Object Based Image Analysis (OBIA) and Data Mining (DM) in Landsat time series for mapping soybean in intensive agricultural regions

2012 ◽  
Author(s):  
Antonio Roberto Formaggio ◽  
Matheus Alves Vieira ◽  
Camilo Daleles Renno
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Image Analysis ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Agricultural Regions

Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas

2012 ◽  
Vol 123 ◽  
pp. 553-562 ◽  
Author(s):  
Matheus Alves Vieira ◽  
Antonio Roberto Formaggio ◽  
Camilo Daleles Rennó ◽  
Clement Atzberger ◽  
Daniel Alves Aguiar ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Image Analysis ◽  
Remotely Sensed ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Harnessing the Temporal Dimension to Improve Object-Based Image Analysis Classification of Wetlands

2018 ◽  
Vol 10 (9) ◽  
pp. 1467 ◽  
Author(s):  
Meghan Halabisky ◽  
Chad Babcock ◽  
L. Moskal
Keyword(s):  
Time Series ◽  
Image Analysis ◽  
Classification Accuracy ◽  
Sampling Design ◽  
Landsat Imagery ◽  
Temporal Dimension ◽  
Substantial Impact ◽  
Object Based Image Analysis ◽  
Temporal Domain ◽  
Object Based

Research related to object-based image analysis has typically relied on data inputs that provide information on the spectral and spatial characteristics of objects, but the temporal domain is far less explored. For some objects, which are spectrally similar to other landscape features, their temporal pattern may be their sole defining characteristic. When multiple images are used in object-based image analysis, it is often constrained to a specific number of images which are selected because they cover the perceived range of temporal variability of the features of interest. Here, we provide a method to identify wetlands using a time series of Landsat imagery by building a Random Forest model using each image observation as an explanatory variable. We tested our approach in Douglas County, Washington, USA. Our approach exploiting the temporal domain classified wetlands with a high level of accuracy and reduced the number of spectrally similar false positives. We explored how sampling design (i.e., random, stratified, purposive) and temporal resolution (i.e., number of image observations) affected classification accuracy. We found that sampling design introduced bias in different ways, but did not have a substantial impact on overall accuracy. We also found that a higher number of image observations up to a point improved classification accuracy dependent on the selection of images used in the model. While time series analysis has been part of pixel-based remote sensing for many decades, with improved computer processing and increased availability of time series datasets (e.g., Landsat archive), it is now much easier to incorporate time series into object-based image analysis classification.

scholarly journals OBJECT-BASED IMAGE ANALYSIS APPROACH TO DETERMINE THE FALLOW PERIODS FOR SHIFTING CULTIVATION IN INDIGENOUS COMMUNITIES IN GUYANA

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 131-136
Author(s):  
H. Persaud ◽  
R. Thomas ◽  
P. Bholanath ◽  
T. Smartt ◽  
P. Watt
Keyword(s):  
Time Series ◽  
Image Analysis ◽  
Shifting Cultivation ◽  
Sustainable Forest Management ◽  
Indigenous Communities ◽  
Sustainable Land Use ◽  
Primary Forest ◽  
Agricultural Practice ◽  
Object Based Image Analysis ◽  
Object Based

Abstract. Shifting cultivation is an agricultural practice that is the basis of subsistence for the Indigenous population in Guyana and has impacted on a total forest area of 13,922ha to varying degrees of impact on forest carbon. Generally, within these communities, there are two types of shifting cultivation: pioneer and rotational. Pioneer shifting cultivation involves the cutting of primary forest and subsequent cropping and then abandonment. Rotational shifting cultivation involves revisiting areas on a rotational cycle. In Guyana, shifting cultivation is not included in the sustainable land use system since no work has been done to understand the rotational cycles. This study utilized an Object-based image analysis (OBIA) of time-series satellite data (Landsat TM5 and OLI) for the period 2004 to 2017 to determine the dynamics of land cover, time-series changes, and prevailing shifting cultivation cycle in the indigenous communities of Jawalla and Phillipai in the western section of Guyana. OBIA proved to be an efficient method for shifting cultivation and sustainable forest management analyses in Guyana. The findings of this study indicate that short fallows are associated with shifting cultivation in Guyana and the size of the patches cleared each year has been increasing. These trends have potential ecological and livelihood implications that can impact the flow of ecosystem services and the sustainability of livelihoods.

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