Examining the potential of open source remote sensing for building effective decision support systems for precision agriculture in resource-poor settings

Kpienbaareh, Daniel; Kansanga, Moses Mosonsieyiri; Luginaah, Isaac

doi:10.1007/s10708-018-9932-x

Cited by 19 publications

(14 citation statements)

References 64 publications

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“…The two bands of the Sentinel-1 IW mode compared to the several bands available from Sentinel-2 and PlanetScope and the use of the random forest algorithm may explain the poor accuracy from the Sentinel-1-only classification [77]. Quad-polarized data are known to perform better in identifying crop types when integrated with other data [78]. Future crop type mapping in the study locations should, therefore, integrate time series quad-polarized H/α polarimetric decomposition, if available, with available time series optical data to achieve better accuracies.…”

Section: Discussionmentioning

confidence: 99%

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

et al. 2021

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Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.

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Section: Discussionmentioning

confidence: 99%

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

et al. 2021

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“…Actually, this is a common open-source architecture for ESA Toolboxes ideal for the exploitation of earth observation data. As its name implies, it is mainly designed for processing data concerning Copernicus Sentinel missions [56], but it is functional for different operations on different data as well [54,55].…”

Section: Classification Algorithm Feedbackmentioning

confidence: 99%

“…The results were then analysed and compared using 150 random points automatically identified in the orthomosaics [54,55].…”

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confidence: 99%

Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets

et al. 2021

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The consolidation of unmanned aerial vehicle (UAV) photogrammetric techniques for campaigns with high and medium observation scales has triggered the development of new application areas. Most of these vehicles are equipped with common visible-band sensors capable of mapping areas of interest at various spatial resolutions. It is often necessary to identify vegetated areas for masking purposes during the postprocessing phase, excluding them for the digital elevation models (DEMs) generation or change detection purposes. However, vegetation can be extracted using sensors capable of capturing the near-infrared part of the spectrum, which cannot be recorded by visible (RGB) cameras. In this study, after reviewing different visible-band vegetation indices in various environments using different UAV technology, the influence of the spatial resolution of orthomosaics generated by photogrammetric processes in the vegetation extraction was examined. The triangular greenness index (TGI) index provided a high level of separability between vegetation and nonvegetation areas for all case studies in any spatial resolution. The efficiency of the indices remained fundamentally linked to the context of the scenario under investigation, and the correlation between spatial resolution and index incisiveness was found to be more complex than might be trivially assumed.

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“…The development of powerful cloud-based computational frameworks, coupled with the increasing accessibility of imagery resulting from the Landsat Global Archive Consolidation (LGAC) initiative [39] and the European Commission Copernicus programme (Sentinel 2A and 2B) [40] is making custom classification more accessible, and have the potential to help overcome the limitations of existing products. An example of the combined use of these assets is the Google Earth Engine (GEE), a planetary-scale platform for geospatial data science powered by Google's cloud platform [41].…”

Section: Introductionmentioning

confidence: 99%

“…As highlighted by [54], the rising availability of improved RS data and tools offers the opportunity for the development of land cover and land uses maps at finer spatial and temporal resolutions, which are suitable to support applications in an agricultural setting with predominance of smallholder and resource-poor agricultural systems. This is particularly crucial in the face of rising climate variability and the associated uncertainties on smallholder agriculture [40]. Additionally, according to [3], Mozambique is one of the three Southern African countries of high priority for cropland mapping and which would really benefit from a timely land cover/land use update.…”

Section: Introductionmentioning

confidence: 99%

Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

2020

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Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.

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Examining the potential of open source remote sensing for building effective decision support systems for precision agriculture in resource-poor settings

Cited by 19 publications

References 64 publications

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets

Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

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