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

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.

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Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

Remote Sensing ◽

10.3390/rs12183038 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3038

Author(s):

Dhahi Al-Shammari ◽

Ignacio Fuentes ◽

Brett M. Whelan ◽

Patrick Filippi ◽

Thomas F. A. Bishop

Keyword(s):

Time Series ◽

Vegetation Index ◽

Landsat Imagery ◽

Google Earth ◽

Growth Stages ◽

Landsat 8 ◽

Crop Type ◽

Eastern Australia ◽

Normalised Difference Vegetation Index ◽

Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

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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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An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

Transactions of the ASABE ◽

10.13031/trans.13661 ◽

2020 ◽

Vol 63 (4) ◽

pp. 1133-1146

Author(s):

Beichen Lyu ◽

Stuart D. Smith ◽

Yexiang Xue ◽

Katy M. Rainey ◽

Keith Cherkauer

Keyword(s):

Data Processing ◽

High Throughput ◽

Large Scale ◽

Vegetation Index ◽

Vegetation Indices ◽

Unmanned Aerial Systems ◽

Growth Stages ◽

Processing Pipeline ◽

High Throughput Phenotyping ◽

Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1473-2018 ◽

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&thinsp;%, the producer accuracy was 96&thinsp;% and 94&thinsp;% respectively, and the user precision was 95.05&thinsp;% and 95.9&thinsp;% 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.

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Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Remote Sensing ◽

10.3390/rs14020284 ◽

2022 ◽

Vol 14 (2) ◽

pp. 284

Author(s):

Changchun Li ◽

Weinan Chen ◽

Yilin Wang ◽

Yu Wang ◽

Chunyan Ma ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growth Period ◽

Google Earth ◽

Henan Province ◽

Sar Images ◽

Modis Ndvi ◽

Optical Images ◽

Google Earth Engine

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

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Impact of mire reclamation on export potential and characteristics of dissolved carbons in the Sanjiang Plain, Northeast China

Biogeosciences Discussions ◽

10.5194/bgd-9-5347-2012 ◽

2012 ◽

Vol 9 (5) ◽

pp. 5347-5371 ◽

Cited By ~ 1

Author(s):

Y. D. Guo ◽

C. C. Song ◽

Y. Z. Lu ◽

Y. Y. Song ◽

Z. M. Wan

Keyword(s):

Northeast China ◽

Large Scale ◽

Hydrological Regime ◽

Sanjiang Plain ◽

Drainage Ditches ◽

The Sanjiang Plain ◽

Dissolved Carbon ◽

Average Value ◽

Export Dynamics ◽

Hydrophobic Fraction

Abstract. As an important dissolved organic carbon (DOC) reservoir, the mires in the Sanjiang Plain, Northeast China, have been suffering from large scale of reclamation, and thus elevated loss and degradation since the 1960s. This study compares the export dynamics of the dissolved carbons, as well as the chemical characteristics of DOC, in the natural mire, degraded mire and drainage ditches during the growing seasons from 2008 to 2010 with the aim to clarify the final effects of the longterm reclamation on the export dynamics of the dissolved carbons. Results show that the average concentrations of total dissolved carbon (TC) and DOC are much higher in natural mires than that in degraded mire and drainage ditches. The DOC concentration for natural mires, about 35.53 ± 5.15 mg l−1 on average, is nearly 2.39 times of that in degraded mire (14.84 ± 4.21 mg l−1) and 2.77 times of the average value in ditches (12.84 ± 4.49 mg l−1). Similarly, the hydrophobic fraction and SUVA254 of DOC also represent lower values in the degraded mire and ditches, which suggests that mire reclamation has resulted not only in the reduced DOC concentrations but also in the reduced chemical stability. Whereas the inorganic dissolved carbons (DIC) exhibits obvious increased trends in drainage ditches in comparison to natural mires. Analyses of exitation-emission fluorescence spectra reveal that the reclamation has greatly altered the DOC composition with more biological organic substances exporting from the Sanjiang Plain. The presence of protein- and tryptophan-like substances in the ditches indicates there has been extensive agricultural pollution in the surface waters. Changes in the hydrological regime of the mire landscapes by sustained agriculture activities are deemed the prodominant reason, and the trends in the export dynamics of dissolved carbons will keep on if mire reclamation continues in the future.

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Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

Sustainability ◽

10.3390/su12166497 ◽

2020 ◽

Vol 12 (16) ◽

pp. 6497

Author(s):

Zhengrong Liu ◽

Huanjun Liu ◽

Chong Luo ◽

Haoxuan Yang ◽

Xiangtian Meng ◽

...

Keyword(s):

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Meteorological Data ◽

Regional Scale ◽

Google Earth ◽

Spatial And Temporal Patterns ◽

Large Area ◽

Disaster Monitoring ◽

Google Earth Engine

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Google Earth Engine provides the possibility of quickly extracting the disaster range over a large area. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (RNDVI_TM(i)), the NDVI median phenology standardization model (RNDVI_AM(i)(j)), and the NDVI median spatiotemporal standardization model (RNDVI_ZM(i)(j)). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for RNDVI_TM(i), RNDVI_AM(i), and (j)RNDVI_ZM(i)(j), respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and was applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

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Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

Remote Sensing ◽

10.3390/rs13040579 ◽

2021 ◽

Vol 13 (4) ◽

pp. 579

Author(s):

Xueqin Jiang ◽

Shenghui Fang ◽

Xia Huang ◽

Yanghua Liu ◽

Linlin Guo

Keyword(s):

Time Series ◽

Precision Agriculture ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Southern China ◽

Growth Period ◽

Growth Monitoring ◽

Rice Growth ◽

Red Edge

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.

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A Robust Vegetation Index Based on Different UAV RGB Images to Estimate SPAD Values of Naked Barley Leaves

Remote Sensing ◽

10.3390/rs13040686 ◽

2021 ◽

Vol 13 (4) ◽

pp. 686

Author(s):

Yu Liu ◽

Kenji Hatou ◽

Takanori Aihara ◽

Sakuya Kurose ◽

Tsutomu Akiyama ◽

...

Keyword(s):

Large Scale ◽

Vegetation Index ◽

Smallholder Farmers ◽

Vegetation Indices ◽

Ground Level ◽

High Temporal Resolution ◽

Growth Stages ◽

Chlorophyll Contents ◽

Naked Barley ◽

Barley Leaves

Chlorophyll content in plant leaves is an essential indicator of the growth condition and the fertilization management effect of naked barley crops. The soil plant analysis development (SPAD) values strongly correlate with leaf chlorophyll contents. Unmanned Aerial Vehicles (UAV) can provide an efficient way to retrieve SPAD values on a relatively large scale with a high temporal resolution. But the UAV mounted with high-cost multispectral or hyperspectral sensors may be a tremendous economic burden for smallholder farmers. To overcome this shortcoming, we investigated the potential of UAV mounted with a commercial digital camera for estimating the SPAD values of naked barley leaves. We related 21 color-based vegetation indices (VIs) calculated from UAV images acquired from two flight heights (6.0 m and 50.0 m above ground level) in four different growth stages with SPAD values. Our results indicated that vegetation extraction and naked barley ears mask could improve the correlation between image-calculated vegetation indices and SPAD values. The VIs of ‘L*,’ ‘b*,’ ‘G − B’ and ‘2G − R − B’ showed significant correlations with SPAD values of naked barley leaves at both flight heights. The validation of the regression model showed that the index of ‘G-B’ could be regarded as the most robust vegetation index for predicting the SPAD values of naked barley leaves for different images and different flight heights. Our study demonstrated that the UAV mounted with a commercial camera has great potentiality in retrieving SPAD values of naked barley leaves under unstable photography conditions. It is significant for farmers to take advantage of the cheap measurement system to monitor crops.

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Exploring the Spatial Characteristics of Typhoon-Induced Vegetation Damages in the Southeast Coastal Area of China from 2000 to 2018

Remote Sensing ◽

10.3390/rs12101692 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1692 ◽

Cited By ~ 2

Author(s):

Lizhen Lu ◽

Chuyi Wu ◽

Liping Di

Keyword(s):

Coastal Area ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Damage Index ◽

Google Earth ◽

Spatial Characteristics ◽

Spatial Statistical Analysis ◽

Landfalling Typhoon ◽

Vegetation Damage

The southeast coastal area of China (SCAC), a typhoon-prone area with a long coastline, suffers severe damage from typhoons almost every year. Exploring the spatial characteristics of historical typhoon-induced vegetation damage (VD) is crucial to predicting VD after severe typhoon landfalls and improving strategies for vegetation protection and restoration. Remote sensing is an efficient and feasible approach for measuring large-scale VD caused by natural disasters. This paper, by exploring the spatial distribution of VD of every severe landfalling typhoon with Google Earth Engine (GEE), aims to reveal the spatial characteristics of typhoon-induced VD in SCAC. Firstly, the values of disaster vegetation damage index (DVDI), difference in enhanced vegetation index (DEVI), and normalized difference vegetation index (DNDVI) for the 28 selected landing typhoons in SCAC were calculated and compared by using moderate resolution imaging spectroradiometer (MODIS) data in GEE. Secondly, every DVDI image was overlaid with land cover, elevation, relative aspect and typhoon path layers in ArcGIS. Thirdly, spatial characteristics of VD were revealed with the aid of spatial statistical analysis. The study found that: (1) DVDI is a more effective index for evaluating VD caused by typhoons. (2) The Pearl River Delta is the most severe VD region. The severe VD regions for four typhoon groups have significantly spatial correlation with typhoon-landing locations. (3) Forests are ranked the first in terms of damaged areas by typhoon in every year, followed by sparse forests. (4) Topography has no influence on VD by a single typhoon event, and relative aspect has no correlation with VD caused by typhoons in SCAC.

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