Thematic Mapper Research Articles

Artificial intelligence tools and vegetation indices combined to estimate aboveground biomass in tropical forests

Measurement of forest biomass is a time-, money-, and labor-consuming activity. Developing methodological approaches for quantifying biomass in natural ecosystems has motivated countless investigations in several vegetation types worldwide. In this study, we developed a model to estimate the above-ground biomass (AGB) in a study site spatially located in Western Pará state, Brazilian Amazon. We applied artificial neural networks and remotely sensed data for adjusting our model based on forest inventory data. We tested four vegetation indices retrieved from a Landsat-5 thematic mapper scene and assessed their correlations with the field measured AGB. We trained 286 artificial neural networks using the vegetation stratus (two phytophysiognomies: dense lowland ombrophilous forest and dense submontane ombrophilous forest) and the normalized difference vegetation index (NDVI) and aerosol-free vegetation index as predictors variables of the AGB. We selected the artificial neural network model showing higher correlation coefficient and concentration of residuals in the center classes, which indicates higher predictive capabilities of biomass estimation. The generated model architecture (4-13-1) was composed by 4 predictor neurons in the input layer, 13 neurons in the hidden layer, activated by logistic functions, and a single neuron (the AGB in ton · ha − 1) in the output layer, activated by an identity function. We observed that the NDVI and aerosol free vegetation index showed the best performance to estimate the AGB in the study area. Using artificial neural network and a Landsat-5 image combined, we were able to accurately predict (estimation error of ∼20 % ) AGB in tropical forest. This is a promising methodological approach that can be applied to assess ecosystem services related to carbon stock in tropical regions.

Prospects and challenges of achieving sustainable urban green-spaces: A case study of urban greening in Dhaka North City Corporation (DNCC), Bangladesh

Urban green spaces and urban forest covers are a significant component of urban planning which is critically ignored in Bangladesh. This study aims to identify the prospects and challenges of achieving sustainable urban green spaces in Dhaka North City Corporation (DNCC), Bangladesh which is a significant indicator of the sustainable development goal (SDG11). We have analyzed the historical land use and land cover change (LULC) from 1992, 2002, 2012, and 2022 using Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) images by ArcGIS 10.8 and Google Earth-pro software. The use of the Normalized Difference Vegetation Index (NDVI) primarily indicates vegetation greenness as well as determining other land uses such as waterbody, settlements and barren land. The calculated kappa values varied from 80% to 86.7% for all the years and fit the current research. A strength-weakness-opportunity-threat (SWOT) analysis reviewed and analyzed existing sectoral policy and plans to identify challenges to achieve sustainable urban forestry. Fifteen key informant interviews (KII) from Dhaka North City Corporation (DNCC), Department of Environment (DOE), Bangladesh Forest Department (BFD), and Department of Social Forestry Department (DSF) wing under BFD were taken to identify the status of urban forestry, its problems, opportunities, and threats. The responses were drawn using a Driver-Pressure-State-Impact-Response (DPSIR) framework. Of the total measurements of 194.2sq km (2022), normal vegetation cover has decreased significantly from 1992 to 2022 by approximately 65.9%, while urban areas increased by almost 95% simultaneously. From 1992 to 2022, areas of water bodies decreased by more than 32.4%. The vegetation coverage in DNCC is only 16.17%, inferior in meeting sustainable urban greening to fulfil the criteria of sustainable cities and communities. From the KII, it is found that despite some relevant forestry policy and plans, limited lands, institutional corruption, and weak governance are the major institutional drawbacks. In the DNCC, unplanned and rapid megacity expansion, roads and utility service expansion weak institutional policy, plan, and program implementation for urban green space protection are some of the driving forces of green cover loss. Introducing strategic environmental assessment of urban forestry policy, plans, and programs, removing inter-institutional conflicts, strategic sectoral plans, and programs for increased green spaces through the bioeconomy concept is required. In addition, increased participation from urban stakeholders is some of the significant responses identified for strategic urban green space and forest cover improvement in DNCC.

Analysis of Multi-Temporal Shoreline Changes Due to a Harbor Using Remote Sensing Data and GIS Techniques

Coastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities.

Evaluating the Spatiotemporal Dynamics of Land Surface Temperature in Relation to the Land Use/Land Cover changes in Nag-Hammadi District, Egypt, using Remote Sensing and GIS

In this study, three multi-temporal remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) and Landsat -8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) in 1990, 2005, and 2020 were used. The maximum likelihood classifier (MLC) was opted to classify land use and land cover (LULC). Land surface temperature (LST) and LULC spectral indices i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Latent Heat Index (NDLI) and Bare Soil Index (BSI) have been computed and their relationships were examined. The overall accuracy of LULC was more than 93%. The analyses showed a notable transformation in LULC over the study period. For instance, built-up areas increased 103.7% with a rate of 45.5 ha/year and agriculture land increased by 28.9% with a rate of 186.4 ha/year. Whereas, bare soil was sharply decreased by 36.4% at a rate of 227.7ha/year. The minimum and maximum LST values increased by 2.9°C and 4.9°C, respectively, from 1990 to 2020. Furthermore, LST has a negative relationship with NDVI and NDLI (NDVI: 1990: r2 = 0.62; 2005: r2 = 0.62; 2020: r2 = 0.65. NDLI: 1990: r2 = 0.79; 2005: r2 = 0.78; 2020: r2 = 0.61) and a positive relationship with NDBI and BSI (NDBI: 1990: r2 = 0.68; 2005: r2 = 0.73; 2020: r2 = 0.44. BSI: 1990: r2 = 0.77; 2005: r2 = 0.78; 2020: r2 = 0.53). These results provided useful information about LULC changes and its impact on LST, which are necessary for experts and land-use planners to formulate sustainable LST mitigation policies, create an environmental comfort in Nag-Hammadi district, and other geographical locations with similar conditions.

NDVI forest cover changes at high altitudes of the Sulaymaniyah Governorate of Kurdistan Region, northern Iraq, 1984−2021

ABSTRACT This study aims to demonstrate the extent of forest cover at high altitudes in Sulaymaniyah Governorate (Province) in the Kurdistan Region in northern Iraq, from 1984 to 2021 and discuss and explain the changes. The forest data were collected from the satellite digital images of the Landsat Thematic Mapper (TM) from 1984 to 2008 and Landsat Operational Land Imager (OLI) from 2014 to 2021. Data preprocessing preparation included employing a radiometric calibration to convert the images from radiance to Top Atmospheric Reflectance (TOA) in order to remove the cosine effect of different solar zenith angles. Next, a quick atmospheric correction (QUAC) was applied to remove atmospheric noise, particles, molecules and vapor from the data; then the basic equation of Normalized Difference Vegetation Index (NDVI) was applied to extract the forest data for 98 samples taken from across the study area. The study shows that forest cover more than doubled during the survey period: in 1984, 9.1% of the study area was covered by forest, but by 2021 forest cover had increased to 18.4%. We discuss how this increase in forest cover is related to better forest management and preservation by the government and by improving the population’s environmental awareness.

Multitemporal analysis of land use and land cover in the Uraim river microbasin, Paragominas - Pará

The objective of the work was to identify and characterize the changes in land use and land cover that occurred between 1984 and 2018, in the watershed of the Uraim River, Paragominas-PA. The method used was deductive, with a quantitative and qualitative scope, and an applied and explanatory nature. Supervised classification was using ENVI Classic, v.5.1 software. The matrix data (Thematic Mapper and Operational Land Imager images from the LANDSAT 4-5 and 8 satellites) used acquired from the USGS platform. Eight classes defined: water bodies, primary forest, altered forest, degraded forest, agricultural activities, exposed soil, urban area, and clouds and shadows. The data obtained indicated that between 1984 and 1999 there was a loss of areas (n= 127,109.12 ha; 44%) in the primary forest class and an increase in the agricultural areas (n= 47,985.03 ha; 9%) in the watershed region, which contributed to the deforestation aspect; this basin suffers environmental pressures, which can seriously impact the water resources that supply a large part of the municipality. Thus, the development of management plans for land use and occupation and maintenance of Environmental Protection Areas is one of the tools indicated for environmental conservation and recovery of degraded areas.

Assessing the Impacts of Land Use/Land Cover Changes on Water Resources of the Nile River Basin, Ethiopia

Land use/land cover change and climate change have diverse impacts on the water resources of river basins. This study investigated the trends of climate change and land use/land cover change in the Nile River Basin. The climate trends were analyzed using the Mann–Kendall test, Sen’s slope estimator test and an innovative trend analysis method. Land use/land cover (LULC) change was examined using Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper (ETM+) with a resolution of 30 m during 2012–2022. The findings revealed that forestland and shrub land area decreased by 5.18 and 2.39%, respectively. On the other hand, area of grassland, cropland, settlements and water bodies increased by 1.56, 6.18, 0.05 and 0.11%, respectively. A significant increasing trend in precipitation was observed at the Gondar (Z = 1.69) and Motta (Z = 0.93) stations. However, the trend was decreasing at the Adet (Z = −0.32), Dangla (Z = −0.37) and Bahir Dar stations. The trend in temperature increased at all stations. The significant changes in land use/land cover may be caused by human-induced activities in the basin.

Investigation and Prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) Changes for Mashhad City in Iran during 1990–2030

Studies on how cities are affected by urban heat islands (UHI) are critical nowadays for a better understanding of the connected effects and for providing helpful insights for sustainable city development planning. In this study, Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper+ (ETM+), and Landsat-8 Operational Land Imager (OLI) images were used to assess the dynamics of the spatiotemporal pattern of land use/land cover (LU/LC) and land surface temperature (LST) in the metropolitan city of Mashhad, Iran in the period between 1990 and 2019. The Markov chain model (MCM) was used to predict LU/LC and LST for 2030. In the analyzed LU/LC maps, three LU/LC classes were distinguished, including built-up land (BUL), vegetated land (VL), and bare land (BL) using the maximum likelihood (ML) classification method. The collected data showed different variations in the geographical pattern of Mashhad LST during the research period that impacted the LST in this metropolis. The study evaluated the variations in LU/LC classes and evaluated their impact on the LST. The value of the LST was positively correlated with the occurrence of the built-up land (BUL), and with the bare land areas, while it was negatively correlated with the occurrence of the VL areas. The analysis of changes observed over three decades with 10-year intervals and the prediction of the LU/LC and LST for 2030 constitute an important contribution to the delineation of the dynamics of long LU/LC and LST records. These innovative results may have an important impact on policymaking fostering environmental sustainability, such as the control and management of urban expansion of Mashhad in connection with UHI.

Thin cloud correction method for visible remote sensing images using a spectral transformation scheme

ABSTRACT Thin cloud contamination adversely influences the interpretation of ground surface information in optical remote sensing images, especially for visible spectral bands with short wavelengths. To eliminate the thin cloud effect in visible remote sensing images, this study proposes a thin cloud correction method based on a spectral transformation scheme. Given the strong linear correlation among the three visible bands, a spectral transformation scheme is presented to derive intermediate images and further estimate spectrally varied transmission maps for the three visible bands. An improved strategy that locally estimates atmospheric light was also developed to produce spatially varied atmospheric light maps. Spectrally varied transmission maps and spatially varied atmospheric light maps contribute to the complete removal of thin clouds. Several remotely sensed images featuring various land covers were collected from Landsat 8 Operational Land Imager, Landsat 7 Enhanced Thematic Mapper Plus, and GaoFen-2 to perform simulated and real tests and verify the effectiveness and universality of the presented approach. Four existing thin cloud correction algorithms were used as baseline for comprehensive evaluation, including three single-image-based approaches and a deep-learning-based approach. Results demonstrate that the proposed method outperformed the other four baseline methods, yielding more natural and clearer cloud-free images. The ground surface information in the cloud-contaminated images can be appropriately recovered by the proposed method, with high determination coefficients (>0.8906) and low root-mean-square errors (<2.4711) and spectral angle maps (<0.8870). In summary, the proposed method can completely remove thin clouds, faithfully recover ground surface information, and effectively work for visible remote sensing images from diverse sensors. Furthermore, the factors potentially affecting correction performance and the applicability to open-source datasets were investigated.

Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager.

GIS based landslide susceptibility mapping and assessment using weighted overlay method in Wayanad: A part of Western Ghats, Kerala

The objectives of the study are to generate a landslide susceptibility zonation (LSZ) map of Wayanad for effective and efficient disaster management in the future and to determine the various parameters and usefulness of remote sensing and GIS in landslide susceptibility mapping. The topographical factors like slope, elevation, and aspects influencing landslides are derived from the digital elevation model (DEM). The rainfall data were collected from 2011 to 2019 from the Indian Meteorological Department (IMD) and four sampling sites were identified through a field visit. We used Landsat8 Enhanced Thematic Mapper Plus (ETM+) satellite imagery with a resolution of 30 m to create land use and land cover (LULC) using the unsupervised classification method. It was divided into seven categories based on the annual precipitation of the study area using inverse distance weighting (IDW). The LSZ map was derived using the weighted overlay method by analysing the geographical and climatic factors. These factors helped produce the final LSZ map, which was divided into three zones from high to low. LSZ areas with steep slopes, high elevation, prolonged rainfall, and predominant land use changes. Study reveals that the north-east, south, and south-east regions of the study area are more vulnerable to landslides.

Assessment of Taungya agroforestry system in dryland forests rehabilitation in Sudan

The decline of the forest cover due to deforestation and agricultural expansion in Sudan has reached a critical situation. Taungya agroforestry systems have been used as a strategy to safeguard against this process, allowing farmers to cultivate their subsistence crops at the early stage of tree plantations. The current study research tackled Nabag Forest Reserve (NFR) as a case study to assess and explore &lt;em&gt;Taungya&lt;/em&gt; agroforestry system as a practice used for forest rehabilitation in the study area. Satellite images of Landsat 5 Thematic Mapper, and Landsat 8 Operational Land Imager/Thermal Infrared Sensor of 1991, 2001, 2011, and 2021 were used to generate forest cover maps. Both unsupervised and supervised classification, as well as ground truth points, were applied to classify the vegetation cover in NFR as dense forest (DF), light forest (LF), agricultural field (AF), and bare land (BL). The results showed that two trends of forest cover changes occurred in NFR between 1991-2001 and 2011-2021. It was well explored that through &lt;em&gt;Taungya &lt;/em&gt;agroforestry, there was a considerable increase in forest cover over this period. This could be clearly detected by increasing dense forest and light forest cover by 1041.73 ha (24.95%) and 2.95 ha (0.07%), respectively and decreasing of bare land and agricultural field by 409.79 ha (9.81%) and 634.52 ha (15.15%) during the addressed period. The findings of the study indicated that &lt;em&gt;Taungya&lt;/em&gt; agroforestry system could be a feasible land-use alternative for forest recovery in the dry land of Sudan.

Land Use/Land Cover Change and Environmental Impact Analysis of Ramgarh-Naudiha Region in Uttar Pradesh, India through Geospatial Technology

Rapidly changing LULC scenario with growing population is of great concern in the modern world. The present study was undertaken to evaluate the changes in LULC pattern in Ramgarh-Naudiha region of Sonbhadra district, UP, over 20 years during 1998-2018 using datasets from the Landsat Thematic Mapper (TM) 5 and Landsat 8 (OLI/TIRS) satellites. LULC map for the chosen period has been generated by unsupervised classification with maximum likelihood algorithm. Results indicate that the study area is vulnerable to such LULC changes due to its sensitive geographic location. It is found that the major changes did happen in agriculture, forest, wasteland and water bodies. Agriculture and Forest areas have decreased by ~2 and 6.56% respectively in the study period. The wastelands had increased fast from 5.08% in 1998 to 18.87% in 2018 at the cost of the forest cover and agricultural land respectively. In 1998, water bodies were 7.49%, whereas, it has decreased to 2.04% in 2018. On the contrary, urban fringe area has grown from 0.33% in 1998 to 0.49% in 2018 especially due to population growth. The present study concludes that this LULC analysis will increase awareness and help in taking necessary action in appropriate land use planning and management.

NVIRONMENTAL IMPACTS OF URBAN SPRAWL USING REMOTE SENSING INDICES: A CASE STUDY OF AMMAN CITY – THE CAPITAL OF JORDAN

Urban sprawl is known to have a negative impact on the environment and is considered one of the main challenges in urban development. The current work aims to understand the spatio-temporal characteristics of urban growth and its environmental impact in the city of Amman. The paper's empirical method relies on NDVI, NDBI, and LST calculated using remote sensing and GIS techniques. For this study, Landsat Thematic Mapper Landsat 5-TM images from 1990, 1995, 1999, and 2004 were acquired, as well as Landsat 8-OLI images from 2013, 2017, and 2022. The surface temperature of Amman has increased as the area of paved roads, residential, commercial and industrial land use types have increased, while green spaces and vacant lots have decreased. The NDVI analysis revealed that there was variation in the vegetation index during the study year due to human activities as well as climatic change. Correlations between biophysical variables and LST revealed that NDVI had a significant negative correlation, while NDBI had a positive relationship. Further, a significant positive correlation was observed between road networks and built-up areas which enhance the occurrence of urban sprawl due to urbanization. This study highlighted the significance of considering the environmental consequences of urban sprawl when developing and implementing GAM policies and strategies.

Effects of Climate Change, Land Use and Land Cover Variability on Green and Blue Water in Wami/Ruvu Basin, Tanzania

Water basins are the primary food sources, giving green water and blue water worldwide. Despite the basins’ potential, information on the periodical variations in blue water and green water is sparse, particularly in developing countries. The study specifically evaluated the changes in land use and land cover variability (LULCV), effects of land use and land cover variability on green water and blue water variations, and effects of climatic changes on green water and blue water. The evaluation involved the Enhanced Thematic Mapper and Operational Land Imager satellite images of 1990, 2000, 2010 and 2020. Image processing utilized the Soil and Water Assessment Tool (SWAT) in ArcGIS software. The land use and land cover variabilities indicated that land use supporting social-economic activities increased, while natural land cover decreased. Proportionally, blue water per annum was decreasing due to declining natural vegetation, enhanced by the increased socio-economic activities. Whereas, the increase in green water per annum was due to the increased temperatures, boosted by climate changes. Since the temperature rise is mainly uncontrolled, greenhouse farming should be encouraged for making green water more productive in agriculture and communities should be encouraged to practice environmentally friendly anthropogenic activities for sustainable green water and blue water management.&#x0D; Keywords: Basin, Green water, Blue water, SWAT model, Climate change

Image Processing Approaches as a Diagnostic Parameter to Determine Pollution by Using Satellite Imagery, Northern Iraq

This work highlights the estimation of the Al-Khoser River water case that disposes of its waste directly into the Tigris River within Mosul city. Furthermore, the work studies the effects of environmental and climate change and the impact of pollution resulting from waste thrown into the Al-Khoser River over the years. Al-Khoser River is located in the Northern Mesopotamia of Mosul city. This study aims to detect the polluted water area and the polluted surrounding area. Temporal remote sensing data of different Landsat generations were considered in this work, specifically Enhanced Thematic Mapper Plus of 2000 and Operational Land Imager of 2015. The study aims to measure the amount of pollution in the study area over 15 years using a supervised classification approach and other tools in ERDAS Imagine Software version 2014. Supervised classification is favored for remote sensing data processing because it contains different digital image processing methods. It is noticed by applying to preprocess and post-processing techniques adopted in the polluted section of Al-Khoser River and monitoring the changes in the objects around it. Hence, the river’s water has been classified into clear water and contaminated water, which shows the impact of pollution over the years. The analysis detected a polluted area in the river that enlarged over the years 2000 to 2015 from 4.139 km² to 21.45 km², respectively. The study showed the differences in the size of objects around the river. The study concludes that daily wastes produced by the residential areas through which Al-Khoser and Tigris rivers pass would cause the polluted sections of the river to increase.

Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 109 m3 from July 2002 to December 2022, with a growth rate of 5.3766 × 108 m3/a. The growth rate from January 2005 to January 2015 is 4.4850 × 108 m3/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 108 m3/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.

Lake surface temperature retrieved from Landsat satellite series (1984 to 2021) for the North Slave Region

Abstract. Lake surface temperature (LST) is an important attribute that highlights regional weather and climate variability and trends. The spatial resolution and thermal sensors on Landsat platforms provide the capability of monitoring the temporal and spatial distribution of lake surface temperature on small- to medium-sized lakes. In this study, a retrieval algorithm was applied to the thermal bands of Landsat archives to generate a LST dataset (North Slave LST dataset) for 535 lakes in the North Slave Region (NSR) of the Northwest Territories (NWT), Canada, for the period of 1984 to 2021. North Slave LST was retrieved from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS); however, most of the dataset was created from the thermal bands of Landsat 5 (43 %) due to its longevity (1984–2013). Cloud masks were applied to Landsat images to eliminate cloud cover. In addition, a 100 m inward buffer was applied to lakes to prevent pixel mixing with shorelines. To evaluate the algorithm applied, retrieved LST was compared with in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) LST observations. A good agreement was observed between in situ observations and North Slave LST, with a mean bias of 0.12 ∘C and a root mean squared deviation (RMSD) of 1.7 ∘C. The North Slave LST dataset contains more available data for warmer months (May to September; 57.3 %) compared to colder months (October to April). The average number of images per year for each lake across the NSR ranged from 20 to 45. The North Slave LST dataset, available at https://doi.org/10.5683/SP3/J4GMC2 (Attiah et al., 2022), will provide communities, scientists, and stakeholders with spatial and temporal changing temperature trends on lakes for the past 38 years.

Shoreline change detection along North Sebou–Moulay Bousselham, based on remote sensing analysis

Sustainable management of the shoreline is crucial. Shoreline mapping is very essential to investigate the impact of the dynamic nature of the coastal area over a certain period. Coastal zone at the north Sebou estuary experience some issues as shoreline retreats as well as shoreline advancement in some locations. Shoreline mapping along the northern area of Sebou estuary was implemented by applying remote sensing technology to detect and analyze shoreline changes.Multi-temporal satellite images with different periods during the last 37 years (1984–2021) were used to detect changes as well as analyze shoreline change rates. Landsat images (Landsat 5–TM, Thematic Mapper TM, Landsat7 ETM+, Landsat 8 OLI_TIRS) were corrected from geometric and radiometric perspectives, then classified by assigning six different classes for analyzing the evolution of the coastal zone from 1984 to 2021. To detect changes in both land use and land cover, the Support Vector Machine algorithm supervised classification was executed to classify the images. As a powerful statistical analysis tool, DSAS was used to assess the rate of shoreline change by EPR technique and to anticipate the required coastal protection measures along the coastline.The classification results show that agricultural land, bare land, and sand-dune beaches have decreased by −8.26%, −5.92%, and −4.53% respectively. On the other hand, statistical analysis of DSAS shows that the northern area of Sebou estuary experienced an average accretion rate of (+0.96 m/yr) while erosion rates achieved an average of (−1.04 m/yr). A drastic erosion was noticeable at the right side of the estuary with (−3.1 m/yr). The decision matrix by EPR method has been developed. It illustrates the evolution, risk level, and proposed decision at each sector along the shorelines during the preceding three decades. The final goal of this study is to support decision-makers in implementing preventive management techniques in the most vulnerable locations.

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.