Interpretation Of Imagery Research Articles

ANALISIS PERUBAHAN PENGGUNAAN LAHAN DAERAH TANGKAPAN AIR DANAU KERINCI (Land Use Change Analysis in The Kerinci Lake Catchment Area)

Lake Kerinci is one of the 15 national priority lakes. In general, residents in this area carry out agricultural or plantation activities without applying conservation principles, resulting in a decrease in the carrying capacity of the Catchment Area (DTA). In addition, DTA Lake Kerinci has also experienced an increase in population, thus encouraging land-use changes. This study aims to analyze land-use changes in DTA Lake Kerinci. Analysis of land-use change analysis uses Landsat imagery 5 TM for 2009 and Landsat imagery 8 OLI-TIRS for 2019 using the guided classification method (Maximum Likelihood Classification/MLC). The accuracy of the image interpretation results is carried out through the Kappa accuracy test. Interpretation of land-use imagery and classification have relatively high accuracy with a kappa value of 78.67% (good) on land use in 2009, 83.30% (almost perfect) on land use in 2019, with an average Kappa Accuracy (KA) of 79.63%. Land-use changes in Lake Kerinci DTA for the 2009-2019 period experienced a significant increase in the area of dry agricultural land by 13% or 13,151.05 ha and a decrease in primary forest area by 6.76% or 6,834.13 ha.

A Typology for Reef Passages

Coral reefs host exceptionally diverse and abundant marine life. Connecting coasts and sheltered lagoons to the open ocean, reef passages are important yet poorly studied components of these ecosystems. Abiotic and biotic elements ‘pass’ through these reef passages, supporting critical ecological processes (e.g. fish spawning). Reef passages provide multiple social and ecological benefits for islands and their peoples, but are so far neither characterized nor recognized for their multifaceted significance. This study investigated 113 reef passages across nine Pacific islands (Fiji, New Caledonia, Vanuatu). GIS-based visual interpretations of satellite imagery were used to develop criteria to define three distinct types, mainly based on distance to coastline and presence/absence of an enclosed water body. The discussion identifies ways to refine and augment this preliminary typology as part of a research agenda for reef passages. With these next steps, this typology will be extendable to other regions to better document reef passages and their various roles, supporting biodiversity conservation and sustainable fisheries management.

Slowing deforestation in Indonesia follows declining oil palm expansion and lower oil prices

Much concern about tropical deforestation focuses on oil palm plantations, but their impacts remain poorly quantified. Using nation-wide interpretation of satellite imagery, and sample-based error calibration, we estimated the impact of large-scale (industrial) and smallholder oil palm plantations on natural old-growth ("primary") forests from 2001 to 2019 in Indonesia, the world's largest palm oil producer. Over nineteen years, the area mapped under oil palm doubled, reaching 16.24 Mha in 2019 (64% industrial; 36% smallholder), more than the official estimates of 14.72 Mha. The forest area declined by 11% (9.79 Mha), including 32% (3.09 Mha) ultimately converted into oil palm, and 29% (2.85 Mha) cleared and converted in the same year. Industrial plantations replaced more forest than detected smallholder plantings (2.13 Mha vs 0.72 Mha). New plantations peaked in 2009 and 2012 and declined thereafter. Expansion of industrial plantations and forest loss were correlated with palm oil prices. A price decline of 1% was associated with a 1.08% decrease in new industrial plantations and with a 0.68% decrease of forest loss. Deforestation fell below pre-2004 levels in 2017-2019 providing an opportunity to focus on sustainable management. As the price of palm oil has doubled since the start of the COVID-19 pandemic, effective regulation is key to minimising future forest conversion.

Identifikasi Perubahan Luas Lahan Perkebunan Kelapa Di Kecamatan Sungai Kakap Kabupaten Kubu Raya

Identification of Coconut Plantation Area Changes In Sungai Kakap Sub-District Kubu Raya Regency, Land cover turnover and land use cannot be averted. Growth of the city and change in needs of facilities have caused the agricultural and plantation lands to undergo change in their functions. This reasearch aims to identify the change of coconut plantation land area in Sungai Kakap sub-district, Kubu Raya regency. The methods used in this research were surveys and landsat imagery interpretation of 2002 and 2017. The result shows that there were an increasement of coconut plantation area amounted to 6452,19 ha or about 403 ha per year from 2002 to 2017. The increasement of plantation area is due to the improvement of coconuts' selling price, thus encouraging farmers to manage and to expand the plantation area of coconut.

Pengaruh Tutupan Hutan dan Lahan Terhadap Sektor Pertanian dalam Perekonomian Pendapatan Daerah: Studi Di Provinsi Lampung

Deforestation and land cover changes are the upstream causal of the environmental performance and welfare, namely starting from declining hydro-orological functions, increased microclimate fluctuations, the emergence of shocks to the ecological balance, declining agricultural yields, declining incomes, further suppressing economic growth in the agricultural sector, which is generally followed by downstream sectors. The research was conducted in May - December 2020. The linear model postulate was applied at 5% and 10% significant levels with the response variable in the form of income from agricultural sector which was acquired from the BPS website, then optimization using Minitab. While the explanatory variables used are the proportion of land cover classes. Land and forest cover data were identified through the interpretation of Landsat satellite imagery downloaded from the NASA website using ArcGIS and Ecognition, then validation tests carried out with field exploration of the land cover class. The results of the research showed the relationship between the model equation R-Sq (adj) = 77.9% and the impact that occurred in each land cover class for each 1% decrease. Changes in the area of state forests, community forests, plantations, rice fields have an impact on decreasing the income of the agricultural sector respectively (IDR Million) 35.3; 16.7; 48.7; and 10.0. It suggest that conduct research on downstream economic activities such as inland fishery production, hydropower, industry, and trade.

Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT–2 SAR Images and CNNs

With the increasing availability of SAR imagery in recent years, more research is being conducted using deep learning (DL) for the classification of ice and open water; however, ice and open water classification using conventional DL methods such as convolutional neural networks (CNNs) is not yet accurate enough to replace manual analysis for operational ice chart mapping. Understanding the uncertainties associated with CNN model predictions can help to quantify errors and, therefore, guide efforts on potential enhancements using more–advanced DL models and/or synergistic approaches. This paper evaluates an approach for estimating the aleatoric uncertainty [a measure used to identify the noise inherent in data] of CNN probabilities to map ice and open water with a custom loss function applied to RADARSAT–2 HH and HV observations. The images were acquired during the 2014 ice season of Lake Erie and Lake Ontario, two of the five Laurentian Great Lakes of North America. Operational image analysis charts from the Canadian Ice Service (CIS), which are based on visual interpretation of SAR imagery, are used to provide training and testing labels for the CNN model and to evaluate the accuracy of the model predictions. Bathymetry, as a variable that has an impact on the ice regime of lakes, was also incorporated during model training in supplementary experiments. Adding aleatoric loss and bathymetry information improved the accuracy of mapping water and ice. Results are evaluated quantitatively (accuracy metrics) and qualitatively (visual comparisons). Ice and open water scores were improved in some sections of the lakes by using aleatoric loss and including bathymetry. In Lake Erie, the ice score was improved by ∼2 on average in the shallow near–shore zone as a result of better mapping of dark ice (low backscatter) in the western basin. As for Lake Ontario, the open water score was improved by ∼6 on average in the deepest profundal off–shore zone.

Detailed Cartography of Cotopaxi’s 1877 Primary Lahar Deposits Obtained by Drone-Imagery and Field Surveys in the Proximal Northern Drainage

Cotopaxi is an active volcano in Ecuador, whose eruptions are characterized by producing destructive primary lahars which represent a major risk for the country. The hazard assessment related to such lahars relies largely on the knowledge of the latest event, which occurred on 26 June 1877, for either scenario definition or simulation calibration. A detailed (1:5000 scale) cartography of the deposits belonging to that eruption has been obtained in the proximal northern drainage of Cotopaxi. The cartography was performed through a combination of geological fieldwork, as well as the analysis and interpretation of high-definition imagery obtained by drone surveys combined with the Structure from Motion technology for image processing. Such imagery included red and green visible bands, and a near-infrared band, which allowed the obtention of NDVI imagery where the primary lahar deposits were identified and cartographed with support of fieldwork data. Both data sources are mutually complementary, and the final cartography would be impossible if any of them were not available. The results obtained represent a significant advance for the level of detail with respect to previous cartographic works. Moreover, they should allow an improved calibration of the new generation of numerical models that simulate lahar flow for hazard assessment at Cotopaxi.

The formation and spatio-temporal variations of a new sand spit in Vaan Island, India—An influence of the multipurpose artificial reefs deployed to restore the sinking island

Sand spits constitute one of the depositional landforms that occur along the coast. This study (conducted from December 2015 to December 2020) investigated the spatio-temporal changes of the newly formed sand spit of Vaan Island in Gulf of Mannar, Southeast India. The investigation was based on morphological variations, bathymetry variations, wave climate, sediment nature and rate of deposition. Morphological variations were monitored using handheld Garmin GPS, and bathymetry variations were studied with low-cost bathymetry survey technique using Garmin echo-sounders. The freely available ECMWF wave data were used to obtain information on wave climate that approaches the study area. Satellite sun glitter imagery interpretation method was used to obtain information on wave direction around the Vaan Island for different time periods. Sand spit sediment characters and sedimentation rate were also estimated. The results indicate that the artificial reef modules, deployed on the windward side of the island for the purpose of restoring it from sinking, hinder the wave action, which has led to the formation of sand spit on the northwest by the long-shore sediment drift. Seasonal variations and morphological changes undergone by the sand spit are associated with the local wave climate. Bathymetry variation and shoreline configuration are also related. On shifting into deep contour the spit breaches into two or more pieces, whereas on shifting into shallow flat contour the area of exposure above sea level increases during the regular seasonal oscillation. The changes that this highly dynamic spit undergoes are cyclic, which indicates that the spit is undisturbed. Higher sediment supply from the nearby reef area will further increase the area of spit and island in future.

The Category of Imagery in the Study of Fine Arts in China in the Second Half of the 20th — Early 21st Century: Chinese Art Discourse

The article is devoted to the important problem of studying the imagery principles of fine art in China in the second half of the 20th — early 21st century. The focus is on the category of imagery. Today, this category is among the main ones within the research apparatus of Chinese art critics. Today the fine art of China has a very interesting art history ground; almost all the main vectors of Chinese art development need theoretical generalization. The categorical apparatus of research, which in many aspects differs from the conceptual and terminological discourse familiar to European scientists, is of great importance for the analysis of the development of Chinese art. Based on the analysis of a wide range of professional literature, which covers current research of the last decade (2012–2022), the authors of the article offer the following conclusions. Chinese scholars are actively using the traditional spectrum of art analysis: from the search for formal characteristics of works of art to the generalization of the system of imagery principles. In most cases, the category of imagery is interpreted as part of the axiological apparatus of art. This allows us to explain the essential features of the artistic idea of a work of art in relation to the properties of technical and compositional aspects. The main artistic elements that make up the content of the analytical apparatus include technical and technological, formal, compositional and plastic aspects of the analysis. They represent the “material” imagery nature of the work of art. According to research by Chinese scholars (including Fu Qiang, Jia Xiaodong, Zhang Chaohui, etc.), the system of means of expression is an integral part of the organization and expression of the artistic nature of art. At the same time, the interpretation of the artistic imagery of the work determines the “immaterial” (aesthetic and philosophical) nature of the evaluation of the work of art. It is often based on literary or religious reminiscences. In many studies, Chinese scholars have sought to show the direct relationship between two groups of artistic elements that generally make up the conceptual field of the imagery category.

Ground Validation of Seismic Line Forest Regeneration Assessments Based on Visual Interpretation of Satellite Imagery

Ground Validation of Seismic Line Forest Regeneration Assessments Based on Visual Interpretation of Satellite Imagery

Tracking Deforestation with Neural Networks

Forests are key to Earth’s biodiversity and the global ecosystem, hosting 80% of the planet’s plant biomass and two-thirds of all mammal species. However, they are also in decline; each year, around five million hectares of woodland are lost to logging and wildfires. Tracking this loss is essential to controlling and mitigating deforestation, but this requires careful interpretation of satellite imagery. Yiqiao Yin, Kieran Pichai, Benjamin Park and Aaron Park have developed a model to automatically identify forests from these images. Their new approach is already showing huge promise.

ТРАГІЗМ ЧИ ГЕРОЇЗМ? ПСИХОІНТЕРПРЕТАЦІЙНА МОДЕЛЬ ЛИЦАРЯ-КОЗАКА В УКРАЇНСЬКІЙ МУЗИЦІ ЯК НАЦІОНАЛЬНИЙ МЕТАКОНЦЕПТ

Ukrainian history and the current situation make us realize the importance of the influence on public opinion, and accordingly, its behavioural reactions, the means of informational and spiritual warfare. The Moscow narratives, the thinking clichеs created on their basis and the manipulative ideological influences caused by them are aimed at such goals. Therefore, it is extremely relevant to analyse and, perhaps, creatively revise some established models formed in Ukrainian art in general and music in particular, which in the context of this study we call «psychointerpretive», and among them ‒ a model formed on the basis of the interpretation of collective imagery, which reflects the archetype of the Cossack knight. The purpose of the article is to reveal the semantic features of the Cossack knight psycho-interpretive model in Ukrainian music as a national metaconcept and to substantiate its influence on the collective thinking of modern society. The research methodology is reflected in its following tasks: 1) justification of the «psychointerpretive model» concept; 2) examination of Cossack imagery «significant» examples in Ukrainian music; 3) ascertaining the possible impact of the specified psychointerpretive model as a national metaconcept on the collective thinking of modern society; 4) predicting the potential dynamics of the metaconcept. Scientific novelty. The «psychointerpretive model» concept introduced into scientific circulation, the meaning of which is explained in the theoretical-interpretive, musical-philosophical and psychological spheres in the article. Conclusion. The real nation’s life circumstances a best determine its psycho-emotional development, and we hope that Ukrainians, having passed through tragic trials, will restore the strength of the chivalrous and strong-willed element of their own psyche, while examples of modern Ukrainian chivalry heroism will become the basis for the potential dynamics of metaconcept. In this way, we will be able to influence the collective consciousness and national self-identification ‒ victim or winner, and therefore ‒ the behaviour of society through the world of artistic images, adjusting meaning-making at the national metaconcept level.

Integrated Workflow for Interpretation of Satellite Imageries Using Machine Learning to Predict Algal Bloom in Lake

Integrated Workflow for Interpretation of Satellite Imageries Using Machine Learning to Predict Algal Bloom in Lake

FADA: Feature Aligned Domain Adaptive Object Detection in Remote Sensing Imagery

Deep learning-based object detectors have been widely adopted in the field of remote sensing imagery interpretation. These detectors heavily depend on the expensive large-scale labeled datasets, while the scarce remote sensing datasets limit the performance. The domain adaptive object detection can alleviate this problem. However, it struggles with the confusing feature’s alignment, damaging the domain generalization performance, especially for the remote sensing scene with sparse objects and diverse backgrounds. For that reason, a semisynthetic data generator (SDG) is proposed to automatically generate the remote sensing dataset with low cost and replace the real-world training dataset, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature aligned domain adaptive object detector</i> (FADA) is proposed to enhance the domain adaptation among the cross-domain remote sensing images. The FADA contains two proposed modules in addition to the base detector: an adversarial-based foreground alignment (AFA) and a prototype-based confusing feature alignment (PCFA). The AFA aligns the cross-domain foreground feature by adversarial training (AT), and it can filter the noisy background feature that is not suitable to transfer. Then, the PCFA adaptively aligns the confusing background and foreground feature, further promoting the domain adaptation performance. Comprehensive experiments validate the effectiveness of the proposed method. Compared with the baseline model trained on the semisynthetic source dataset, our FADA improves the generalized performance on the real-world target dataset a large-scale Dataset for Object deTection in Aerial images (DOTA) by 15.7% average precision (AP) and achieves state-of-the-art results.

近40年青藏高原生态格局演变及其驱动因素

PDF HTML阅读 XML下载 导出引用 引用提醒 近40年青藏高原生态格局演变及其驱动因素 DOI: 10.5846/stxb202204191060 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 第二次青藏高原综合科学考察研究项目资助（2019QZKK0307）；北京市校际大学生创新创业项目（202198077） Evolution of ecological patterns and its driving factors on Qinghai-Tibet Plateau over the past 40 years Author: Affiliation: Fund Project: Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0307)、Beijing College Students' Innovation and Entrepreneurship Project（202198077） 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:20世纪80年代至今近40年间，青藏高原自然与人工生态系统发生了广泛而深刻的变化，作为我国重要的生态屏障，亟需对其生态系统格局演变过程及其驱动因素进行系统定量的解析。本研究利用1980年至2018年间8期遥感解译土地利用与覆被数据，将青藏高原9类主要生态系统类型，森林、灌丛、草地、农田、城镇、水体与湿地、冰川、裸地、荒漠，依其主要构成组分，划分为以植被为主体的自然生态系统（森林、灌丛、草地）、以无机环境为主体的自然生态系统（水体与湿地、冰川、裸地、荒漠），以及人工生态系统（农田、城镇）共三大类。统计分析表明1980至2018年的近40年来，青藏高原以植被为主的自然生态系统面积约占61.9%，其中草地生态系统变化率较大，局部年际变化逾30%/10a，草地灌丛面积扩张明显，最高可达约7%/10a。此外，青藏高原喜马拉雅山脉附近的冰川消减较快，下降速率约达25%/10a。青藏高原东缘向西城镇扩张明显，城镇面积占比增加约40%。研究还对气温和降水计算其变化速率，量化驱动生态系统演变的外部气候环境的时空动态特征，结合地理环境变量、人类活动强度、土壤侵蚀度、生物丰度等综合的驱动因素指标，建立多层级结构方程模型。研究发现，以植被为主的自然生态系统变化速率与气温、降水的变化速率呈现显著负相关，以无机环境要素为主的自然生态系统与气候因子的变化速率呈现显著正相关，人工生态系统则与外部环境因素耦合关系不强，结果表明青藏高原森林、灌丛、草地一类自然植被生态系统与环境变化之间呈现负反馈的保守性耦合关系，相比较水体与湿地、冰川、裸地和荒漠生态系统来讲，具有更强的韧性，因此保护区域自然植被将有利于维护青藏高原整体的生态屏障功能。 Abstract:From the 1980s to the present, the natural and artificial ecosystems of the Qinghai-Tibet Plateau have undergone extensive and profound changes. As an important ecological barrier area in China, it is essential to systematically and quantitatively analyze the evolution of the ecosystem patterns and its driving factors. In this study, 8-periods datasets from 1980 to 2018 of the land use and cover change based on the satellite remote sensing imagery interpretation were used to reclassify 9 major ecosystem types on the Qinghai-Tibet Plateau, including forest, shrub, grassland, agricultural land, urban, waters and wetland, glacier, bare land, and desert. According to their main components, they were divided into the natural ecosystems dominated by vegetation (forest, shrub, and grassland), and the natural ecosystems dominated by inorganic environment (waters and wetland, glacier, bare land, and desert), and there was another type of artificial ecosystems (agricultural land, and urban). The statistical results showed that from 1980 to 2018, the area of natural ecosystems dominated by vegetation accounted for about 61.9% of the Qinghai-Tibet Plateau. Among the natural vegetation, the grassland has fluctuated largely with the changing rate exceeding 30%/10a. The shrub expanded significantly and raised up approximately 7%/10a. The area of glacier near the Himalayas Mountains have been declining rapidly, with a shrinking rate of approximately 25%/10a. On the eastern edge of the Plateau, the urban has expanded to the west significantly, as the proportion of urban areas has increased by about 40%. We calculated the changing rate of the temperature and precipitation to quantify the spatio-temporal characteristics of the external climatic environment that drove the evolution of the ecosystems. Combined with the other comprehensive driving factors including geographical factors, human activity intensity index, soil erosion degree, and the biological abundance, we established a multi-level structural equation model. The results indicated that the changes of the natural ecosystems dominated by vegetation was significantly negatively correlated with the changing rate of temperature and precipitation, while the changes of natural ecosystems dominated by inorganic environment was significantly positively correlated with the changing rate of the climatic factors. The coupling relationship between the artificial ecosystem and external environmental factors was weak. The results of the negative feedbacks suggested a conservative coupling relationship between the vegetational natural ecosystems, forest, shrub, and grassland, and the external environmental changes, which had higher resilience than the non-vegetation ecosystems, like glacier, waters and wetland, bare land and desert. Therefore, protecting the natural vegetation in the area means to maintain the overall ecological barrier function of the Qinghai-Tibet Plateau. 参考文献 相似文献 引证文献

Identification of Domain Complexes in the Danube Delta Coastal Belt Using the Biophysical Cross-shore Classification System (BCCS), Based on Interpretation of Satellite Imagery

Finkl, C.W. and Makowski, C., 2022. Identification of domain complexes in the Danube Delta Coastal Belt using the Biophysical Cross-shore Classification System (BCCS), based on interpretation of satellite imagery. Journal of Coastal Research, 38(1), 1–18. Coconut Creek (Florida), ISSN 0749-0208. The Biophysical Cross-shore Classification System (BCCS) was applied to the Danube Delta on the NW margin of the Black Sea to ascertain whether this method of classification was applicable to extensive low-lying and low-relief wetland coastalscapes. For organizational purposes, the coastal belt study area was broken into four previously recognized delta lobes (Kilia, Sulina, St. George II, and Cosna–Sinoie) and the Chituc Formation (beach ridge plain) because these provided distinctive coastal segments that were manageable for cross-shore classification. The village of Prymors'ke in the Lyman municipality of Ukraine and the onshore Chituc Formation beach ridge strandplain on the southern margin of the delta marked the northern- and southernmost extents of the Danubian classification effort, respectively. Codified cross-shore catenary sequences were based on the occurrence of recurring archetypes (i.e. Barrier, Beach, Beach Ridge, Delta, Developed, Lagoon, and Wetland) that had alongshore extent and could be identified as domains. Sub archetypes were also interpreted and added to the cross-shore sequences to further refine the Danube Delta classification. Because long cross-shore catenary sequences included inland biophysical and anthropogenic units that often differed dramatically from immediate onshore features, the conflated catenas were bifurcated into onshore (seaward) and inland (landward) domains, sections, or branches that were collectively referred to as domain complexes. Extension of cross-shore transects up to 20 km inland to incorporate all salient aspects of the delta provided useful subdivision of delta landscapes. Sixteen cross-shore transects were used to derive 12 domain complexes extending 167.5 km along the Danubian coastal belt that reached from the approximate latitudes of Prymors'ke, Ukraine, in the north to about Corbu in Romania to the south. Classification of the Danube Delta coastal belts showed that it is possible to apply the BCCS to large-area delta regions using extended cross-shore transects with bifurcated catenary sequences that together describe seaward (onshore) and landward (inland) sections of the delta in the form of domain complexes.

The Use of Machine Learning for Accessing Landslide Susceptibility Class: Study Case of Kecamatan Pacet, Kabupaten Mojokerto

Kecamatan Pacet, Kabupaten Mojokerto is one of an area with many landslide events in East Java Province. As a mitigation effort, this research aimed to map the landslide susceptibility class distribution of the research area. This research applied a machine learning analysis technic which combined Frequency Ratio (FR) and Logistic Regression (LR) models to assess the landslide susceptibility class distribution. FR bivariate analysis is used to normalized the data and to identify the influence significancy on each class of triggering factors. LR multivariate analysis is applied to generate the landslide probability (susceptibility) and to show the influence significancy of each triggering factor to landslide events. There are 12 triggering factors to landslide used in this research, which is: TPI, TWI, SPI, slope, aspect, elevation, profile curvature, distance to drainage, geological unit, rainfall, land use, and distance to the road. This research has 383 landslides and 383 non-landslide events as the data sample based on field survey, BPBD Kabupaten Mojokerto, and Google Earth Pro imagery interpretation. The proportion of dataset training and testing is 70% and 30%, which generated from the data inventory. This research used ROC analysis to validate the landslide susceptibility model. The result showed that the landslide susceptibility model has an AUC value of 0.91, which indicated that the model has high accuracy.

National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine

Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016–2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. The entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on Collect-Earth-Online, an online platform for systematically collecting geospatial data. The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. When compared to Morocco’s utilized agricultural land (SAU) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national SAU areas with an R-value of 0.9. Furthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019–2020 season by 2% since the 2018–2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 (COVID-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. Such a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture.

Analysis on the Evolution of Rural Settlement Pattern and Its Influencing Factors in China from 1995 to 2015

Since the early 1990s, China has experienced rapid industrialization and urbanization. As cities have expanded rapidly, the spatial patterns of rural settlements also changed significantly. This study uses land use data from satellite imagery interpretation, socioeconomic statistics, and field survey data, together with techniques including landscape pattern analysis, kernel density estimation, and spatial measurement models, to analyze the evolving spatial patterns of rural settlements influencing factors in China from 1995 to 2015. The results indicate the following: First, China’s rural settlements experienced significant changes in the period 1995–2015, as 88.92% of Prefectural-level administrative district units saw an increase in rural settlement area, with total settlement size increasing by 1.35 million hectares, and settlement area sprawl index values can be summarized as “high in the west and low in the east”. Second, in the two-decade study period, the population agglomeration capacity of rural settlements in China continuously weakened, and the shape and structure of rural settlement became more complex and irregular. The scale and scope of the disappearance of rural settlement areas in the northeast and southeast regions was relatively drastic, and the kernel density value of settlements dropped significantly. Third, the increase in rural settlement land area is concentrated in low-altitude and low-slope areas, with a significant tendency to be near water and roads. Fourth, social and economic factors, such as per capita net income of rural residents, the proportion of the population employed in agriculture, the size and structure of the permanent rural population, local fiscal revenue, and urbanization level, are the main factors that cause changes of rural settlement patterns. The results of this study can serve as a reference for promoting regional rural sustainable development policies and advancing rural spatial governance and comprehensive revitalization.

Abstracts from the Spectra 2018 Symposium Part 2: Visualization

Abstracts from the Spectra 2018 Symposium Part 2: <i>Visualization</i>