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プロフィール詳細
プロジェクトを作成
★★★★★
☆☆☆☆☆
Gachoki S.に依頼
Kenya

Freelance Geospatial and remote sensing analyst

プロフィール概要
専門分野
サービス
Writing Technical Writing
Research Gap Analysis, Scientific and Technical Research, Systematic Literature Review
Consulting Scientific and Technical Consulting
Data & AI Predictive Modeling, Statistical Analysis, Image Processing, Image Analysis, Big Data Analytics, Data Mining, Data Cleaning, Data Processing
職務経験

University of Twente

- 現在

DRIP Scholar

International Centre of Insect Physiology and Ecology

9月 2019 - 現在

GIS Consultant

Altai consulting

5月 2022 - 5月 2023

Senior Research Assistant

International Centre of Insect Physiology and Ecology

6月 2019 - 8月 2019

Research Assistant

International Centre of Insect Physiology and Ecology

4月 2018 - 5月 2019

GIS assistant

International Livestock Research Institute

2月 2016 - 8月 2016

学歴

PhD Candidate (Spatial ecology and epidemiology) (Natural Resources (NRS))

Universiteit Twente Faculteit Geo-Informatie Wetenschappen en Aardobservatie

10月 2019 - 現在

Master of Science in Geo-Information Science and Earth Observation (Natural Resource Management (NRM))

Universiteit Twente Faculteit Geo-Informatie Wetenschappen en Aardobservatie

9月 2016 - 3月 2018

BSc. Geospatial Information Science (IGGRES)

Dedan Kimathi University of Technology

9月 2011 - 5月 2015

認定資格

  • Strategic Planning Professional

    Association for Strategic Planning

    2月 2023 - 現在

出版物
JOURNAL ARTICLE
Stella Gachoki, Thomas Groen, Anton Vrieling, Michael Okal, Andrew Skidmore, Daniel Masiga (2021). Satellite-based modelling of potential tsetse (Glossina pallidipes) breeding and foraging sites using teneral and non-teneral fly occurrence data . Parasites & Vectors.
Marian Adan and Elfatih M. Abdel-Rahman and Stella Gachoki and Beatrice W. Muriithi and H. Michael G. Lattorff and Vivian Kerubo and Tobias Landmann and Samira A. Mohamed and Henri E.Z. Tonnang and Thomas Dubois(2021). Use of earth observation satellite data to guide the implementation of integrated pest and pollinator management (IPPM) technologies in an avocado production system . Remote Sensing Applications: Society and Environment. 23. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 100566. Elsevier {BV}
Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2 @article{dde20ca801b943119cd5dc65289b6b47, title = "Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2", abstract = "The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growing seasons. In semi-arid East Africa however, vegetation responds rapidly to a concentration of rainfall over short periods and consequently is subject to strong interannual variability. Obtaining a sufficient density of cloud-free acquisitions to accurately describe these short vegetation cycles is therefore challenging. The objective of this study is to evaluate if data from two satellite constellations, i.e., PlanetScope (3 m resolution) and Sentinel-2 (10 m resolution), each independently allow for accurate mapping of vegetation phenology under these challenging conditions. The study area is a rangeland with bimodal seasonality located at the 128-km2 Kapiti Farm in Machakos County, Kenya. Using all the available PlanetScope and Sentinel-2 imagery between March 2017 and February 2019, we derived temporal NDVI profiles and fitted double hyperbolic tangent models (equivalent to commonly-used logistic functions), separately for the two rainy seasons locally referred to as the short and long rains. We estimated start- and end-of-season for the series using a 50% threshold between minimum and maximum levels of the modelled time series (SOS50/EOS50). We compared our estimates against those obtained from vegetation index series from two alternative sources, i.e. a) greenness chromatic coordinate (GCC) series obtained from digital repeat photography, and b) MODIS NDVI. We found that both PlanetScope and Sentinel-2 series resulted in acceptable retrievals of phenology (RMSD of ~8 days for SOS50 and ~15 days for EOS50 when compared against GCC series) suggesting that the sensors individually provide sufficient temporal detail. However, when applying the model to the entire study area, fewer spatial artefacts occurred in the PlanetScope results. This could be explained by the higher observation frequency of PlanetScope, which becomes critical during periods of persistent cloud cover. We further illustrated that PlanetScope series could differentiate the phenology of individual trees from grassland surroundings, whereby tree green-up was found to be both earlier and later than for grass, depending on location. The spatially-detailed phenology retrievals, as achieved in this study, are expected to help in better understanding climate and degradation impacts on rangeland vegetation, particularly for heterogeneous rangeland systems with large interannual variability in phenology and productivity.", keywords = "ITC-ISI-JOURNAL-ARTICLE, ITC-HYBRID, UT-Hybrid-D", author = "Yan Cheng and A. Vrieling and Francesco Fava and Michele Meroni and M. Marshall and S.M. Gachoki", year = "2020", month = oct, doi = "10.1016/j.rse.2020.112004", language = "English", volume = "248", pages = "1--20", journal = "Remote sensing of environment", issn = "0034-4257", publisher = "Elsevier", } . Remote sensing of environment.
(2020). Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2 . Remote Sensing of Environment.