Phenology-Based Vegetation Index Differencing for Mapping of Rubber Plantations Using Landsat OLI Data

Fan, Hui; Fu, Xiaohua; Zhang, Zheng; Wu, Qiong

doi:10.3390/rs70506041

Cited by 94 publications

(69 citation statements)

References 47 publications

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“…However, the inherent spectral variability of crop types is frequently influenced by local weather or farmer decisions. Specifically, different crops in the same region may share similar spectral signatures, while the same crop types may present different spectral signatures in different locations [14,15]. Fortunately, each crop has a specific crop calendar with well-defined planting times as well as unique seasonal growth and development rhythms, resulting in variation among apparent spectral reflecting performance within the crop-growing season [16].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…For future studies, improvements may come from incorporating stand age as an explanatory variable or using alternative or more vegetation indices in the models. Fan et al [53], for example, used Landsat 8 time series of NDVI, EVI, the Atmospherically-Resistant Vegetation Index (ARVI), Normalized Difference Moisture Index (NDMI) and Tasseled Cap Greenness (TCG) to identify foliation (leaf flushing) and defoliation (leaf-off) days to help distinguish rubber trees from natural forests and croplands with up to 96% (kappa = 0.92) accuracy in Tropical South West China. For a first cut analysis, we chose NDVI as a commonly-used measure of greenness.…”

Section: Discussionmentioning

confidence: 99%

Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

et al. 2017

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Abstract:Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plots.

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“…For e.g., even the most widely used global forest product [6] defines forest on the basis of tree structure and therefore does not differentiate between forest and plantations. The main difficulty lies in that tree plantations frequently have spectral properties similar to natural forests [3]. On the other hand, detecting burned area is also challenging since the fires have a seasonal pattern and only last for a few months.…”

Section: Related Workmentioning

confidence: 99%

Predict Land Covers with Transition Modeling and Incremental Learning

Jia¹,

Khandelwal²,

Nayak³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Successful land cover prediction can provide promising insights in the applications where manual labeling is extremely difficult. However, traditional machine learning models are plagued by temporal variation and noisy features when directly applied to land cover prediction. Moreover, these models cannot take fully advantage of the spatio-temporal relationship involved in land cover transitions. In this paper, we propose a novel spatio-temporal framework to discover the transitions among land covers and at the same time conduct classification at each time step. Based on the proposed model, we incrementally update the model parameters in the prediction process, thus to mitigate the impact of the temporal variation. Our experiments in two challenging land cover applications demonstrate the superiority of the proposed method over multiple baselines. In addition, we show the efficacy of spatio-temporal transition modeling and incremental learning through extensive analysis.

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Phenology-Based Vegetation Index Differencing for Mapping of Rubber Plantations Using Landsat OLI Data

Cited by 94 publications

References 47 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

Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

Predict Land Covers with Transition Modeling and Incremental Learning

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