Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018

Agricultural greenhouse (AG), one of the fastest-growing technology-based approaches worldwide in terms of controlling the environmental conditions of crops, plays an essential role in food production, resource conservation and the rural economy, but has also caused environmental and socio-economic problems due to policy promotion and market demand. Therefore, long-term monitoring of AG is of utmost importance for the sustainable management of protected agriculture, and previous efforts have verified the effectiveness of remote sensing-based techniques for mono-temporal AG mapping in a relatively small area. However, currently, a continuous annual AG remote sensing-based dataset at large-scale is generally unavailable. In this study, an annual AG mapping method oriented to the provincial area and long-term period was developed to produce the first Landsat-derived annual AG dataset in Shandong province, China from 1989 to 2018 on the Google Earth Engine (GEE) platform. The mapping window for each year was selected based on the vegetation growth and the phenological information, which was critical in distinguishing AG from other misclassified categories. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement. Finally, the average User’s Accuracy, Producer’s Accuracy and F1-score of AG based on visually-interpreted samples over 30 years reached 96.56%, 86.64% and 0.911, respectively. Furthermore, we also found that the ranked features via calculating the importance of each tested feature resulted in the highest accuracy and the strongest stability in the initial classification stage, and the proposed temporal consistency correction algorithm improved the final products by approximately five percent on average. In general, the resultant AG sequence dataset from our study has revealed the expansion of this typical object of “Human–Nature” interaction in agriculture and has a potential application in use of greenhouse-related technology and the scientific planning of protected agriculture.

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Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽

10.3390/land10040433 ◽

2021 ◽

Vol 10 (4) ◽

pp. 433

Author(s):

Xiaolan Huang ◽

Weicheng Wu ◽

Tingting Shen ◽

Lifeng Xie ◽

Yaozu Qin ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Forest Canopy ◽

Canopy Cover ◽

Google Earth ◽

Tree Canopy ◽

Landsat Data ◽

High Resolution Data ◽

Modis Ndvi ◽

Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

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Remote Sensing Is Changing Our View of the Coast: Insights from 40 Years of Monitoring at Narrabeen-Collaroy, Australia

Remote Sensing ◽

10.3390/rs10111744 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1744 ◽

Cited By ~ 28

Author(s):

Kristen Splinter ◽

Mitchell Harley ◽

Ian Turner

Keyword(s):

Remote Sensing ◽

Data Collection ◽

Temporal Resolution ◽

Monitoring Program ◽

Google Earth ◽

Airborne Lidar ◽

Data Collection Program ◽

Insight Into ◽

Collection Program

Narrabeen-Collaroy Beach, located on the Northern Beaches of Sydney along the Pacific coast of southeast Australia, is one of the longest continuously monitored beaches in the world. This paper provides an overview of the evolution and international scientific impact of this long-term beach monitoring program, from its humble beginnings over 40 years ago using the rod and tape measure Emery field survey method; to today, where the application of remote sensing data collection including drones, satellites and crowd-sourced smartphone images, are now core aspects of this continuing and much expanded monitoring effort. Commenced in 1976, surveying at this beach for the first 30 years focused on in-situ methods, whereby the growing database of monthly beach profile surveys informed the coastal science community about fundamental processes such as beach state evolution and the role of cross-shore and alongshore sediment transport in embayment morphodynamics. In the mid-2000s, continuous (hourly) video-based monitoring was the first application of routine remote sensing at the site, providing much greater spatial and temporal resolution over the traditional monthly surveys. This implementation of video as the first of a now rapidly expanding range of remote sensing tools and techniques also facilitated much wider access by the international research community to the continuing data collection program at Narrabeen-Collaroy. In the past decade the video-based data streams have formed the basis of deeper understanding into storm to multi-year response of the shoreline to changing wave conditions and also contributed to progress in the understanding of estuary entrance dynamics. More recently, ‘opportunistic’ remote sensing platforms such as surf cameras and smartphones have also been used for image-based shoreline data collection. Commencing in 2011, a significant new focus for the Narrabeen-Collaroy monitoring program shifted to include airborne lidar (and later Unmanned Aerial Vehicles (UAVs)), in an enhanced effort to quantify the morphological impacts of individual storm events, understand key drivers of erosion, and the placing of these observations within their broader regional context. A fixed continuous scanning lidar installed in 2014 again improved the spatial and temporal resolution of the remote-sensed data collection, providing new insight into swash dynamics and the often-overlooked processes of post-storm beach recovery. The use of satellite data that is now readily available to all coastal researchers via Google Earth Engine continues to expand the routine data collection program and provide key insight into multi-decadal shoreline variability. As new and expanding remote sensing technologies continue to emerge, a key lesson from the long-term monitoring at Narrabeen-Collaroy is the importance of a regular re-evaluation of what data is most needed to progress the science.

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Changes in Mesopotamian Wetlands: Investigations Using Diverse Remote Sensing Datasets

10.21203/rs.3.rs-165486/v1 ◽

2021 ◽

Author(s):

Ali K. M. Al-Nasrawi ◽

Ignacio Fuentes ◽

Dhahi Al-Shammari

Keyword(s):

Remote Sensing ◽

Drainage System ◽

Meteorological Drought ◽

Significant Loss ◽

Google Earth ◽

Social Communities ◽

Term Change ◽

Supported Living ◽

Mesopotamian Marshes

Abstract Early civilizations have inhabited stable-water-resourced areas that supported living needs and activities, including agriculture. The Mesopotamian marshes, recognised as the most ancient human-inhabited area (~6000 years ago) and refuge of rich biodiversity, have experienced dramatic changes during the past five decades, starting to fail in providing adequate environmental functioning and support of social communities as they used to for thousands of years. The aim of this study is to observe, analyse and report the extent of changes in these marshes from 1972 to 2020. Data from various remote sensing sources were acquired through Google Earth Engine (GEE) including climate variables, land cover, surface reflectance, and surface water occurrence collections. Results show a clear wetlands dynamism over time and a significant loss in marshlands extent, even though no significant long-term change was observed in lumped rainfall from 1982, and even during periods where no meteorological drought had been recorded. Human interventions have disturbed the ecosystems, which is evident when studying water occurrence changes. These show that the diversion of rivers and the building of a new drainage system caused the migration and spatiotemporal changes of marshlands. Nonetheless, restoration plans (after 2003) and strong wet conditions (period 2018 - 2020) have helped to recover the ecosystems, these have not led the marshlands to regain their former extent. Further studies should pay more attention to the drainage network within the study area as well as the neighboring regions and their impact on the streamflow that feeds the study area.

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GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6758 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11037-11044

Author(s):

Lianghua Huang ◽

Xin Zhao ◽

Kaiqi Huang

Keyword(s):

Online Learning ◽

Success Rate ◽

Large Scale ◽

State Of The Art ◽

Temporal Consistency ◽

Post Processing ◽

Two Stage ◽

Multi Scale ◽

Instance Search

A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area.

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The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/886/1/012100 ◽

2021 ◽

Vol 886 (1) ◽

pp. 012100

Author(s):

Munajat Nursaputra ◽

Siti Halimah Larekeng ◽

Nasri ◽

Andi Siady Hamzah

Keyword(s):

Remote Sensing ◽

Time Series ◽

Large Scale ◽

Vegetation Index ◽

Forest Degradation ◽

Google Earth ◽

Aerial Images ◽

Forest Monitoring ◽

Forest Density ◽

Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

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Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images

Remote Sensing ◽

10.3390/rs13193842 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3842

Author(s):

Yaxin Ding ◽

Xiaomei Yang ◽

Hailiang Jin ◽

Zhihua Wang ◽

Yueming Liu ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Large Scale ◽

Dynamic Change ◽

Google Earth ◽

Tidal Range ◽

Remote Sensing Images ◽

Data Platform ◽

Range Observation ◽

Change Monitoring

The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring.

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Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

Sustainability ◽

10.3390/su122410274 ◽

2020 ◽

Vol 12 (24) ◽

pp. 10274

Author(s):

Wu Xiao ◽

Wenqi Chen ◽

Tingting He ◽

Linlin Ruan ◽

Jiwang Guo

Keyword(s):

Remote Sensing ◽

Total Nitrogen ◽

Model Building ◽

Google Earth ◽

Shandong Province ◽

Coefficient Of Determination ◽

Soil Productivity ◽

Soil Total Nitrogen ◽

Wide Range ◽

Google Earth Engine

Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

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Understanding Long-Term Changes of Coastal Saltmarshes from Satellite Remote Sensing

10.5194/egusphere-egu2020-2570 ◽

2020 ◽

Author(s):

Marieke Laengner ◽

Daphne van der Wal

Keyword(s):

Satellite Remote Sensing ◽

Large Scale ◽

Open Water ◽

Google Earth ◽

Environmental Drivers ◽

Decision Tree Classification ◽

Tidal Forces ◽

Time Period ◽

Long Time

<p>Saltmarshes are known to be very important coastal ecosystems. They provide crucial functions for flora and fauna, as well as valuable ecosystem services for humankind. Many methods that are used to investigate these ecosystems are limited in space and time. Long time series of global satellite data enable to observe changes in the extent of saltmarshes on a large scale and over a long time period. We developed an unsupervised decision tree classification method in Google Earth Engine that automatically classifies satellite images into saltmarsh vegetation, mudflats, and open water. We applied the method using Landsat 5 TM data between 1985 and 2011. With this, we are able to detect trends in the seaward extent of saltmarshes globally. We reveal transitions between saltmarsh, mudflat and open water. Furthermore, we put saltmarsh habitat changes in a spatial context and couple trends in saltmarsh dynamics to environmental drivers, such as sea level rise, tidal forces, waves, and sediment availability.</p>

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Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region Using Landsat Imagery and the Google Earth Engine

Remote Sensing ◽

10.3390/rs12010055 ◽

2019 ◽

Vol 12 (1) ◽

pp. 55 ◽

Cited By ~ 2

Author(s):

Cong Ou ◽

Jianyu Yang ◽

Zhenrong Du ◽

Yiming Liu ◽

Quanlong Feng ◽

...

Keyword(s):

Remote Sensing ◽

Landsat Imagery ◽

Google Earth ◽

Spatiotemporal Dynamics ◽

Landscape Patterns ◽

Agricultural Region ◽

Multi Temporal ◽

Google Earth Engine ◽

Production Approach

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon’s entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies.

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Challenges in implementing industry revolution 4.0 in INDIAN manufacturing SMES: insights from five case studies

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.4.13024 ◽

2018 ◽

Vol 7 (2.4) ◽

pp. 136 ◽

Cited By ~ 1

Author(s):

Suresh N ◽

Hemamala K ◽

Ashok N

Keyword(s):

Industry 4.0 ◽

Large Scale ◽

Manufacturing Firms ◽

Advanced Technology ◽

Batch Size ◽

Medium Size ◽

Small Scale ◽

Market Demand ◽

Manufacturing Enterprises

Purpose-This research examines Small and Medium manufacturing Enterprises (SME’s) awareness, current capability, willingness and ability to identify the challenges involved in implementing Industry 4.0(I 4.0) at their premises.Design/methodology/approach-A set of questionnaire was framed to collect qualitative and quantitative data from five manufacturing SME’s and they were analyzed to gain insight.Findings –3 out of 5 manufacturing SME’s are aware, capable, willing and have ability to identify the challenges for implementing Industry 4.0 at their premises. The study also found that implementation of I 4.0 depends on size of the firm. Medium size manufacturing firms had started investing in Information Technology but small scale industries is still struggling to figure out their long term benefit.Practical implications – The advancement and the integration of the technologies such as Cyber Physical system, Internet Of Things, Artificial intelligence, Big data, Cloud computing and 3D printing provides greater flexibility to the manufacturing firms. In today’s global competition with a huge demand for personalized products at low price with best quality, innovation and capability to full filling batch size of one is becoming important. Hence, to meet the market demand many large-scale industries started investing in advanced technology where SME has yet to pay attention.Originality/value –Paper indicates the Indian manufacturing SME’s preparedness for Industry 4.0. It contains five cases capturing the current manufacturing practices followed in the SME’s and their capability towards implementing Industry I 4.0 in Indian environment.

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