR signal which may have been underestimated so far. We conclude that spatially continuous, very high spatial resolution data from UAS can be a valuable data source for an improved understanding of the exact meaning of common vegetation index products, operationally used for monitoring the environment.

Использование вегетационных индексов для экстраполяции результатов элементного анализа накопленного содержания тяжёлых металлов в лесном массиве

Accumulating heavy metals in urban and suburban forest areas is becoming a challenge. Usually analytical methods are employed for assessment of elements concentration in absorbing environments. They are related to some difficulties for characteristic of the whole forest area. The index images were used for solving this task. Multispectral Santinel 2A satellite pictures made the base data for vegetation indices. More than 20 indices were chosen as aid to diagnose the increase of heavy metal concentration, especially photosynthetic apparatus alteration. The element analysis was done by atom-absorption method. The concentration of Cu, Zn, Pb, Cd, Ni, Cr и St was analyzed. The relation between concentration of each element and some vegetation indices was shown by regression analysis. We used multiple or Poisson regression according to predictors distribution. Basing on regression equation the map of Cu concentration in a small birch grove area was made and average Cu concentration in this forest estimated.

Urban overspill and its upshot to the Bafoussam Emergent Metropolis in Cameroon

Unbridled urban sprawl in Sub-Saharan African cities reflects an infirmity that warrants both vaccination and treatment to curb urban disorder and its costs. This article divulges that rapid population growth, spatial expansion and economic activities are responsible for the urban overspill and its upshot in Bafoussam. Unplanned settlement from the rapid growing population has led to the development of slumps and reduction of arable land. A multi-spectral Landsat satellite imagery of 1988, 2001 and 2016 was used to determine the spatial expansion of the town over a period of 30 years. Field observation and interview sessions were done to have information on the proliferation of economic activities to the expansion of the town. An in-depth secondary data collection was done to gather information on the rate of population growth of Bafoussam. The data was processed to generate maps through the ArcGIS 10.4 and Adobe Illustrator CS soft wares and tables through SPSS 17 for results and analyses. Results from findings propound a significant increase in the built-up area of Bafoussam of 19.34% in 1988, 50.30% in 2001 and 79.41% in 2016. This increase was accompanied by a drop in the vegetation of 78.64% in 1988, 48.50% in 2001 and 20.07% in 2016. The built-up increase was provoked by a persistent rise in the population of the city from 62, 239 inhabitants in 1976 to 112,681 in 1987 and 282,800 in 2010. Industrial and commercial activities developed in the course of the twin increase and unfortunately with no control triggered urban disorder. This article advocates for a mandatory implementation of the urban norms in Cameroon to liberate Bafoussam from the drowning overspill syndrome plaguing Sub-Saharan cities.

USE AN OPEN SOURCE CHANGE MATTERS SITE TO MEASURE THE VEGETATION INDEX IN THE WESTERN PLATEAU OF ANBAR IN IRAQ

As a result of the development of geo-technologies, in recent years, software has emerged that has provided the possibility of deriving information. The most important of these are geographic information systems (GIS), which are many sources in satellite data from multi-spectral satellites, The source via the Internet is one of the most important modern means of providing large data that varied between maps and satellite images (LANDSAT-SPOT-NASA). The study of land cover changes is one of the most important subjects of interest to the sciences and in particular the geographical research related to the environment and its effects on humans and living organisms. In the current research, the data of interactive open source maps were adopted, which provided the possibility of analysis. Using the Change Matters esri, it is one of the most efficient locations for detection of land cover changes by dealing with visuals for different periods of time. The study also reviewed some of the open source websites dealing with GIS to indicate their importance and role in the analysis and production of maps. The research aims to employ open-source digital technologies via the web to extract information about land cover changes.

A PCA-PD fusion method for change detection in remote sensing multi temporal images

In remote sensing, for applications as environment monitoring, change detection based on image processing is one of the most important techniques. To reach high performance various techniques of fusion are exploited using a combination of multi-temporal, multispectral and panchromatic satellite images. A solution for handling such kind of images holds when using some simple statistical methods like the Percent Difference (PD) technique as well as the Principal Component Analysis (PCA) one. In this paper, an automatic change detection method issued from the two previous techniques is proposed and applied on multispectral and panchromatic images captured by a high resolution optical satellite. This approach is characterized by two aspects: the first one consists of the fusion of the different data and the second one performs the detection of the changes for the resulting images. The experimental results show the reasonable quantitative performance and the effectiveness of the proposed method for change detection, consisting of an automatic extraction of most of change information as well as the obtention of better results for most precision metrics consisting of an overall accuracy of up to 91% and a Kappa coefficient of up to 66%, comparing to those obtained using the simple PD and PCA techniques.

Cross Fusion-Based Low Dynamic and Saturated Image Enhancement for Infrared Search and Tracking Systems

Unmanned aerial vehicles and battleships are equipped with the infrared search and tracking (IRST) systems for its mission to search and detect targets even in low visibility environments. However, infrared sensors are easily affected by diverse types of conditions, therefore most of IRST systems need to apply advanced contrast enhancement (CE) methods to cope with the low dynamic range of sensor output and image saturation. The general histogram equalization for infrared images has unwanted side effects such as low contrast expansion and saturation. Also, the local area processing for saturation reduction has been studied to solve the problems regarding the saturation and non-uniformity. We propose the cross fusion based adaptive contrast enhancement with three counter non-uniformity methods. We evaluate the proposed method and compare it with conventional CE methods using the discrete entropy, PSNR, SSIM, RMSE, and computation time indexes. We present the experimental results for images from various products using several datasets such as infrared, multi-spectral satellite, surveillance, general gray and color images, as well as video sequences. The results are compared using the integrated image quality measurement index and they show that the proposed method maintains its performance on various degraded datasets.

Environmental Assessment of Land Use Land Cover Using Remote Sensing and GIS: A Case Study of Khirthar National Park

National Park area is a natural reserve with an outstanding beauty having rich biodiversity, natural eco-system and sustainable environment is a gift from nature to conserve. It is extremely important to monitor the sustainability of National Park by extracting land use land cover change information over the period of time using satellite imagery which is the main application of change detection. The aim of this research study is to assess the land use land cover change at Khirthar National Park by acquiring multispectral Land sat satellite imagery to detect decadal information from 1992 to 2019 using supervised classification Maximum Likelihood classifier. The Land sat satellite data used in this research study was almost of the summer season to assess land use land cover change using supervised classification Maximum Likelihood Classifier and results showed increasing trend in agriculture while decreasing trend in natural vegetation at Khirthar National Park which is a clear indication of increasing anthropogenic activities. The supervised classification Maximum Likelihood Algorithm used to detect land use land cover change at Khirthar National Park results overall accuracy in the year 1992, 2000, 2010 and 2019 was 99.7446%, 99.4536%, 99.6495% and 96.7530% while the kappa statistics results achieved in the year 1992, 2000, 2010 and 2019 was 0.9951, 0.9879, 0.9648 and 0.9503. These increasing human activities will not only disturbs the ecology of National Park but also will impact on the local flora and fauna of the park, therefore it is highly recommended to prevent further loss of biodiversity, natural eco-system and the sustainable management for such natural heritage.

Considerations in Harmful Algal Bloom Research and Monitoring: Perspectives From a Consensus-Building Workshop and Technology Testing

Recurrent blooms of harmful algae and cyanobacteria (HABs) plague many coastal and inland waters throughout the USA and have significant socioeconomic impacts to the adjacent communities. Notable HAB events in recent years continue to underscore the many remaining gaps in knowledge and increased needs for technological advances leading to early detection. This review summarizes the main research and management priorities that can be addressed through ocean observation-based approaches and technological solutions for harmful algal blooms, provides an update to the state of the technology to detect HAB events based on recent activities of the Alliance for Coastal Technology (ACT), offers considerations for ensuring data quality, and highlights both ongoing challenges and opportunities for solutions in integrating HAB-focused technologies in research and management. Specifically, technological advances are discussed for remote sensing (both multispectral satellite and hyperspectral); deployable in situ detection of HAB species on fixed or mobile platforms (based on bulk or taxa-specific biomass, images, or molecular approaches); and field-based and/or rapid quantitative detection of HAB toxins (via molecular and analytical chemistry methods). Suggestions for addressing challenges to continued development and adoption of new technologies are summarized, based on a consensus-building workshop hosted by ACT, including dealing with the uncertainties in investment for HAB research, monitoring, and management. Challenges associated with choosing appropriate technologies for a given ecosystem and/or management concern are also addressed, and examples of programs that are leveraging and combining complementary approaches are highlighted.