Campo Verde Database: Seeking to Improve Agricultural Remote Sensing of Tropical Areas

Sanches, I. D.; Feitosa, Raul Queiroz; Achanccaray, Pedro; Soares, Marinalva Dias; Luiz, A. J. B.; Schultz, Bruno; Maurano, L. E. P.

doi:10.1109/lgrs.2017.2789120

Cited by 47 publications

(33 citation statements)

References 12 publications

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“…Summer crops are responsible for more than 90% of annual grain production in the country [9]. Due to area expansion and evolution of management practices, Brazil has become the largest exporter of such commodities as soybean, sugar, meat, coffee, and orange juice [10,11]. According to the Brazilian Institute of Geography and Statistics (IBGE), in 2017 pasture areas in Brazil represented 149.8 M ha; soybean, 33.9 M ha; corn, 17.8 M ha; sugarcane, 9.3 M ha; beans, 3.1 M ha; wheat, 1.9 M ha; orange/citrus orchards, 0.6 M ha; and planted trees for wood production, 7.7 M ha [12].…”

Section: Introductionmentioning

confidence: 99%

Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

et al. 2019

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Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.

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Section: Introductionmentioning

confidence: 99%

Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

et al. 2019

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“…In other words, the Sentinel series may have a high synergetic potential for overcoming the limitation of single remote sensing techniques for forest AGB estimation. The Sentinel imagery have been applied in a number of previous vegetation studies, focusing on classification [30][31][32], vegetation parameters on agricultural fields [33][34][35], grassland [36][37][38], and forests [39,40], as well as the damage extent of disasters [41][42][43], while forest AGB mapping based on Sentinel imagery is still insufficient.The techniques for estimating forests' AGB based on remote sensing data have allowed for 'scale-up' or extrapolation of the field data collected for larger scales [8,44]. It is a predictive mapping process for an estimation of the value at a location without direct observation.…”

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confidence: 99%

Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery

Lin

Ren

Zhang

et al. 2018

Forests

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Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10 −3 , 0.07, 0.08 Mg·ha −1 , and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha −1 , and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha −1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques.Traditional field-based measurements provide the most accurate AGB values, but they are destructive and spatially limited [10,11]. Uncertainty and bias in field measurements obviously exist, particularly those with large trees and tropical issues [4,5]. Combining remote sensing and sample plot data has become a popular method to generate spatially explicit estimations of forest AGB [12,13]. Various types of remote-sensing data are used for forest biomass estimation such as optical sensor data, radio detection and ranging (radar) data, and light detection and ranging (LiDAR) data, with each one having certain advantages over the others [14,15]. Optical sensors were first applied to retrieve the horizontal forest structure and AGB assessments through field sampling, due to their aggregate spectral signatures (reflectance or vegetation indices) with global coverage, repetitiveness, and cost-effectiveness [16,17]. Optical remote sensing data from a number of platforms, such as IKONOS, Quickbird, Worldview, ZY-3, systeme probatoire d'observation de la terre (SPOT), Sentinel, Landsat, and moderate-resolution imaging spectroradiometer (MODIS), with spatial resolutions varying from less than one meter to hundreds of meters, have been used by numerous researchers for biomass estimation [18][19][20]. However, the widespread usage of optical data is limited...

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“…The analysis of crop dynamics is simplified by the fact that there is usually a single crop per parcel during the whole productive season. Crop dynamics in tropical regions is considerately more complex, as multiple harvests per year are possible and due to particular practices such as crop rotation, non-tillage, and irrigation [5]. Considering also the large agricultural extents and the diversity of possible crop types, the production of detailed and reliable crop maps in tropical regions is a very challenging task.…”

Section: Crop Mapping From Remote Sensing Datamentioning

confidence: 99%

“…The edges identify possible class transitions on adjacent dates. From this graph, we can infer the set of admissible sequences to be evaluated in the computation of Equation (5). In this way, we reduce the number of sequences to be evaluated.…”

Section: Modelling Crop Dynamicsmentioning

confidence: 99%

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Combining Deep Learning and Prior Knowledge for Crop Mapping in Tropical Regions from Multitemporal SAR Image Sequences

et al. 2019

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Accurate crop type identification and crop area estimation from remote sensing data in tropical regions are still considered challenging tasks. The more favorable weather conditions, in comparison to the characteristic conditions of temperate regions, permit higher flexibility in land use, planning, and management, which implies complex crop dynamics. Moreover, the frequent cloud cover prevents the use of optical data during large periods of the year, making SAR data an attractive alternative for crop mapping in tropical regions. This paper evaluates the effectiveness of Deep Learning (DL) techniques for crop recognition from multi-date SAR images from tropical regions. Three DL strategies are investigated: autoencoders, convolutional neural networks, and fully-convolutional networks. The paper further proposes a post-classification technique to enforce prior knowledge about crop dynamics in the target area. Experiments conducted on a Sentinel-1 multitemporal sequence of a tropical region in Brazil reveal the pros and cons of the tested methods. In our experiments, the proposed crop dynamics model was able to correct up to 16.5% of classification errors and managed to improve the performance up to 3.2% and 8.7% in terms of overall accuracy and average F1-score, respectively.

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Campo Verde Database: Seeking to Improve Agricultural Remote Sensing of Tropical Areas

Cited by 47 publications

References 12 publications

Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery

Combining Deep Learning and Prior Knowledge for Crop Mapping in Tropical Regions from Multitemporal SAR Image Sequences

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