A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping

Maus, Victor; Câmara, Gilberto; Cartaxo, Ricardo; Sánchez, Alber; Ramos, Fernando M.; Queiroz, Gilberto Ribeiro de

doi:10.1109/jstars.2016.2517118

Cited by 202 publications

(195 citation statements)

References 35 publications

Supporting

Mentioning

183

Contrasting

Unclassified

Order By: Relevance

“…For the performance of different feature scenarios, the results indicated that the spectral features are fundamental for in-season crop classification, and temporal features extracted from high resolution time series significantly improves the classification accuracy relative to the case of using a single image, which is also consistent with the findings of the previous studies [55][56][57][58][59]. Ideally, each feature would provide extra information and improve classification accuracy.…”

Section: Discussionsupporting

confidence: 85%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

show abstract

Section: Discussionsupporting

confidence: 85%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Faced with the challenge of systematic land cover and land use mapping on a large scale, the images provided by the MODIS sensor (Moderate Resolution Imaging Spectroradiometer) [19], (e.g., [20]), combined with new classification algorithms (e.g., [21]), made possible highly accurate and recurring representations of the Earth's surface (e.g., [22,23]). Specifically, this work uses MODIS data and a novel approach to produce annual pasture area maps for the entire Brazil.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016

Parente

Ferreira

2018

Remote Sensing

View full text Add to dashboard Cite

Abstract:The pasturelands areas of Brazil constitute an important asset for the country, as the main food source for the world's largest commercial herd, representing the largest stock of open land in the country, occupying~21% of the national territory. Understanding the spatio-temporal dynamics of these areas is of fundamental importance for the goal of promoting improved territorial governance, emission mitigation and productivity gains. To this effect, this study mapped, through objective criteria and automatic classification methods (Random Forest) applied to MODIS (Moderate Resolution Imaging Spectroradiometer) images, the totality of the Brazilian pastures between 2000 and 2016. Based on 90 spectro-temporal metrics derived from the Red, NIR and SWIR1 bands and distinct vegetation indices, distributed between dry and wet seasons, a total of 17 pasture maps with an approximate overall accuracy of 80% were produced with cloud-computing (Google Earth Engine). During this period, the pasture area varied from~152 (2000) to~179 (2016) million hectares. This expansion pattern was consistent with the bovine herd variation and mostly occurred in the Amazon, which increased its total pasture area by~15 million hectares between 2000 and 2005, while the Cerrado, Caatinga and Pantanal biomes showed an increase of~8 million hectares in this same period. The Atlantic Forest was the only biome in which there was a retraction of pasture areas throughout this series. In general, the results of this study suggest the existence of two relevant moments for the Brazilian pasture land uses. The first, strongly supported by the opening of new grazing areas, prevailed between 2000 and 2005 and mostly occurred in the Deforestation Arc and in the Matopiba regions. From 2006 on, the total pasture area in Brazil showed a trend towards stabilization, indicating a slight intensification of livestock activity in recent years.

show abstract

“…Nonetheless, these techniques are based on a reduction dimension which implies a loss of information. To address this issue, new techniques, such as Dynamic Time Warping approaches with temporal weights, were successfully tested to classify land cover classes including single and double cropping systems, based on shape matching of MODIS EVI time series [69]. Bailly et al [70] implemented a Dense Bag-of-Temporal-SIFT-Words approach, which involves representing vegetation index time series in sequences of local features, such as peaks and constant growth.…”

Section: Sequential Croppingmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

View full text Add to dashboard Cite

For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

show abstract

A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping

Cited by 202 publications

References 35 publications

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016

Remote Sensing and Cropping Practices: A Review

Contact Info

Product

Resources

About