Cropland Mapping and Change Detection: Toward Zimbabwean Cropland Inventory

Useya, Juliana; Chen, Shengbo; Murefu, Mike

doi:10.1109/access.2019.2912807

Cited by 27 publications

(10 citation statements)

References 51 publications

(80 reference statements)

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“…NDVI and bare-soil index thresholding determine the spatiotemporal cropland changes. 28 A new on-orbit CD method for SAR image developed via extreme self-paced learning machine by gradually selecting the most confident changed pixels. Incremental pattern predicts the changed pixel.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Mixed pixel removal in north Tamil Nadu region for accurate area measurement

Mani¹,

Nagalingam²,

Aruldoss³

et al. 2021

Computational Intelligence

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In remote sensing, vegetation and water areas delineation from satellite image plays a vital role for urban and rural planning. Delineation of vegetation and water area is a challenging task due to mixed pixels and geometric distortion over the boundary region. Geometric distortion arises due to change in velocity and speed of satellite during image acquisition, and mixed pixels arises due to different surfaces in a particular area. Traditional methods apply classifier algorithms such as support vector machine, neural network, and fuzzy for vegetation and water area delineation. The traditional methods require more training dataset and consume more interpretation time for delineation. In this article, we propose transverse dyadic wavelet transform (TDyWT) to delineate vegetation and water area from Landsat 8 images. The TDyWT method enhances the boundary and curvature area of satellite image for accurate delineation. From the experimental results, the proposed TDyWT approach delineates the area of subclass for vegetation and water areas with 95% of accuracy with respect to the ground truth.

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Section: Related Workmentioning

confidence: 99%

“…Change detection (CD) is carried out using postclassification statistical method. NDVI and bare‐soil index thresholding determine the spatiotemporal cropland changes 28 . A new on‐orbit CD method for SAR image developed via extreme self‐paced learning machine by gradually selecting the most confident changed pixels.…”

Section: Related Workmentioning

confidence: 99%

Mixed pixel removal in north Tamil Nadu region for accurate area measurement

Mani¹,

Nagalingam²,

Aruldoss³

et al. 2021

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…RF (Breiman, 2001) is a supervised ensemble classification method that considers a forest of randomized trees in a random composition of single decision trees that try to find the best fit between the original and the sampling data (Dimov et al, 2016). RF utilizes bootstrap aggregation (iterative bagging) operation where the number of trees (ntree) are independently built using a random subset of samples from the training data.…”

Section: Image Classificationmentioning

confidence: 99%

Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data

Useya

Chen

2019

Chin. Geogr. Sci.

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It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations' economic development. Majority of agricultural professionals rarely capture the cropping patterns necessary to promote Good Agricultural Practises. Objective of this research is to explore the potential of mapping cropping patterns occurring on different field parcels on small-scale farmlands in Zimbabwe. The first study location under investigation are the International Maize and Wheat Improvement Center (CIMMYT) research station and a few neighboring fields, the second is Middle Sabi Estate. Fourier time series modeling was implemented to determine the trends befalling on the two study sites. Results reveal that Sentinel-1 synthetic aperture radar (SAR) time series allow detection of subtle changes that occur to the crops and fields respectively, hence can be utilized to detect cropping patterns on small-scale farmlands. Discrimination of the main crops (maize and soybean) grown at CIMMYT was possible, and crop rotation was synthesized where sowing starts in November. A single cropping of early and late crops was observed, there were no winter crops planted during the investigation period. At Middle Sabi Estate, single cropping on perennial sugarcane fields and triple cropping of fields growing leafy vegetables, tomatoes and onions were observed. Classification of stacked images was used to derive the crop rotation maps representing what is practised at the farming lands. Random forest classification of the multi-temporal image stacks achieved overall accuracies of 99% and 95% on the respective study sites. In conclusion, Sentinel-1 time series can be implemented effectively to map the cropping patterns and crop rotations occurring on small-scale farming land. We recommend the use of Sentinel-1 SAR multi-temporal data to spatially explicitly map cropping patterns of single-, double-and triple-cropping systems on both small-scale and large-scale farming areas to ensure food security.

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“…In the past decade, a series of high-resolution satellites have launched a new era of satellite remote sensing. The acquisition of high-resolution remote sensing images is more convenient, with high spatial resolution and rich detailed information of ground features, which has important and far-reaching significance for the monitoring of land-use change [6][7][8], building change [9,10], vegetation ecological monitoring [11][12][13], disaster monitoring and evaluation [14,15], and coastline change [16]. A series of classical remote sensing change detection methods developed in the past few decades have also been applied in the change detection of high-resolution images.…”

Section: Introductionmentioning

confidence: 99%