scholarly journals Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California

2018 ◽  
Vol 10 (11) ◽  
pp. 1745 ◽  
Author(s):  
Ana de Castro ◽  
Johan Six ◽  
Richard Plant ◽  
José Peña
Keyword(s):  
Time Series ◽  
Image Analysis ◽  
The Other ◽  
Crop Phenology ◽  
Central California ◽  
Modis Ndvi ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Crop Calendar ◽  
Ndvi Time Series

Remote sensing technology allows monitoring the progress of vegetation and crop phenology in large regions. Seasonal vegetation trends are commonly estimated from high temporal resolution but coarse spatial resolution satellite imagery, e.g., from MODIS-NDVI (Moderate Resolution Imaging Spectroradiometer—Normalized Difference Vegetation Index) time-series, which has usually limited their application to scenarios with few land uses or crops covering areas larger than actual parcel sizes. As an alternative, this paper proposes a general and robust procedure to map crop phenology at the level of individual crop parcels, and validates its feasibility in a complex and diverse cropland area located in central California. A first calibration phase consisted of evaluating the three curve-fitting models implemented in the TIMESAT software (i.e., asymmetric Gaussian (AG), double logistic (DL), and adaptive Savitzky–Golay (SG) filtering) and reporting the model and its settings that best adjusted to the MODIS-NDVI profile of each crop studied. Next, based on the selected crop-specific models and with a crop map previously obtained from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-temporal images, the procedure mapped four crop calendar events (i.e., start, end, middle, and length of the season) and five phenology-related metrics (i.e., base, maximum, amplitude, derivatives, and integrals of the NDVI values) of the study region by object-based image analysis (OBIA) of the MODIS-NDVI time-series. To mitigate the impact of mixed pixels, the OBIA procedure was designed to automatically apply a restrictive criterion based on the coverage of MODIS-NDVI pixels in each crop parcel: (1) using only the MODIS-NDVI pixels that were placed 100% within each crop parcel (i.e., “pure” pixels); or (2) if no “pure” pixels exist in any crop parcel, using only pixels with coverage percentages greater than 50%, and in such cases, reporting the mixing percentage in the output file. The calibration phase showed that the performance of the SG filtering was superior in most crops, with the exception of rice, while the AG model was intermediate in all of the cases. Differences between the dates of the start and end of the season that were observed in 120 ground-truth fields and the ones estimated by the crop-specific models were in a range of 11 days (for the corn fields) and 22 days (for the vineyard fields) on average. The OBIA procedure was also validated in 240 independent parcels with “pure” MODIS-NDVI pixels, reporting 89% and 82% of accuracy when mapping the start and end of the season, respectively. Our results revealed different growth patterns of the studied crops, especially of the crop calendar events of herbaceous (i.e., corn, rice, sunflower, and tomato) and woody crops (i.e., almond, walnut, and vineyard), of the NDVI derivatives of rice and the other studied herbaceous crops, and of the NDVI integrals of vineyard and the other studied woody crops. The resulting maps and tables provide valuable geospatial information for every parcel over time with several applications in cropland management, irrigation scheduling, and ecosystem modeling.

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.

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 New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

2021 ◽  
Vol 193 (2) ◽  
Author(s):  
Jens Oldeland ◽  
Rasmus Revermann ◽  
Jona Luther-Mosebach ◽  
Tillmann Buttschardt ◽  
Jan R. K. Lehmann
Keyword(s):  
Image Analysis ◽  
Plant Species ◽  
Just In Time ◽  
Post Processing ◽  
Tall Grass ◽  
Field Surveys ◽  
Distribution Maps ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Before And After

AbstractPlant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

scholarly journals Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 830
Author(s):  
Adam R. Benjamin ◽  
Amr Abd-Elrahman ◽  
Lyn A. Gettys ◽  
Hartwig H. Hochmair ◽  
Kyle Thayer
Keyword(s):  
Image Analysis ◽  
Aquatic Vegetation ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Temporal Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Significant Difference ◽  
Additional Processing ◽  
Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

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