SiRCub, A Novel Approach to Recognize Agricultural Crops Using Supervised Classification

2019 ◽  
pp. 1129-1147
Author(s):  
Jordi Creus Tomàs ◽  
Fabio Augusto Faria ◽  
Júlio César Dalla Mora Esquerdo ◽  
Alexandre Camargo Coutinho ◽  
Claudia Bauzer Medeiros
Keyword(s):  
Time Series ◽  
Software Package ◽  
Agricultural Research ◽  
Crop Model ◽  
Support Vector ◽  
Agricultural Crop ◽  
Agricultural Crops ◽  
Mato Grosso ◽  
Novel Approach ◽  
Ndvi Time Series

This paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles.

SiRCub, A Novel Approach to Recognize Agricultural Crops Using Supervised Classification

2017 ◽  
Vol 8 (4) ◽  
pp. 20-36
Author(s):  
Jordi Creus Tomàs ◽  
Fabio Augusto Faria ◽  
Júlio César Dalla Mora Esquerdo ◽  
Alexandre Camargo Coutinho ◽  
Claudia Bauzer Medeiros
Keyword(s):  
Time Series ◽  
Supervised Classification ◽  
Agricultural Research ◽  
Support Vector ◽  
Agricultural Crop ◽  
Mato Grosso ◽  
New Approach ◽  
Novel Approach ◽  
Research Corporation ◽  
Ndvi Time Series

This paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles.

scholarly journals Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil

2012 ◽  
Vol 47 (9) ◽  
pp. 1270-1278 ◽  
Author(s):  
Daniel de Castro Victoria ◽  
Adriano Rolim da Paz ◽  
Alexandre Camargo Coutinho ◽  
Jude Kastens ◽  
J. Christopher Brown
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Vegetation Index ◽  
The State ◽  
Mato Grosso ◽  
Cropland Area ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Production Areas

The objective of this work was to evaluate a simple, semi‑automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R² = 0.89), but poor agreement in municipalities with less than 5% crop cover (R² = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil.

scholarly journals Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Long Zhao ◽  
Pan Zhang ◽  
Xiaoyi Ma ◽  
Zhuokun Pan
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Information Extraction ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Land Resource ◽  
Spectral Angle Mapper ◽  
Ndvi Time Series

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

scholarly journals DETERMINAN KESEJAHTERAAN PETANI TANAMAN PANGAN DI PROVINSI ACEH TAHUN 2012-2017

2019 ◽  
Vol 4 (2) ◽  
pp. 178-198
Author(s):  
Akram Akramur Rasyid ◽  
Budyanra Budyanra
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Rice Seed ◽  
Agricultural Crop ◽  
Agricultural Crops ◽  
Previous Period ◽  
Welfare Level ◽  
Current Period ◽  
The Government

AbstractThe economy in Aceh Province is heavily dependent on agriculture, especially agricultural-cropswhich absorb the largest workforce of 29.68 percent and contribute the second largest grossdomestic product (GDP) after industrial sector by 13.14 percent in 2017 and increase year toyear. However, the absorption of agricultural manpower especially large agricultural-crops is notbalanced with farmers' welfare level when viewed from NTPP which is still below 100 value. Thisresearch aims to know the development of welfare level of agricultural-crop farmer and othervariables, variables that affect it. In this research, the welfare level of agricultural-crop farmers isapproached with NTPP variable as dependent variable, while independent variables used are drypaddy harvest price, rice seed price, grain transport cost, and NTPP of previous period. The datasource is from the Badan Pusat Statistik (BPS) in 2012-2017 monthly. This research usesdescriptive analysis and time series data regression analysis. The results showed that the variablesof dry harvested paddy price, NTPP of the previous period were significantly positive, and theprice of paddy seed was negatively significant to NTPP current period in the long run. In the shortterm the price of dry grain harvest and NTPP of the previous period was significantly positive forNTPP current period. The results of this study recommend to the government in maintaining thestability of the rice seed price by subsidizing the good quality of rice seeds to farmers andmaintaining the stability of the dry paddy harvest price by determining the Government PurchasePrice (GPP) according to the harvest time.Keywords: Farmers’ welfare, agricultural-crops, dry paddy harvest price, rice seed price, graintransport cost, time series data regression.AbstrakPerekonomian di Provinsi Aceh sangat bergantung dengan sektor pertanian khususnya tanamanpangan yang mampu menyerap tenaga kerja terbesar yaitu 29,68 persen dan memberikankontribusi produk domestik bruto (PDB) kedua terbesar setelah sektor industri sebesar 13,14persen tahun 2017 dan meningkat dari tahun ke tahun. Namun, penyerapan tenaga kerja sektorpertanian khususnya tanaman pangan yang besar tersebut tidak diimbangi dengan tingkatkesejahteraan petani bila dilihat dari NTPP (Nilai Tukar Petani Tanaman Pangan) yang masihberada dibawah nilai 100. Penelitian ini bertujuan untuk mengetahui perkembangan tingkatkesejahteraan petani tanaman pangan dan variabel-variabel yang memengaruhinya. Pada penelitianini tingkat kesejahteraan petani tanaman pangan didekati dengan variabel NTPP sebagai variabeldependen, sedangkan variabel independen yang digunakan adalah harga gabah kering panen, hargabenih padi, ongkos angkut gabah, dan NTPP bulan sebelumnya. Penelitian ini menggunakananalisis deskriptif dan analisis regresi data time series. Hasil penelitian menunjukkan bahwavariabel harga gabah kering panen, NTPP bulan sebelumnya signifikan positif, dan harga benihpadi signifikan negatif terhadap NTPP bulan sekarang pada jangka panjang. Pada jangka pendekharga gabah kering panen dan NTPP bulan sebelumnya signifikan positif terhadap NTPP bulansekarang. hasil penelitian ini merekomendasikan kepada pemerintah dalam menjaga kestabilanharga benih padi dengan memberikan subsidi benih padi yang berkualitas kepada para petani danmenjaga kestabilan harga gabah kering panen dengan menentukan harga pembelian pemerintah(HPP) sesuai masa panen.

scholarly journals Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China

2020 ◽  
Vol 12 (24) ◽  
pp. 4010
Author(s):  
Xiang Liu ◽  
Huiyu Liu ◽  
Pawanjeet Datta ◽  
Julian Frey ◽  
Barbara Koch
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spartina Alterniflora ◽  
Vegetation Index ◽  
Invasive Plant ◽  
Training Data ◽  
Support Vector ◽  
Detection Scheme ◽  
Series Analysis ◽  
Ndvi Time Series

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.

scholarly journals Real-Time Detection of Application-Layer DDoS Attack Using Time Series Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Tongguang Ni ◽  
Xiaoqing Gu ◽  
Hongyuan Wang ◽  
Yu Li
Keyword(s):  
Time Series ◽  
Denial Of Service ◽  
Support Vector ◽  
Svm Classifier ◽  
Ddos Attacks ◽  
Application Layer ◽  
Ip Address ◽  
Detection Systems ◽  
Ddos Attack ◽  
Novel Approach

Distributed denial of service (DDoS) attacks are one of the major threats to the current Internet, and application-layer DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. Consequently, neither intrusion detection systems (IDS) nor victim server can detect malicious packets. In this paper, a novel approach to detect application-layer DDoS attack is proposed based on entropy of HTTP GET requests per source IP address (HRPI). By approximating the adaptive autoregressive (AAR) model, the HRPI time series is transformed into a multidimensional vector series. Then, a trained support vector machine (SVM) classifier is applied to identify the attacks. The experiments with several databases are performed and results show that this approach can detect application-layer DDoS attacks effectively.

scholarly journals Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications

2019 ◽  
Vol 11 (11) ◽  
pp. 1370 ◽  
Author(s):  
Petar Dimitrov ◽  
Qinghan Dong ◽  
Herman Eerens ◽  
Alexander Gikov ◽  
Lachezar Filchev ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Training Data ◽  
Support Vector ◽  
Pixel Classification ◽  
Good Correspondence ◽  
Winter Cereals ◽  
Ndvi Time Series ◽  
Sentinel 2

This paper presents the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m normalized difference vegetation index (NDVI) time series. Two sub-pixel classification methods, artificial neural network (ANN) and support vector regression (SVR) were used where the output was a set of area fraction images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classifications were used to obtain training data for the sub-pixel classifications. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10 × 10 km2, especially when the SVR method was used. For the five dominant classes in the test sites the R2 obtained after the aggregation was 86% (winter cereals), 81% (sunflower), 92% (broad-leaved forest), 89% (maize), and 67% (grasslands) when the SVR method was used.

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