Maximum Likelihood Classification Research Articles

Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle after 2018 Hurricane Michael

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters.

Seguimiento de los cambios en el uso del suelo y la cobertura del suelo utilizando tecnicas de teledeteccion y SIG en la Isla de Phu Quoc, Provincia de Kien Giang, Vietnam

The changes in land use and land cover (LULC) are among the primary drivers of global environmental changes in many developing countries. In this study, LULC changes were assessed on Phu Quoc Island, located in Kien Giang province, Vietnam, from 2001 to 2022. The study utilized remote sensing and Geographic Information System (GIS) technology using Landsat images for the years 2001, 2009, and 2022. Image classification for each year was conducted through supervised classification using a maximum likelihood classifier, with the main LULC classes being forests, bare land, agricultural areas, water bodies, and built-up areas. The accuracy of the classification was evaluated using the kappa coefficient, achieving values consistently above 0.8 for all three images. Over the 20-year period (2001-2022), the area of forest, agriculture, and water bodies decreased by 14.90 km2, 30.96 km2, and 0.64 km2, respectively. Meanwhile, the areas of bare land and built-up areas increased by 22.22 km2 and 24.28 km2, respectively. Additionally, this study employed the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) to quickly assess LULC changes, obtaining results consistent with the supervised classification. The findings underscore the importance of closely monitoring LULC changes to facilitate effective natural resource management and maintain a sustainable environment.

Assessing Two Decades of Land Use/Land Cover Changes in the Uluabat Lake Ramsar Site using Multi-Temporal Satellite Imagery

The Ramsar Convention on Wetlands designates over 2,000 sites of international importance, providing crucial habitats for diverse species. Uluabat Lake, faces anthropogenic pressures such as urbanization, agriculture, and industrialization, affecting its ecological integrity. Using multi-temporal Landsat 7 and Landsat 9 satellite images from 2002 and 2022, along with 2019 management plans, we assessed land use/land cover (LULC) changes in the lake's catchment area. Data were pre-processed with ENVI and stored in ERDAS Imagine. We employed pixel-based image analysis with maximum likelihood classification (MLC) to generate LULC maps and evaluated classification accuracy using ground truth data and the kappa coefficient. Our findings revealed a 15.8% reduction in lake area, from 136.1 km² in 2002 to 114.5 km² in 2022, primarily due to sediment transport from surrounding agricultural land and tributary streams. Urban-agricultural and reed-swamp areas increased by 74.7% and 59.6%, respectively, while shrubs and forests declined by 35.64%, largely from reed conversion to agriculture in the Mustafakemalpaşa River delta. Overall classification accuracy ranged from 88.2% to 91% with a kappa coefficient of 0.81 to 0.82. These transformations highlight the increase in reed and swamp areas and the decrease in lake area, emphasizing the need for effective conservation and management practices.

Assessing Two Decades of Land Use/Land Cover Changes in the Uluabat Lake Ramsar Site using Multi-Temporal Satellite Imagery

The Ramsar Convention on Wetlands designates over 2,000 sites of international importance, providing crucial habitats for diverse species. Uluabat Lake, faces anthropogenic pressures such as urbanization, agriculture, and industrialization, affecting its ecological integrity. Using multi-temporal Landsat 7 and Landsat 9 satellite images from 2002 and 2022, along with 2019 management plans, we assessed land use/land cover (LULC) changes in the lake's catchment area. Data were pre-processed with ENVI and stored in ERDAS Imagine. We employed pixel-based image analysis with maximum likelihood classification (MLC) to generate LULC maps and evaluated classification accuracy using ground truth data and the kappa coefficient. Our findings revealed a 15.8% reduction in lake area, from 136.1 km² in 2002 to 114.5 km² in 2022, primarily due to sediment transport from surrounding agricultural land and tributary streams. Urban-agricultural and reed-swamp areas increased by 74.7% and 59.6%, respectively, while shrubs and forests declined by 35.64%, largely from reed conversion to agriculture in the Mustafakemalpaşa River delta. Overall classification accuracy ranged from 88.2% to 91% with a kappa coefficient of 0.81 to 0.82. These transformations highlight the increase in reed and swamp areas and the decrease in lake area, emphasizing the need for effective conservation and management practices.

Analysis of Urban Thermal Environment Improved by Blue-Green Spaces on the Landsat Data: A Case Study on Tianjin

China’s urbanization is characterized by rapid speed, large scale and high energy consumption, which causes a series of ecological problems. Among these problems, urban thermal environment is particularly prominent, and the most intuitive reflection is the heat island effect. It is of great significance to optimize the urban layout to give full play to the cooling effect of blue-green spaces for improving urban thermal environment. Compared with other methods, it is more extensive and feasible. However, the current research on urban thermal environment mostly focuses on revealing the driving effects of different land use, and lacks of in-depth discussions on blue-green spaces. Tianjin is located in the northeast of the North China Plain, and has a typical warm temperate sub-humid monsoon climate. This paper takes the six districts of Tianjin City as the research area, covering Heping District, Nankai District, Hongqiao District, Hebei District, Hexi District and Hedong District, with a total area of about 181.18 square kilometers. The typical climate and urbanization characteristics of Tianjin can provide reference for cities with the same characteristics. Firstly, this paper had adopted the Landsat changing scenario of the city gained during the periods of 2004, 2011 and 2017 to formulate the land surface temperature by Mono-window Algorithm, and divided the results into seven temperature zones by standard deviation classification method. In regard to blue-green spaces, this paper had identified Landsat satellite data by Maximum Likelihood Classifier, and classified land use into three categories: blue spaces, green spaces, and non-blue and green spaces. And, next, this paper had studied the correlation between the landscape pattern of blue-green spaces and urban thermal environment by Landscape Pattern Analysis, and used Moving-window Analysis to compare the relationship between thermal environment and blue-green spaces at different spatial scales. And so the research results prove that, the high-temperature regions of Tianjin has been expanding along with the expansion and agglomeration of the urban areas and across the river space restrictions, while the low-temperature regions of the city are mainly concentrated in the blue-green spaces, during the periods of 2004, 2011 and 2017. Furthermore, in the landscape pattern level, there is a significant negative correlation between patch perimeter index of blue-green spaces and land surface temperature, and a significant positive correlation between patch perimeter area ratio index of blue-green spaces and land surface temperature. In the multi-scale spatial pattern, the correlation between the Percent of Landscape of blue spaces patches and land surface temperature is the highest in the 1500 meters × 1500 meters sample, and the Percent of Landscape of green spaces patches and land surface temperature is the highest in the 300 meters × 300 meters sample. In addition, the improvement efficiency of thermal environment in blue spaces is higher. In the end, the optimization suggestions of adjusting the layout of blue-green spaces to effectively improve the urban thermal environment problem are proposed. By optimizing the blue-green space landscape patterns at the optimal scale, increasing the blue spaces in the northern high temperature range, widening and transforming the existing waterways, constructing riverside greenways and other strategies to effectively deal with the thermal environmental problems, improving the ecology and livability of the city. It is hoped to provide new ideas and references for the construction of ecological and livable city under the background of rapid urbanization. In the research process, due to the limitation of data sources, calculation methods and other factors, the relevant conclusion is still limited at the spatial scale. In the future research, the thermal environment and the pattern of blue-green spatial landscape at the fine scale will be studied.

An advanced classification method for urban land cover classification

This manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment. The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. The SVM model demonstrates enhanced precision in land cover classification, showcasing its effectiveness in discerning subtle differences in landscape features. Furthermore, using the precise classification results produced by the SVM method, this study examines the temporal variations in land cover between 2018 and 2020. The results provide insight into dynamic land cover changes and highlight the significance of applying reliable classification techniques for thorough temporal analysis with Landsat8 images.

Ứng dụng GIS và tư liệu viễn thám đánh giá biến động lớp phủ rừng tại vùng đệm Vườn Quốc gia Cát Tiên thuộc địa phận huyện Tân Phú, tỉnh Đồng Nai.

The Cat Tien National Park in the South of Vietnam represents a protected ecological area, it is located in tropical rainforest zone. For assessment of forest cover change in three buffer zones of Cat Tien National Park, namely Nam Cat Tien, Nui Tuong and Phu An at Tan Phu district, satellite images (Landsat 5 TM and Landsat 8 OLI) take in 1990, 2000, 2010 and 2020 were used. Land use/land cover were classified by 5 categories using maximum likelihood classifier (MLC) method, result of constructing a key for image classification showed that images without reference data for classification have over 83.50% of map accuracies. After classification, differentiation of forest land use/landcover was analysied in each year. Deforestation and agricultural area reduction took place in 1990 - 2020 period. Besides, study has quantified changes in forest cover during periods: 1990 - 2000, 2000 - 2010 and 2010 - 2020. As a result, findings show that the extents of forest cover have decreased from 5522.22 ha to 2620.98 ha during the period of 1990 - 2020. Maps of forest status as well as changes in forests can be used for forest resource management activities under the Cat Tien National Park management.

Analysis of Land Cover Change in Karanganyar District in 2013 and 2022 Based on Story Maps

Abstract Land use change is influenced by human activities that affect the land cover of an area. Most of the Karanganyar Regency area is still agricultural land and as the population increases from year to year, it has an impact on land conversion. The purpose of this study is to analyze land cover changes in Karanganyar Regency in 2013 and 2022, analyze the factors that influence land change, and present information on land cover changes using Story Maps. The research method used qualitative descriptive analysis with the Maximum Likelihood Classification method and the results tested for accuracy were obtained from the Kappa method. The results showed that land cover change from 2013 to 2022 was dominated by residential land and paddy fields as much as 11946.10 ha or 14.87% and 14777.35 ha or 18.40%. Factors of land cover change are population growth, relatively cheap land value, easy accessibility, and easy licensing mechanism for infrastructure development. The land cover of Karanganyar Regency from 2013 to 2022 is represented in Story Maps, displaying maps and analysis with an attractive and interactive display.

Comparison of the Direct Supervised Classification and Segment Mean Shift Classification: A Case Study of Hetauda Sub-Metropolitan City of Nepal

Comparison between Land Use Land Cover (LULC) classification and methods is a common practice to assess their suitability and reliability in specific areas. In the case of Hetauda Sub-Metropolitan City in Nepal, Sentinel-2B satellite imagery was utilized to evaluate the accuracy of LULC classification through both direct supervised classification and segment mean shift classification. Direct supervised classification was conducted in ERDAS Imagine, while segment mean shift classification was performed in ArcGIS to identify six LULC classes: forest, agriculture, built-up area, bare area, grassland, and waterbody. Pre-processing steps such as layer stacking, mosaicking, data extraction, atmospheric and radiometric correction were employed to enhance the imagery. Training samples were chosen via visual interpretation based on local knowledge to train the Maximum Likelihood Classifier (MLC). The results revealed that the segment mean shift method achieved the highest overall accuracy at 90%, whereas direct supervised classification yielded 85%. Furthermore, kappa statistics indicated a strong level of agreement, with segment mean shift scoring the highest at 0.88 and direct supervised classification at 0.81. Individual class accuracies varied, with forest classification being the most reliable at 98% and waterbody classification at 71%. Overall, the segment mean shift method was deemed more accurate and suitable than the pixel-based direct supervised classification for LULC classification in Hetauda Sub-Metropolitan City, Nepal.

Land use-land cover dynamics in Boyo wetland using remote sensing and GIS for the period 1992–2022

ABSTRACT Land use changes are one of the main factors contributing to the degradation of wetland ecosystems. The objective of this study was to describe land use and land cover (LULC) change in the Boyo Wetland from 1992–2022. We employed remote sensing and GIS to obtain data for this study. As the main data source, Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) images were used. We downloaded Landsat images from the USGS website that were cloud-free. A supervised maximum likelihood classifier algorithm was used for the classification process. Five LULCs are identified, which include farmland, built-up areas, bare land, grazing land, and wetland. For the duration of the research period, the rate of increase in the percent of farmland, built-up, and barren land was observed to be 18.24%, 9.48%, and 4.52%, respectively, while a decreasing pattern was observed in grazing land and wetland by − 19.44% and − 12.79%, respectively. The expansion of agriculture and built-up areas owing to population growth are the primary factors influencing change in the study area. Therefore, a rapid protective measure should be taken to rehabilitate the study wetland.

Identification of Damaged Canopies in Farmland Artificial Shelterbelts Based on Fusion of Unmanned Aerial Vehicle LiDAR and Multispectral Features

With the decline in the protective function for agricultural ecosystems of farmland shelterbelts due to tree withering and dying caused by pest and disease, quickly and accurately identifying the distribution of canopy damage is of great significance for forestry management departments to implement dynamic monitoring. This study focused on Populus bolleana and utilized an unmanned aerial vehicle (UAV) multispectral camera to acquire red–green–blue (RGB) images and multispectral images (MSIs), which were fused with a digital surface model (DSM) generated by UAV LiDAR for feature fusion to obtain DSM + RGB and DSM + MSI images, and random forest (RF), support vector machine (SVM), maximum likelihood classification (MLC), and a deep learning U-Net model were employed to build classification models for forest stand canopy recognition for the four image types. The model results indicate that the recognition performance of RF is superior to that of U-Net, and U-Net performs better overall than SVM and MLC. The classification accuracy of different feature fusion images shows a trend of DSM + MSI images (Kappa = 0.8656, OA = 91.55%) > MSI images > DSM + RGB images > RGB images. DSM + MSI images exhibit the highest producer’s accuracy for identifying healthy and withered canopies, with values of 95.91% and 91.15%, respectively, while RGB images show the lowest accuracy, with producer’s accuracy values of 79.3% and 78.91% for healthy and withered canopies, respectively. This study presents a method for identifying the distribution of Populus bolleana canopies damaged by Anoplophora glabripennis and healthy canopies using the feature fusion of multi-source remote sensing data, providing a valuable data reference for the precise monitoring and management of farmland shelterbelts.

Monitoring firn and wet snow on mountain glaciers: polarization and orbit effects

ABSTRACT Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance. Synthetic Aperture Radar (SAR) images are often used to monitor the characteristics of glaciers based on the backscattering coefficient. However, the influence of satellite orbit and polarization when collecting images for wide regions has not been well considered. This study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Tibet Interior Mountains by using Sentinel-1 C-band SAR data. The investigated regions have different climate patterns. We compare backscatter coefficient distributions for wet snow and firn, derived from maximum likelihood classification under various polarizations, alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and firn. However, a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in summer. We furthermore show, by analyzing weather stations on glaciers, that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the orbit. In contrast, firn ratios across different orbits show less variation in winter, and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic influences. These findings demonstrate glaciers’ sensitivity to temperature fluctuations and the radar wave’s responsiveness to surface characteristics. Consequently, when employing SAR for glacier monitoring, it is crucial to consider the influence of orbit and polarization, in combination with temperature variations, and whether the season is winter or summer.

Assessing the trends and drivers of agricultural land use change in the Ramsar County, northern Iran

Agricultural land use change (ALUC) is a primary driver of global environmental change, with implications for food security and the environment. Ramsar County in the north of Iran has continuously undergone many changes due to the tourism characteristics of the region. The present study demonstrates the ALUC trends from 1992 to 2022 and the main drivers affecting ALUC in Ramsar County, northern Iran, using household surveys and Landsat data. Six land use classifications have been made for the region by image processing. Images have been categorized using the maximum likelihood classifier. Likewise, utilizing a questionnaire from 200 randomly selected farmers, in-person interviews were conducted. The kappa coefficient and overall accuracy for 1992, and 2022 were 85.91%, 88.86%, and 88.38%, 90.83%, respectively. It was observed that from 1992 to 2022, orchard (12%), open land (4.8%), and built-up areas (160.5%), increased respectively. In contrast, forest (−12.1%), water (−82.6%), and agri-land (−14.8%), declined respectively. Additionally, the analysis of the household survey indicates the high cost of production, the lack of a suitable market and price fluctuations of products, the aging of farmers, the increase in the population of non-native residents, weak policies to support farmers, lack of coordination between relevant institutions, the beautiful nature and tourism of the region, and climate changes and drought in other parts of the country were the most important drivers affecting ALUC in the region. Overall, this study can provide significant insights to decision-makers at the beginning of strategic planning to control the ALUC in the region.

Impacts of land use and cover changes on ecosystem service values from 1992 to 2052 in Gena District, Southwest Ethiopia

Land use and cover changes alter the functions and structures of ecosystem, resulting in variations in Ecosystem Service Values (ESVs). Thus, we examined the impacts of land use/land cover (LULC) changes on ESVs from 1992 to 2052 using geospatial technologies. The Landsat images were classified using the supervised maximum likelihood classification technique, and future changes in LULC were predicted using the CA-Markov model. Ecosystem Service Values coefficients were adopted from empirical studies and ESVs changes were evaluated based on the benefit transfer method using LULC data for the study periods, with their corresponding modified ESVs coefficients. The results revealed that, the proportions of grassland, forestland and shrubland declined by 58.5 %, 48.15 % and 33.48 %, respectively, from 1992 to 2022. Simultaneously, the highest rate of expansions of waterbodies (34 times), farmland and settlement threefold as well as bareland (60.2 %) from 1992 to 2022 was noticed. As a result, decreasing trends were experienced in the total ESVs of the district from US$33.6 million in 1992 to US$27.79 million in 2022, and are anticipated to further decline to US$25.94 million in 2052. The ESVs of forestland, shrubland and grassland shrank from 53.1 %, 40 % and 2.78 % in 1992 to 33.28 %, 33.16 % and 1.4 % in 2022 these changes are anticipated to continue for the next three decades, except trend for the increase in grassland ecosystem service value. Therefore, the government should redesign effective land management strategies to alleviate the negative consequences of LULC changes, facilitate payment for ecosystem services, and design ecotourism to boost the income of residents for major land use management-based production systems to increase the ESV in the district.

An experimental comparison of pixel-based and object-based classifications with different machine learning algorithms in landscape pattern analysis – Case study from Quang Ngai city, Vietnam

In landscape pattern analysis, the choice of an efficient method for image classification is widely studied, but the features are mostly extracted from digital values by the traditional approach (Pixel-based) and modern approach (Object-based). In this study, we compared the performance of two supervised classification algorithms (Maximum likelihood classifier - MLC and Support vector machines - SVM). We used SPOT-5 image data from 2011 to analyze the landscape pattern of a complex territory in Quang Ngai City, Vietnam. We collected 215 ground-truth samples and classified them into seven landscape classes. The results showed that the overall accuracy of the classification of Pixel-based (MLC), Object-based (MLC), Pixel-based (SVM) and Object-based (SVM) was 67.9%, 74.0%, 72.1%, and 82.3%, respectively. The combination of the object-based approach and SVM algorithm had the best classification result, which reflected the current spatial distribution of land cover types accurately. In the next step, we computed landscape metrics from detailed images to compare quantitative parameters with the actual verification data sources. From these metrics results, we discussed how classification methods could affect landscape structure, and possible ways to improve the accuracy of landscape-pattern identification.

A Novel Laboratory Technique for Measuring Grain‐Size‐Specific Transport Characteristics of Bed Load Pulses

AbstractAlthough several laboratory studies on the propagation of bed load pulses were carried out in the last decades, most studies neglect grain‐size‐specific aspects or use invasive measurement techniques. To remedy the situation, we present a novel, time‐efficient and non‐destructive laboratory technique to investigate grain‐size‐specific transport characteristics of bed load pulses. The method consists of a through‐water, high‐resolution image acquisition followed by the application of a supervised color classification algorithm (Gaussian Maximum Likelihood Classification). The analyzed bed load pulse consisted of five different grain size classes of dyed quartz sand and gravel, each having a unique color. The initial experimental bed was uni‐colored and contained the same size fractions as the augmented pulse. Quality assessment based on a confusion matrix approach and basic random sampling showed a high classification performance. By statistically analyzing the temporal and spatial color distribution of the experimental reach, characteristic parameters to describe the propagation behavior were determined. The bed load pulse presented in the application example initially showed strong deviations in the grain‐size‐specific advection and dispersion, and advection proved to be predominant in the transport process.

An Investigation of the Impact of Spatiotemporal Changes of Land Use and Land Cover and Land Surface Temperature Using RS Techniques for Selected Cities in Sri Lanka

Changes in land use and land cover (LULC) have an impact on land surface temperature (LST) locally, regionally, and globally. This research focused on the spatiotemporal dynamics of LULC and LST between 2000 and 2021 at six selected meteorological stations, belonging to the different climatic zones of Sri Lanka. The LULC were classified from Landsat ETM+ and OLI/TIRS data using the maximum likelihood classification approach, while LST was retrieved using the thermal band of both Landsat images. Resultsreveal that the built-up areas have increased in all selected locations and bare land, vegetation cover, and agricultural land have all declined in the last 20 years. In Galle and Batticaloa areas, built-up areas have grown significantly by 11.9% and 11.14% respectively, by 2021. The thermal environment spatiotemporal alterations were consistent with the urbanization trend. Since many other land use types have been converted into urban areas, the average LST in Vavuniya, Anuradhapura, Batticaloa, and Galle has been rising steadily. This showed that increasing built-up area density has played a significant role in raising LST in the study areas. The vegetation cover, agricultural land, and water bodies showed the least LST change. By examining the correlation analysis between the Normalized Difference Vegetation Index (NDVI) and LST, a somewhat positive relationship was revealed only in the areas of Galle, Batticaloa, and Hambantota. Nevertheless, the findings of this study will serve as a helpful benchmark for future landscape and urban design initiatives aimed at reducing the detrimental effects of LST on urban sustainability.

Impacts of spatio-temporal change of landscape patterns on habitat quality across Zayanderud Dam watershed in central Iran

The biodiversity of an ecosystem is greatly influenced by the spatio-temporal pattern of the landscape. Understanding how landscape type affects habitat quality (HQ) is important for maintaining environmental and ecological sustainability, preserving biodiversity, and guaranteeing ecological health. This research examined the relationship between the HQ and landscape pattern. The study presented an interpretation of the biodiversity variation associated with the landscape pattern in the Zayanderud Dam watershed area by integrating the Land Change Modeler and the InVEST model. Landsat images and maximum likelihood classification were used to analyze the spatio-temporal characteristics of the landscape pattern in 1991 and 2021. The future landscape pattern in 2051 was simulated using a Land Change Modeler. Subsequently, the InVEST model and the landscape maps were used to identify the spatial distribution of HQ and its changes over three periods. The mean values of the HQ in the study area were 0.601, 0.489, and 0.391, respectively, demonstrating a decreasing trend. The effect of landscape pattern change on HQ was also assessed based on landscape metrics, including PD, NP, SHDI, and CONTAG. HQ had a significant positive correlation with the CONTAG parameter (R = 0.78). Additionally, it had a significant inverse correlation with NP (R = − 0.83), PD (R = − 0.61), and SHDI (R = − 0.42). The results showed that the habitats in the northern region had lower quality compared to those in the southern parts of the Zayanderud Dam watershed. The density, diversity, and connectivity of landscape patches significantly influence the HQ in the study area. This research has the potential to enhance understanding of the impacts of land change patterns on biodiversity and establish a scientific basis for the conservation of natural habitats. Additionally, it can facilitate efficient decision-making and planning related to biodiversity conservation and landscape management.

Prediction of Thrips Damage Distribution in Mango Orchards Using a Novel Maximum Likelihood Classifier

Thrips constitute the primary pest responsible for reducing mango yield and quality every year in Asia. Therefore, the efficient monitoring of thrips damage across mango orchards on a large scale to aid farmers in devising rational pesticide application strategies poses a significant challenge within the current mango industry. This study designs a mango thrips damage inversion prediction method based on the maximum likelihood classifier (MLC). Initially, drone multispectral remote sensing technology is utilized to acquire multispectral data from mango orchards, which are then combined with ground hyperspectral information to identify sensitive bands indicative of mango leaf damage caused by thrips. Subsequently, correlation analysis is conducted on various vegetation indices, leading to the selection of the Greenness Normalized Difference Vegetation Index (GNDVI), which exhibits a strong correlation coefficient of 0.82, as the spectral characteristic parameter for the inversion prediction model. The construction of a remote sensing prediction model for thrips damage distribution in mango orchards is then undertaken based on the MLC. Acknowledging the bias-variance trade-off inherent in the MLC when processing spectral data and its potential limitations in feature extraction and robustness, this study proposes a modification wherein neighboring pixels are weighted differently to enhance the model’s feature extraction capabilities. Experimental results show that the novel MLC maintains stable estimation levels across various numbers of domain pixels, achieving an inversion accuracy of 91.23%. Through the reconstruction of the pixel matrix, the damage distribution of thrips in mango orchards can be swiftly and comprehensively visualized over extensive areas.

Spatiotemporal Land Use/Land Cover Changes and Impact on Urban Thermal Environments: Analyzing Cool Island Intensity Variations

This study pioneers the comprehensive evaluation of the spatiotemporal evolution of land use/land cover (LULC) in Hangzhou city, introducing the novel water body shape index (WBSI) to analyze its seasonal impacts on the urban thermal environment and urban cool island (UCI) effects, uncovering distinct patterns of thermal regulation. It particularly investigates how distance gradients and the water body shape index (WBSI) influence land surface temperature (LST) in the urban core. The region’s climate, featuring hot summers and cold winters, highlights significant seasonal LST variations. Addressing a gap in existing UCI research, the analysis extends beyond the typical large-scale planning focus to include small-scale, high-resolution aspects. Employing remote sensing and geographic information system (GIS) analysis techniques, this study analyzes the seasonal dynamics in Hangzhou’s central urban area. High-resolution LST data, obtained through single-channel inversion and resolution enhancement algorithms, are crucial to this analysis. This study employs the maximum likelihood classification method to analyze land use and land cover changes from 1990 to 2020. This analysis reveals potential drivers of urban thermal environment changes, such as the expansion of residential and commercial areas and the reduction in green spaces. Different regions in LST data are delineated to assess the cool island effect, and the complexity of water body boundaries is quantified using the water body shape index. Spatial and temporal patterns of LST changes are investigated using multivariate regression and time-series analysis models. We identified significant changes in LULC over the past 30 years in Hangzhou, closely correlating with a continuous rise in LST. This observation underscores a clear finding: the strategic importance of blue–green infrastructure in mitigating urban heat, a novel insight that extends the current understanding of urban thermal dynamics. A clear and novel finding of this study is that the intensity of the cool island effect from large water bodies not only diminishes with distance but is intricately influenced by the complexity of their shapes, as quantified by the WBSI, whereas the complexity of their boundaries enhances this effect. Additionally, the regulatory role of the cool island effect is observed to vary seasonally, being most pronounced in summer and less so in autumn and winter, thereby demonstrating a positive impact. In conclusion, our findings innovatively highlight how the specific shapes of water bodies, quantified through the water body shape index (WBSI), emerge as critical, yet previously underappreciated, drivers in modulating the urban thermal environment. This underscores a new avenue for urban planning, advocating for the strategic design of water bodies within urban landscapes. It also finds that spatial factors and seasonal variations significantly affect the intensity of the cool island effect. These findings offer valuable evidence for urban planning and climate change adaptation, emphasizing balancing natural elements with the built environment in urban design.