Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery

Zhong, Liheng; Gong, Peng; Biging, Gregory S.

doi:10.1016/j.rse.2013.08.023

Cited by 303 publications

(177 citation statements)

References 40 publications

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“…Several variables were calculated from multi-temporal GF-1 WFV images (4 seasons). Mean blue, green, red and near-infrared spectral features were computed from the values of all pixels forming an object, showing information related to leaf pigment and vegetation status [48,49]. The textural features related to crop structure, soil background and planting patterns, including gray-level co-occurrence matrix (GLCM) correlation, dissimilarity and entropy, were calculated from GF-1 WFV bands (blue band to near infrared band) [50,51].…”

Section: Feature Extractionmentioning

confidence: 99%

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

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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.

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Section: Feature Extractionmentioning

confidence: 99%

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

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“…Studies revealing pressures from unsustainable agriculture practices have mainly focused on effects from irrigation strategies (Abbas et al 2013;Martínez-López et al 2014;Shahriar Pervez et al 2014), nitrogen treatment (Tilling et al 2007;Chen et al 2010;Perry et al 2012), and crop characterization (Zhong et al 2014;Alcantara et al 2012;Jain et al 2013). Structural properties of the studied areas are less revealing than spectral ones for these tasks, therefore passive multispectral or hyperspectral data have mainly been used.…”

Section: Agriculture Monitoringmentioning

confidence: 99%

“…Thresholding Landsat-derived NDVI values outperformed three MODIS based methodologies in almost all scales for both winter and summer periods, with hierarchical training method being the best among the MODIS ones. Zhong et al (2014) showed that phenological metrics extracted by TM/ETM+ time series can map corn and soybean more accurately than spectral features in cross-year classifications, i.e. when the training and test features correspond to different cropping years.…”

Section: Agriculture Monitoringmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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“…The methods exploit both the absolute greenness as well as the greenness dynamics, or land surface phenology, of the disparate crop types (Chang et al 2007;Shao et al 2010;Turker and Arikan 2005;Wardlow, Egbert, and Kastens 2007;Zhong, Gong, and Biging 2014). Data from the MODIS is well suited for mapping crops worldwide because of its daily temporal and moderate spatial resolution (250 m in visible and NIR bands); MODIS data have been used to map crop types across different parts of the world (Teluguntla et al 2017;Vintrou et al 2012;Wardlow and Egbert 2008).…”

Section: Introductionmentioning

confidence: 99%

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

Wallace

Thenkabail

Rodriguez

et al. 2017

GIScience & Remote Sensing

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An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map cropland status (planted or fallowed) with over 75% user's and producer's accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poor places and for use in historical modeling. Timely and accurate knowledge of the extent of fallowing can provide decision makers with insights and knowledge to mitigate the impacts of drought and provide a scientific basis for effective management response. This study is part of the WaterSMART (Sustain and Manage America's Resources for Tomorrow) project, an interdisciplinary and collaborative research effort focused on improving water conservation and optimizing water use.

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Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery

Cited by 303 publications

References 40 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

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

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