Agricultural Monitoring in Regional Scale Using Clustering on Satellite Image Time Series

Abstract

The remote sensing images are more accessible nowadays and there are proper technologies to receive, distribute, manipulate and process long satellite image time series that can be used to improve traditional methods for harvest monitoring and forecasting. The potential of the satellite multi-temporal images to support research of agricultural monitoring has increased according to improvements in technological development, especially in analysis of large volume of data available for knowledge discovery. In Brazil, sugarcane is cultivated on extensive fields and is the main agriculture crop used to produce ethanol. The main objective of this chapter is to monitor the sugarcane crop by clustering analysis with multi-temporal satellite images having low spatial resolution. A large database of this kind of image and specific software were used to perform the image preprocessing phase, extract time series, apply clustering method and enable the data visualization on several steps during the whole analysis process. According to the analysis done, our methodology allows to identify land areas with similar development patterns, also considering different growing seasons for the crops, covering monthly and annual periods. Results confirm that satellite images of low spatial resolution can indeed be satisfactorily used in agricultural crop monitoring in regional scale.

Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing

“…Clustering analysis is an unsupervised method without the need for a priori information that is applied to vegetation monitoring and classification [ 29 , 30 , 31 ]. Pixels are grouped according to their similarities and a label is assigned to each group.…”

“…The clustering algorithms largely used in remote sensing can be classified in three main classes: centroid-based methods such as K-means algorithm, neighborhood-based methods and hierarchical clustering [ 33 , 34 , 35 , 36 ]. The K-means approach is the best known clustering algorithm widely used in the last few decades [ 29 , 30 , 31 , 37 ], even if it can be unstable for large data sets. The two other classes limit this drawback but have higher computational costs.…”

“…Recent works introduce clustering to process multitemporal remote sensing and analysis land cover evolution, essentially for agricultural monitoring [ 31 , 37 ] and land use change [ 29 , 30 ]. Such time series data mining techniques allow us, by trend analysis, to examine shocks and unexpected variations or to detect anomalies and stable similar patterns.…”

“…Such time series data mining techniques allow us, by trend analysis, to examine shocks and unexpected variations or to detect anomalies and stable similar patterns. In agriculture, this information permits us to distinguish dissimilar levels of vegetation cover towards parcels as well as identify the planting pattern and phenology anomalies across development stages [ 29 , 31 ]. Times series are exploited by selecting sets of dates appropriate to the information to highlight [ 31 , 37 ].…”

Analyzing the Status of Mango Trees in Brgy. Cantipay, Carmen, Cebu Using Ndvi and Time Series Clustering

“…Using time series clustering, similarities between trends are measured in order to form clusters of the same pattern (Jamali et. al, 2015;Roelofsen, 2018;Kunze, 2018, Gonçalves et. al., 2018.…”