Multi-temporal Satellite Images Research Articles

Pre- and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural Network

ABSTRACT This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1, Sentinel-2, spectral indices, and terrain features and GEDI canopy height (rh99) as target variables. Our research consisted of three primary objectives: (1) an evaluation of GEDI rh99 canopy height in relation to an nDSM derived from airborne LiDAR, (2) the development of two CNN models for pre- and post-fire scenarios, and (3) utilizing the trained CNN models to generate pre- and post-fire canopy height maps and canopy height change maps across the continuous landscape. GEDI rh99 accuracy analysis revealed R 2 = 0.85, RMSE = 7.25 m and nRMSE = 0.23 for pre-fire GEDI and R 2 = 0.78, RMSE = 10.1 m and nRMSE = 0.3 for post-fire GEDI. The pre-fire CNN model achieved R 2 = 0.75, RMSE = 7.29 m and nRMSE = 0.22, and post-fire CNN model achieved R 2 = 0.62, RMSE = 11.67 m, and nRMSE = 0.34 when validated against nDSM. Bias analysis was conducted, revealing that GEDI rh99 underestimated nDSM across all canopy height ranges, and slope has no significant impact on GEDI accuracy. Notably, post-fire GEDI rh99 exhibited significant underestimation in high to extreme fire severity. The CNN models’ prediction displayed overestimation in short forests < 20 m and increasing underestimation in taller forests > 20 m and underestimation in high and extreme fire severity. Subsequent investigation indicated that canopy layer thinning in high fire severity conditions resulted in weak GEDI waveform signals and consequent bias. Additionally, the GEDI rh99 bias was propagated to the CNN models, together with insensitivity of spectral band values to canopy height in dense and burnt forests potentially contributed to models’ bias. Finally, we demonstrated CNN models’ ability to monitor forest recovery by generating canopy height maps spanning East Gippsland from 2019 to 2023.

Identification of Pine Wilt Disease-Infested Stands Based on Single- and Multi-Temporal Medium-Resolution Satellite Data

Pine wilt disease (PWD) is known for its high lethality and rapid transmission, earning it the name “cancer of the pine tree”. The prompt removal of infested pine trees is an effective measure for preventing and controlling pine wilt disease. Accurate and efficient monitoring technologies are crucial for the scientific prevention and control of this plant disease. Currently, numerous remote sensing monitoring studies have been conducted on pine wilt disease. However, there is limited research on the temporal identification of PWD-infested forest stands over large areas. To build classification models, this study utilized three machine learning algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). We aimed to investigate the effectiveness of single-temporal and multi-temporal Landsat and Sentinel-2 satellite images PWD-infested forest stands detection. The results indicated that, at a spatial resolution of 30 m, Landsat-9 and Sentinel-2 remote sensing images effectively identified PWD-infested forest stands, with classification accuracies of 77.87% and 78.91%, respectively. Higher spatial resolutions in Sentinel-2 remote sensing images were associated with improved identification capabilities. Furthermore, multi-temporal Landsat satellite data (with a classification accuracy of 85.95%) significantly enhanced the performance of the monitoring model compared to single-temporal Landsat satellite data (with a classification accuracy of 77.87%). The RGI difference was found to be the optimal vegetation index. In conclusion, by combining multi-temporal and single-time-phase Landsat remote sensing data, a monitoring model for PWD-infested forest stands was constructed. It achieved a classification accuracy of 88.26%. In this study, a higher accuracy in identifying pine wilt disease and a lower economic cost were achieved by Landsat and Sentinel images, offering valuable insights for the management of pine wilt disease.

CA-ANN based LULC prediction and influence assessment on LST-NDVI using multi-temporal satellite images

CA-ANN based LULC prediction and influence assessment on LST-NDVI using multi-temporal satellite images

Study on shoreline migration and island dynamics over the last five decades in the Muriganga River using multi-temporal satellite images.

The Muriganga River, also known as channel creek, underwent morphological changes often since it is an alluvial as well as a tidal river. The present study analyses the morphological changes in the Muriganga River and its islands with the help of the Remote Sensing and Geographical Information System (GIS) and digital shoreline analysis tool (DSAS 5.0). Moreover, the computation of morphological changes was also performed on two islands, i.e. Sagar and Ghoramara, which are situated just outside the river reach. Eight cloud-free satellite images of Landsat MSS (1972-1980), Landsat TM (1988-2011) and Landsat OLI (2017-2021) have been used to investigate the river shoreline shifting and island dynamics of the Muriganga River resulted from the erosion-accretion process during the last 49 years. For the short-term study, the erosion-accretion rates are derived from one Landsat image to the next, whereas for long-term analysis, the erosion-accretion rates are estimated based on the difference between 1972 as the reference image and the succeeding images. Short-term and long-term analysis shows that the average rate of erosion is more than that of accretion in Muriganga River. It is also found that the areas of Sagar, Ghoramara, Mousuni and Pushpa islands are shrinking continuously, whereas the Niogi and Basit islands are expanding enormously. These may indicate that the shoreline erosion results in widening the river and the eroded materials are accumulated in Niogi and Basit islands. The results suggest that there is an urge for a better coastal management strategy for the erosion control scheme. This study also helps in gaining knowledge of maintaining the navigability in the Muriganga River.

Crop classification in South Korea for multitemporal PlanetScope imagery using SFC-DenseNet-AM

In this manuscript, a new methodology based on a deep learning model using a Siamese network and attention module was proposed to classify crop cultivation areas, such as onion and garlic, from multitemporal PlanetScope images in South Korea. To consider the seasonal characteristics of crops in the model, training data were constructed from multitemporal satellite images. It was generated using PlanetScope satellite imagery from January and April, corresponding to the seasonal growth period of onion and garlic, in South Korea. Image patches were generated by considering the ratio of crops to minimize the influence of imbalanced data in the training process. Siamese FC-DenseNet with an attention module model (SFC-DenseNet-AM) is proposed, and the attention module is used to classify cultivated crop areas. Based on the proposed network, we extract cultivated crop areas using preliminary cultivation information. The results of the experiment using PlanetScope images indicate that image classification for cultivated areas was effectively performed using the proposed deep learning model. The model's performance, with F1-scores of 0.823 (garlic) and 0.774 (onion), was verified through an ablation study.

Forest holds high rodent diversity than other habitats under a rapidly changing and fragmenting landscape in Quirimbas National Park, Mozambique

Habitat loss and fragmentation have widely recognised as the major challenge for biodiversity conservation across the world. This is particularly true in Quirimbas National Park (QNP) where holds unique fauna, and anthropogenic factors have been intensifying leading to a significant loss of biodiversity, especially rodents which play a vital role for the functioning of ecosystems. However, studies assessing rodent diversity, and deeply analysing spatiotemporal dynamics of this vulnerable landscape are still lacking although they are decisive for successful wildlife management and monitoring. This study aimed to assess the rodent diversity, change intensity and its driving forces, and fragmentation of different habitat types in QNP. Multi-temporal satellite images and field data were used to analyse the habitat fragmentation and change intensities at three levels (time interval, category, and transition) from 1990 to 2020. The stratified sampling and capture-recapture approaches were used to survey rodents in three habitats: forest, grassland and cropland. Ecological metrics were employed to measure rodent diversity, distribution and composition. Statistical tests were applied to check differences in rodent abundance and diversity among habitats. The findings revealed an increase in fragmentation of all habitats, and forest experienced the highest losses (107.3 km2) during 1990–2020. The annual rate change intensity was fast from 2010 to 2020. Cropland and built-up land experienced greater intensively gains and losses, while the annual change intensity of forest was dormant over time. Cropland and built-up land expanded at the expense of forest from 2010 to 2020, whereas from 2000 to 2010, cropland's gain targeted intensively grassland, built-up land and water body, and avoided forest. We recorded four rodent species: Acomys spinosissimus, Mastomys verreauxii, Dasymys incomtus and Aethomys chrysophilus; the last three species are new records for the park. Forest revealed the highest rodent diversity (Shannon entropy = 3.0), and the total species richness was comparable to previous studies in eastern Africa. We found significant differences in rodent abundance among habitats. Turnover and nestedness were the main cause of the overall beta diversity. This study can provide vital information for wildlife managers to better understand the process of habitat loss and fragmentation and its implication to biodiversity, which is crucial for applying appropriate conservation management.

Assessment of flood mitigation strategy based on integrated approach of remote sensing and coastal vulnerability geospatial modeling at the coastal plain of Suriname

Suriname is the smallest South American nation with a low-lying coastal plain that is vulnerable to inundation from the Atlantic Ocean and inland rivers, as well as pluvial flooding primarily due to rainfall. Paramaribo, the capital of Suriname, has the highest population density, and its demographics extend into the surrounding districts of Wanica and Commewijne. Suriname has experienced flood disasters almost annually, which has exacerbated in recent years, posing a significant socioeconomic challenge. The country must balance the need for flood disaster adaptation and climate resilience with the potential impact on its resources and well-being of settlement areas. Policymakers and other stakeholders are working to address environmental impacts on the coast, but there is still a need for a comprehensive approach to monitor and manage flood impacts. This research has three objectives. The first is to analyze flood frequency events from 2021 to 2023 using multi-temporal satellite image processing from Sentinel-1 SAR (Synthetic Aperture Radar). The second is to generate a Coastal Flood Vulnerability Index (CFVI) for floods using a geospatial multi-criteria analysis approach based on exposure, sensitivity, and adaptive capacity components. The third objective is to assess the mitigation strategy for floods in settlement areas based on an integrated analysis of CFVI and stakeholder perception. The research methodology uses a multi-criteria analysis regarding settlement areas and ranking each component by expert opinion in an equation derived from Intergovernmental Panel on Climate Change (IPCC) assessment report at district level. The CFVI indices rely on secondary data acquisition from national and global datasets or referenced works. Interviews were conducted to better understand the stakeholder’s perspectives that are at a strategic or governing level, and to evaluate the existence of flood early-warning and other adaptation capabilities. A flood mitigation strategy is then suggested for the most vulnerable district by CFVI score.

Shoreline change pattern analysis of Nagapattinam coastal stretch, Tamilnadu, India using digital shoreline analysis system (DSAS)

We analysed Nagapattinam coastal zones of southeast coast of India, using Multitemporal satellite images for five equal interval of time period (i.e. 2000, 2005, 2010, 2015, 2020 and 2021). The EPR and LLR methods enumerate the maximum accretion at rates of 22.35, 17.02 m/year and maximum erosion rates of -22.82, -10.84 m/year at the study area coastal stretch. This modification is due to several disasters (like Tsunami, cyclone) and anthropogenic activities (like construction of harbours, excavation of beach sand, industrialisation of garbage dump, urbanisation and discharge of domestic sewage). The Kalman filter model forecasting the shoreline by using statistical analysis shows 242 and 236 m in 2031 and 2041 accretion and erosion of -239 and -226 m in same period. The accretion was mainly observed in Vedaranyam and Thirupoondi and erosion was observed in Tharangambadi, Nagapattinam Poompuhar, Karaikal and Thirumullaivasal region. The outcome of this research ensures to create awareness to protect our shoreline and manage our coastal zones properly with several remedial measures for the future.

Google Earth Engine for Advanced Land Cover Analysis from Landsat-8 Data with Spectral and Topographic Insights

The primary goal of this research is to see how effective cloud-based computing services such as Google Earth Engine (GEE) platform are at classifying multitemporal satellite images and producing high-quality land cover maps for the target year of 2020, with the possibility of using it on a larger-scale area such as metropolitan Melbourne as a test site. To create high-quality land cover maps, the GEE is utilized to analyze a total of 80 Landsat-8 images. The support vector machine (SVM) approach is used to classify the images. Moreover, we use spectral bands, spectral indices, and topographic parameters to improve classification and address the limitations of existing approaches for classification with restricted input variables. Furthermore, we apply a postprocessing strategy to increase the model’s performance by removing the salt-and-pepper noise created by misclassified pixels in supervised classification results. The results demonstrate that given all parameters, the SVM approach achieves an overall accuracy (OA) and kappa accuracy of 88.47% and 85.34%, respectively. Following the implementation of the postprocessing technique, the OA and kappa improve to 92.90% and 90.99%, respectively. The results indicate that Landsat-8 multitemporal data, spectral indices, topographic components, and postprocessing techniques are all important in land cover mapping. Therefore, the use of freely accessible GEE technology and multitemporal Landsat-8 data ensures that decision makers have the resources they need to track land cover throughout the year.

Contrastive-Learning-Based Time-Series Feature Representation for Parcel-Based Crop Mapping Using Incomplete Sentinel-2 Image Sequences

Parcel-based crop classification using multi-temporal satellite optical images plays a vital role in precision agriculture. However, optical image sequences may be incomplete due to the occlusion of clouds and shadows. Thus, exploring inherent time-series features to identify crop types from incomplete optical image sequences is a significant challenge. This study developed a contrastive-learning-based framework for time-series feature representation to improve crop classification using incomplete Sentinel-2 image sequences. Central to this method was the combined use of inherent time-series feature representation and machine-learning-based classifications. First, preprocessed multi-temporal Sentinel-2 satellite images were overlaid onto precise farmland parcel maps to generate raw time-series spectral features (with missing values) for each parcel. Second, an enhanced contrastive learning model was established to map the raw time-series spectral features to their inherent feature representation (without missing values). Thirdly, eXtreme Gradient-Boosting-based and Long Short-Term Memory-based classifiers were applied to feature representation to produce crop classification maps. The proposed method is further discussed and validated through parcel-based time-series crop classifications in two study areas (one in Dijon of France and the other in Zhaosu of China) with multi-temporal Sentinel-2 images in comparison to the existing methods. The classification results, demonstrating significant improvements greater than 3% in overall accuracy and 0.04 in F1 scores over comparison methods, indicate the effectiveness of the proposed contrastive-learning-based time-series feature representation for parcel-based crop classification utilizing incomplete Sentinel-2 image sequences.

The Impact and Prediction of Shoreline Dynamics in Pekalongan, Indonesia

Shoreline in Pekalongan has changed and impacted land use significantly. This research investigates historical changes in shoreline dynamics and their impact on land use from 2003–2021. This research also examines the trend of shoreline prediction for 2031–2041. Multitemporal SPOT5 and SPOT6 satellite images were used as primer data to detect shoreline and land use changes. The DSAS (Digital Shoreline Analysis System) analyzes the shoreline change and generates shoreline prediction. This research shows that the coastal areas of Pekalongan have been impacted by erosion, with 528.08 hectares of area and 56.33 hectares of accretion. The North Pekalongan sub-district is the worst area hit by erosion. The shoreline has retreated 2405.8 meters with a 353.3 m/year erosion rate. Several areas of land use were heavily affected by erosion trends, such as ponds, rice fields, and open land. Shoreline predictions generated by DSAS were heavily reliant on historical rates of shoreline change dynamics. The predicted area of the shoreline is estimated to retreat 49.9–466.2 meters from the existing position in the next 10 and 20 years. This research is expected to provide information to local governments for evaluating the maintenance and development planning of the coastal area

Spatiotemporal landuse dynamics driven by socioeconomic context in the southwestern coastal districts of Bangladesh

Socio-economic aspects and the increasing saltwater intrusion toward the southwestern coastal districts of Bangladesh are the main driving force for the alteration of landuse landcover (LULC) pattern. The present study employed a supervised classification approach using the maximum likelihood algorithm method in the geospatial environment to quantify the spatiotemporal LULC changes in the southwestern coastal districts of Bangladesh. The study used multi-temporal Landsat satellite images and identified the influencing socio-economic factors to find the relation with LULC changes. The study has seen a significant relationship between socioeconomic factors and landuse dynamics. Nearly 1500 km2 area has been converted to shrimp farming in almost all districts previously used for rice cultivation or uncultivated. It is actually due to the profitable shrimp production and saline water ingress toward the inland area that induced people to alter the landuse pattern. From 2010 to 2019, shrimp production increased significantly in the study area. The output of the study could be beneficial to policymakers for implementing the landuse policy and sustainable landuse management focusing on the aquaculture industry in this area.

Chronicle of a death foretold: The vanishing of an emblematic cultural landscape results in the loss of its unique plant communities

Badlands are peculiar geomorphological formations shaping landscapes of high ecological and cultural value. In the last decades, land reclamation for agricultural purposes and the cessation of traditional land use, such as sheep grazing, led to their decline in extent in many areas. To quantify the changes in badland-related plant communities, we resurveyed badland vegetation in a site of the Crete Senesi (Siena, central Italy) after 16 years (2006–2022), using 48 quasi-permanent vegetation plots and by means of uni- and multivariate analysis of variance. We found an increase in the total vegetation cover of plant communities growing in former bare soil and sparsely vegetated areas, in line with an overall decrease in the extent of bare soil surfaces in the study area, which we highlighted through the analysis of multitemporal satellite images. Pioneer vegetation characterized by the endemic plant Artemisia caerulescens subsp. cretacea changed into ruderal annual grasslands, while former bare soils were colonized by such pioneer vegetation. In contrast, perennial grasslands remained stable. Grasslands with shrubs became more similar to perennial grasslands in species composition. Species richness increased in former bare soils, and using the total vegetation cover as a proxy for successional stages, we found that Shannon diversity and evenness peaked at about 90% of total cover. In all the stages of colonization, short distance dispersal species prevailed, both therophytes (Avena sterilis, Parapholis strigosa) and perennials (Artemisia caerulescens subsp. cretacea, Bromopsis erecta). Long distance dispersal species (Galatella linosyris) started colonizing at about 60% of total vegetation cover, and at high vegetation cover all the functional groups coexisted. Our results confirm that the badland landscapes of southern Tuscany and specialist plant diversity adapted to badlands are vanishing after a diminishing of active land management, suggesting the current ineffectiveness of the Natura 2000 network in their conservation.

Land Change in Tirthan Valley: The Case of Great Himalayan National Park Conservation Area

Humans are dominant agent of landscape change. Off lately, anthropogenic induced changes have visibly degraded environment around the world putting a serious question over the sustainability of both natural and man-made ecosystems. The sustainability of the ecosystems has found to be directly linked to changes in LULC. The present study aims to analyse long-term land use change in the Tirthan Valley of Himachal Pradesh; the region is a part of protected area collectively known as GHNPCA. With the help of multi-temporal satellite images of LANDSAT, this study reconstructs the trajectory of land changes over a period of five decades. The data shows an increase in forest cover (7.29%), rocky/barren land (45.35%), agriculture/horticulture land (28.94%) and settlement/ built-up (190.48%) while decrease in pasture/open land (29.97%), snow cover (56.14%) and river/water body (54.52%). The results have shown significant land changes in post protected area status.

Surge-type glaciers in Kalaallit Nunaat (Greenland): distribution, temporal patterns and climatic controls

Abstract We present the first systematic inventory of surge-type glaciers for the whole of Greenland compiled from published datasets and multitemporal satellite images and digital elevation models. The inventory allows us to define the spatial and climatic distribution of surge-type glaciers and to analyse the timing of surges from 1985 to 2019. We identified 274 surge-type glaciers, an increase of 37% compared to previous work. Mapping surge-type glacier distribution by temperature and precipitation variables derived from ERA5-Land reanalysis data shows that the west and east clusters occur in well-defined climatic envelopes. Analysis of the timing of surge active phases during the periods ~1985 to 2000 (T1) and ~2000 to 2019 (T2) suggests that overall surge activity is similar in T1 and T2, but there appears to be a reduction in surging in the west cluster in T2. Our climate analysis shows a coincident increase in mean annual and mean winter air temperature between T1 and T2. We suggest that as glaciers thin under current warming, some surge-type glaciers in the west cluster may be being prevented from surging due to (1) their inability to build-up sufficient mass and (2) a switch from a polythermal to a largely cold-based thermal regime.

Multitemporal Analysis of Land Use/Cover Changes and Landscape Fragmentation in Murshidabad District of West Bengal, India

Rapid population growth, human migration, and commercial activities are changing land use and Land cover at a faster rate. The human being's need and greed to sustain themselves alter the earth's natural environment, and that change affected us. However, poor and unmanageable land conversion led to severe environmental effects. For planning and management purposes, precise information regarding land use and its characteristics is required to ensure the sustainability of the area. The current study uses multi-temporal satellite images to analyze the decadal change from 1991 to 2021. Supervised image classification is performed using the Maximum likelihood classifier. The main goal of this study is to compare post-classification results using change vector analysis and analyze human impact on the environment using FRAGSTAT. Fragstat is a widely used software program designed for analyzing spatial patterns in categorical maps. It is commonly employed in landscape ecology, conservation biology, and land management studies. The primary purpose of Fragstats is to quantify and assess the composition and configuration of patches or landscape elements within a given area. The built-up area increased from 2.57% to 8.41% over the past 30 years, while the agricultural land decreased from 83.51% to 70.05%. It was observed that the density of patches and percentage of landscape reduction over time, the rise in the number of patches for agricultural class from 3570 in 1991 to 10173 in 2021 indicates that spatial diversity is increasing in the class with higher levels of anthropogenic disturbances. Moreover, in landscape-level indices, the number of patch and landscape shape index increases, and a fall in the largest patch index indicate that the landscape is becoming more complicated and fragmented. To achieve the sustainable land-use planning and safeguard natural ecosystems and biodiversity from anthropogenic activities, land-use change maps are utilized as an early warning system.

Digital shoreline analysis system techniques for stability detection: An applied case study on Port Said, Egypt

This study aims to assess shoreline change rates at al-Gamil Beach, west of Port Said City on the northeastern Mediterranean coast of Egypt. The study area is characterized by two main tidal inlets connected Lake EL-Manzala by the sea and other coastal protection structures. Given that the al-Gamil coastline shall be monitored to explore erosion and sedimentation changes along the shoreline, multi-temporal satellite images and an ArcGIS-based DSAS model application were utilized to identify and measure changes to the coastline. Five Landsat images covering the study area were used to identify patterns of coastline accretion and erosion. These images were corrected geometrically and radiometrically to help the investigation process of coastline change rates. The three DSAS statistical models: Linear Regression Rate, End Point Rate, and Net Shoreline Movement were used to assess the shoreline change rate. Based on these findings, the shoreline has alternate exposure to accretion and erosion at varying rates, where the average rate of accretion is nearly +9 m/year, while the maximum rate of erosion is about −27 m/year.

The application of Google Earth Engine platform to assess the erosion - accretion processes of the estuary and coastal areas of Tra Khuc river, Quang Ngai province, Vietnam

Along with storms, tides,... human activities have also affected the erosion - accretion process of the coast and estuaries. When global climate change increases the risk of shoreline change in a negative manner, monitoring and quick assessment of this change is of great importance. Google has developed a cloud computing platform, called Google Earth Engine, to effectively address the challenges of big data analysis. This study aims at exploiting and processing multi-temporal satellite images of the Google Earth Engine platform (GEE), combined with topographic data since 1965 to monitor changes in estuary and shoreline of the coastal area of Cua Dai, Tra Khuc river, Quang Nam province during a period of 1965–2021. The analysis results show shoreline changes and hot spots of erosion and landslides along the coast, on both sides of Cua Dai river. After 56 years, the process of erosion - accretion in the river occurs strongly. The eroded riverbanks are located mainly along alluvial plains away from residential areas. Meanwhile, the coast along Cua Dai estuary changes in favor of accretion. However, in recent years, local erosion still occurs in some coastal areas with a length of 0.7–1.2 km in the territory of Pho An - Tan My villages (Nghia An commune). The study shows the potential for directly exploiting and processing large amounts of free satellite image on the GEE for resource monitoring and management applications.

Monitoring of Surface Water bodies in Mainpuri District Using Remote Sensing and GIS Technology

Abstract: These days, a range of analytical tasks are carried out using popular, computer-dependent, time-oriented methodologies such as remote sensing and GIS (Geographic Information System) technology. Remote sensing technology provides a synoptic perspective that aids in mapping the dynamics of surface water bodies at regional and global scales. With this study, we can gather information on seasonal changes over time in the Mainpuri District because variations in the distribution of surface water have an impact on the hydrogeology and physical surroundings of the area. Mainpuri District has a Ramsar site named Saman Bird Sanctuary, which is home to a significant number of migratory and local migratory birds, primarily Sarus cranes. The research demonstrated that the area's abundance of waterbodies benefits biodiversity by giving migratory birds enough food and shelter. Shrinkage and expansion in surface water bodies are useful for determining the ecology of the region. In the current work, pre-monsoon and post-monsoon multi-temporal Landsat satellite images have been used to monitor spatiotemporal changes in the water bodies’ surface area in the Mainpuri District of Uttar Pradesh, India. In the north-west to southeast of the district, waterbodies' temporal changes show a 60% sharp decline in the number of waterbodies from 1992 to 2022.

Estimation of Wheat Yield on a Farm in Najaf/Iraq, using Principal Component Analysis of Multi Temporal Satellite Images Pairs

The present work aims to estimate the yield of wheat crop for a particular wheat farm in Najaf Governorate, southwestern Iraq. Seven multispectral bands of the Landsat satellite (8 and 9) were used for two different time periods. The satellite image for the 1st time (Landsat 8) represents the high vegetative growth of the wheat crop, and the satellite image for the second time (Landsat 9) represents the farm after harvest. Changes in land use were detected based on Principal Component Analysis (PCA) technology wherein PCA images were used to calculate wheat production for a specific area in Najaf scene in Iraq. RGB color model was adopted as an unsupervised method of scene classification as this model was used to determine the number of classes in a scene. Maximum-likelihood method was applied as supervised classification with the images generated by applying the principal component analysis technique for the 1st time and for the 2nd time separately considering the number of classes derived from the RGB color model. The data was read within the borders of the region of interest (a wheat farm) for which the yield will be calculated by creating a mask in which the outer borders of the study area are defined. The results showed that the rate of wheat production for the study area amounted to 97.85 tons, with an error rate that did not exceed (1.55%) when using PC images.