The Integrated Use of Landsat, Sentinel-2 and PlanetScope Satellite Data for Crop Monitoring

Ichikawa, D.; Wakamori, K.

doi:10.1109/igarss.2018.8519024

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Some studies analyzed the climatic impact on vegetation, especially using NDVI [13,[16][17][18]. An attempt was made to study the impact of climatic parameters such as temperature and rainfall on vegetation, see Fig.…”

Section: Correlation Analysismentioning

confidence: 99%

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Planetscope Nanosatellites Image Classification Using Machine Learning

Haq¹

2022

Computer Systems Science and Engineering

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To adopt sustainable crop practices in changing climate, understanding the climatic parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps are one of the highest resolution data that can transform agricultural practices and management on a large scale. High-resolution PS nanosatellite data was utilized in the current study to monitor agriculture's spatiotemporal assessment for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVI was utilized to assess the vegetation pattern change in the study area. The current study area has sparse vegetation, and exposed soil exhibits brightness due to low soil moisture, constraining NDVI. Therefore, a machine learning (ML) based Random Forest (RF) classification model was used to compare the vegetation extent and computational cost of NDVI. The RF model has been compared with NDVI in the current investigation. It is one of the most precise classification methods because it can model the complexity of input variables, handle outliers, treat noise effectively, and avoid overfitting. Multinomial Logistic Regression (MLR) was implemented to compare the performance of both NDVI and RFbased classification. RF model provided good accuracy (98%) for all vegetation classes based on user accuracy, producer accuracy, and kappa coefficient.

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Section: Correlation Analysismentioning

confidence: 99%

“…[17] use PS data to map Striga weed based on RF. [18] used PS data with other satellite data to monitor crop growth. [19] used ML-based classification to make landuse maps based on PS data and observed an accuracy between 87% and 96%.…”

Section: Introductionmentioning

confidence: 99%

Planetscope Nanosatellites Image Classification Using Machine Learning

Haq¹

2022

Computer Systems Science and Engineering

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“…Pan-sharpening followed a principle-component analysis (PCA) method using the RStoolbox package in R (Leutner et al, 2019). PCA pansharpening is appropriate because PlanetScope imagery is spectrally compatible with Sentinel-2 sensors (Ichikawa and Wakamori, 2018).…”

Section: Vegetative Indicesmentioning

confidence: 99%

Remote Sensing Reveals Lasting Legacies of Land-Use by Small-Scale Foraging Communities in the Southwestern Indian Ocean

Davis

Douglass

2021

Front. Ecol. Evol.

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Archaeologists interested in the evolution of anthropogenic landscapes have productively adopted Niche Construction Theory (NCT), in order to assess long-term legacies of human-environment interactions. Applications of NCT have especially been used to elucidate co-evolutionary dynamics in agricultural and pastoral systems. Meanwhile, foraging and/or highly mobile small-scale communities, often thought of as less intensive in terms of land-use than agropastoral economies, have received less theoretical and analytical attention from a landscape perspective. Here we address this lacuna by contributing a novel remote sensing approach for investigating legacies of human-environment interaction on landscapes that have a long history of co-evolution with highly mobile foraging communities. Our study is centered on coastal southwest Madagascar, a region inhabited by foraging and fishing communities for close to two millennia. Despite significant environmental changes in southwest Madagascar’s environment following human settlement, including a wave of faunal extinctions, little is known about the scale, pace and nature of anthropogenic landscape modification. Archaeological deposits in this area generally bear ephemeral traces of past human activity and do not exhibit readily visible signatures of intensive land-use and landscape modification (e.g., agricultural modifications, monumental architecture, etc.). In this paper we use high-resolution satellite imagery and vegetative indices to reveal a legacy of human-landscape co-evolution by comparing the characteristics – vegetative productivity and geochemical properties – of archaeological sites to those of locations with no documented archaeological materials. Then, we use a random forest (RF) algorithm and spatial statistics to quantify the extent of archaeological activity and use this analysis to contextualize modern-day human-environment dynamics. Our results demonstrate that coastal foraging communities in southwest Madagascar over the past 1,000 years have extensively altered the landscape. Our study thus expands the temporal and spatial scales at which we can evaluate human-environment dynamics on Madagascar, providing new opportunities to study early periods of the island’s human history when mobile foraging communities were the dominant drivers of landscape change.

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“…S. Yuval et al [10] developed a methodology for determining the sowing date on a field scale from PlanetScope images when sowing without plow cultivation. D. Ichikawa et al [11] developed the operational processing system for Landsat-8, Sentinel-2, and PlanetScope satellite data to complex use for monitoring crop growth. The satellite data in these studies were pre-processed and corrected spectral bands were adjusted for the integration of different sensor data.…”

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confidence: 99%

Application of nanosatellites PlanetScope data to monitor crop growth

Kokhan¹,

Vostokov²

2020

E3S Web Conf.

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In this research, an approach to monitor crop growth and development is presented using time series satellite data of high spatial resolution. Monitoring of winter wheat phenology based on images of PlanetScope constellations is considered. By applying various PlanetScope data processing types and ground based GreenSeeker data, differences of NDVI values at two variants of crop fertilization are determined. In particular, the following approaches were used in the research: obtaining the Top of Atmosphere Reflectance (TOA), the Planet Surface Reflectance (SR), and receiving NDVI image in Python using a Rasterio module. It was estimated that NDVI values derived from the surface reflectance imagery were significantly correlated to the ground data of a manual active GreenSeeker optical sensor (p < 0.05). The proposed simplified technique, based on PlanetScope NDVI time series, demonstrates the possibilities to monitor temporal changes in crop growth.

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The Integrated Use of Landsat, Sentinel-2 and PlanetScope Satellite Data for Crop Monitoring

Cited by 5 publications

References 2 publications

Planetscope Nanosatellites Image Classification Using Machine Learning

Planetscope Nanosatellites Image Classification Using Machine Learning

Remote Sensing Reveals Lasting Legacies of Land-Use by Small-Scale Foraging Communities in the Southwestern Indian Ocean

Application of nanosatellites PlanetScope data to monitor crop growth

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