scholarly journals EXTRACTION AND ANALYSIS OF MAJOR AUTUMN CROPS IN JINGXIAN COUNTY BASED ON MULTI - TEMPORAL GF - 1 REMOTE SENSING IMAGE AND OBJECT-ORIENTED

2018 ◽  
Vol XLII-3 ◽  
pp. 1473-1480
Author(s):  
B. Ren ◽  
Q. Wen ◽  
H. Zhou ◽  
F. Guan ◽  
L. Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Extraction ◽  
Spectral Characteristics ◽  
Object Oriented ◽  
Growth Stages ◽  
Phenological Data ◽  
Crop Monitoring ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Crop Planting

The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.

scholarly journals Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

2022 ◽  
Vol 14 (2) ◽  
pp. 273
Author(s):  
Mengyao Li ◽  
Rui Zhang ◽  
Hongxia Luo ◽  
Songwei Gu ◽  
Zili Qin
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Object Oriented ◽  
Growth Period ◽  
Sanjiang Plain ◽  
Google Earth ◽  
Utilization Rate ◽  
Growth Stages ◽  
The Sanjiang Plain ◽  
Growth Method

In recent years, the scale of rural land transfer has gradually expanded, and the phenomenon of non-grain-oriented cultivated land has emerged. Obtaining crop planting information is of the utmost importance to guaranteeing national food security; however, the acquisition of the spatial distribution of crops in large-scale areas often has the disadvantages of excessive calculation and low accuracy. Therefore, the IO-Growth method, which takes the growth stage every 10 days as the index and combines the spectral features of crops to refine the effective interval of conventional wavebands for object-oriented classification, was proposed. The results were as follows: (1) the IO-Growth method obtained classification results with an overall accuracy and F1 score of 0.92, and both values increased by 6.98% compared to the method applied without growth stages; (2) the IO-Growth method reduced 288 features to only 5 features, namely Sentinel-2: Red Edge1, normalized difference vegetation index, Red, short-wave infrared2, and Aerosols, on the 261st to 270th days, which greatly improved the utilization rate of the wavebands; (3) the rise of geographic data processing platforms makes it simple to complete computations with massive data in a short time. The results showed that the IO-Growth method is suitable for large-scale vegetation mapping.

scholarly journals Otobur in Action: Processing of various biodiversity data for botanical gardens; flowering calendar, herbarium digitization, propagation trials and scientific plant names

2019 ◽  
Vol 3 ◽  
Author(s):  
Rasim Aydınkal ◽  
Salih Kanoğlu
Keyword(s):  
Large Scale ◽  
Data Management System ◽  
Data Types ◽  
Phenological Data ◽  
Additional Information ◽  
Germination Success ◽  
High Resolution Images ◽  
Recorded Data ◽  
Annual Estimate ◽  
Biodiversity Information

Over the past decade, great improvements have occurred in the field of biodiversity information technology. Data types such as geographic and phenological (e.g., blooming) characteristics of different specimens, which are used for the analysis of environmental issues, are steadily increasing on a large scale. Most herbaria and botanic gardens are involved in the digital compilations of such kinds of data to be able to transform them into meaningful results that can be used to tackle environmental problems (Leadlay and Greene 1998). These are usually in the form of high resolution images, along with tables displaying additional information about specimens, which are accessible over the internet. This study, will describe how we made an annual estimate of phenological data, constructing a flowering calendar of plants (Fig. 1) in the Nezahat Gökyiğit Botanik Bahçesi (NGBB), using Otobur (Loizeau et al. 2018). Otobur is a data management system developed under NGBB in Istanbul, which is accessible at https://www.otobur.org.tr. In addition to this, we also analyze our recorded data on the ongoing propagation effort of seedlings, in order to analyze and compare their prior germination success and mortality ratios (Fig. 2). This enables us to improve our procedures, and to find the most suitable techniques to apply in the most accurate propagation trials.

scholarly journals Multi-Temporal SAR Data Large-Scale Crop Mapping Based on U-Net Model

2019 ◽  
Vol 11 (1) ◽  
pp. 68 ◽  
Author(s):  
Sisi Wei ◽  
Hong Zhang ◽  
Chao Wang ◽  
Yuanyuan Wang ◽  
Lu Xu
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Mapping Method ◽  
Support Vector ◽  
Smallholder Farming ◽  
Convolutional Network ◽  
Multi Temporal ◽  
Crop Mapping ◽  
Sar Data ◽  
Crop Planting

Due to the unique advantages of microwave detection, such as its low restriction from the atmosphere and its capability to obtain structural information about ground targets, synthetic aperture radar (SAR) is increasingly used in agricultural observations. However, while SAR data has shown great potential for large-scale crop mapping, there have been few studies on the use of SAR images for large-scale multispecies crop classification at present. In this paper, a large-scale crop mapping method using multi-temporal dual-polarization SAR data was proposed. To reduce multi-temporal SAR data redundancy, a multi-temporal images optimization method based on analysis of variance (ANOVA) and Jeffries–Matusita (J–M) distance was applied to the time series of images after preprocessing to select the optimal images. Facing the challenges from smallholder farming modes, which caused the complex crop planting patterns in the study area, U-Net, an improved fully convolutional network (FCN), was used to predict the different crop types. In addition, the batch normalization (BN) algorithm was introduced to the U-Net model to solve the problem of a large number of crops and unbalanced sample numbers, which had greatly improved the efficiency of network training. Finally, we conducted experiments using multi-temporal Sentinel-1 data from Fuyu City, Jilin Province, China in 2017, and we obtained crop mapping results with an overall accuracy of 85% as well as a Kappa coefficient of 0.82. Compared with the traditional machine learning methods (e.g., random forest (RF) and support vector machine (SVM)), the proposed method can still achieve better classification performance under the condition of a complex crop planting structure.

An object-oriented optimization framework for large-scale inverse problems

2021 ◽  
pp. 104790
Author(s):  
Ettore Biondi ◽  
Guillaume Barnier ◽  
Robert G. Clapp ◽  
Francesco Picetti ◽  
Stuart Farris
Keyword(s):  
Inverse Problems ◽  
Large Scale ◽  
Object Oriented ◽  
Optimization Framework

scholarly journals Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

2020 ◽  
Vol 12 (7) ◽  
pp. 1144
Author(s):  
Rosa Aguilar ◽  
Monika Kuffer
Keyword(s):  
High Resolution ◽  
Open Space ◽  
Local Planning ◽  
Open Spaces ◽  
High Resolution Imagery ◽  
Coarse Spatial Resolution ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Cloud Computation

Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

Carbon Footprint Assessment of a Large-Scale Pig Production System in Northern China: A Case Study

2018 ◽  
Vol 61 (3) ◽  
pp. 1121-1131 ◽  
Author(s):  
Yuanqing Zhou ◽  
Hongmin Dong ◽  
Hongwei Xin ◽  
Zhiping Zhu ◽  
Wenqiang Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Ghg Emissions ◽  
Northern China ◽  
Manure Management ◽  
Traditional Practice ◽  
Enteric Fermentation ◽  
Pig Production ◽  
Transport Distance ◽  
Feed Production ◽  
Crop Planting

Abstract. China raises 50% of global live pigs. However, few studies on the carbon footprint (CF) of large-scale pig production based on China’s actual production conditions have been carried out. In this study, life cycle assessment (LCA) and actual production data of a typical large-scale pig farm in northern China were used to assess the greenhouse gas (GHG) emissions or CF associated with the whole process of pig production, including feed production (crop planting, feed processing, and transportation), enteric fermentation, manure management, and energy consumption. The results showed a CF of 3.39 kg CO2-eq per kg of live market pig and relative contributions of 55%, 28%, 13%, and 4% to the total CF by feed production, manure management, farm energy consumption, and enteric fermentation, respectively. Crop planting accounted for 66% of the feed production CF, while feed processing and transportation accounted for the remaining 34%. Long-distance transport of semi-raw feed materials caused by planting-feeding separation and over-fertilization in feed crop planting were two main reasons for the largest contribution of GHG emissions from feed production to the total CF. The CF from nitrogen fertilizer application accounted for 33% to 44% of crop planting and contributed to 16% of the total CF. The CF from the transport of feed ingredients accounted for 17% of the total CF. If the amount of nitrogen fertilizer used for producing the main feed ingredients is reduced from 209 kg hm-2 (for corn) and 216 kg hm-2 (for wheat) to 140 kg hm-2 (corn) and 180 kg hm-2 (wheat), the total CF would be reduced by 7%. If the transport distance for feed materials decreased from 325 to 493 km to 30 km, along with reducing the number of empty vehicles for transport, the total CF would be reduced by 18%. The combined CF mitigation potential for over-fertilization and transport distance is 26%. In addition, the use of pit storage, anaerobic digestion, and lagoon for manure management can reduce GHG emissions from manure management by 76% as compared to the traditional practice of pit storage and lagoon. This case study reveals the impact of planting-feeding separation and over-fertilization on the CF of the pig supply chain in China. The manure management practice of pit storage, anaerobic digestion, and lagoon is much more conductive to reducing the CF as compared to the traditional practice of pit storage and lagoon. Keywords: Greenhouse gas, Life cycle assessment, Mitigation, Pig.

scholarly journals Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2564
Author(s):  
Mauro Martini ◽  
Vittorio Mazzia ◽  
Aleem Khaliq ◽  
Marcello Chiaberge
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Domain Adaptation ◽  
Learning Approaches ◽  
Atmospheric Conditions ◽  
Practical Applications ◽  
Area Of Interest ◽  
Multi Temporal ◽  
Significant Performance ◽  
Adversarial Training

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

A Novel Real-Time Thermal Analysis and Layer Time Control Framework for Large Scale Additive Manufacturing

2020 ◽  
Author(s):  
Sepehr Fathizadan ◽  
Feng Ju ◽  
Kyle Rowe ◽  
Alex Fiechter ◽  
Nils Hofmann
Keyword(s):  
Additive Manufacturing ◽  
Surface Temperature ◽  
Real Time ◽  
Large Scale ◽  
Production Efficiency ◽  
Control Method ◽  
Data Extraction ◽  
Critical Role ◽  
Imaging Data ◽  
Time Data

Abstract Production efficiency and product quality need to be addressed simultaneously to ensure the reliability of large scale additive manufacturing. Specifically, print surface temperature plays a critical role in determining the quality characteristics of the product. Moreover, heat transfer via conduction as a result of spatial correlation between locations on the surface of large and complex geometries necessitates the employment of more robust methodologies to extract and monitor the data. In this paper, we propose a framework for real-time data extraction from thermal images as well as a novel method for controlling layer time during the printing process. A FLIR™ thermal camera captures and stores the stream of images from the print surface temperature while the Thermwood Large Scale Additive Manufacturing (LSAM™) machine is printing components. A set of digital image processing tasks were performed to extract the thermal data. Separate regression models based on real-time thermal imaging data are built on each location on the surface to predict the associated temperatures. Subsequently, a control method is proposed to find the best time for printing the next layer given the predictions. Finally, several scenarios based on the cooling dynamics of surface structure were defined and analyzed, and the results were compared to the current fixed layer time policy. It was concluded that the proposed method can significantly increase the efficiency by reducing the overall printing time while preserving the quality.

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