A5079011175- Remote Sensing in Agriculture
- Smart Agriculture and AI
- Climate change impacts on agriculture
- Remote Sensing and Land Use
- Land Use and Ecosystem Services
- Agricultural Innovations and Practices
- Agricultural risk and resilience
- Soil and Land Suitability Analysis
- Rangeland Management and Livestock Ecology
- Hydrology and Drought Analysis
- Climate Change, Adaptation, Migration
- Agriculture and Rural Development Research
- Flood Risk Assessment and Management
- Remote Sensing and LiDAR Applications
- Rice Cultivation and Yield Improvement
- Remote-Sensing Image Classification
- Animal Diversity and Health Studies
- Climate variability and models
- Precipitation Measurement and Analysis
- Dermatoglyphics and Human Traits
- Island Studies and Pacific Affairs
- Tropical and Extratropical Cyclones Research
- Agricultural Economics and Practices
- Food Supply Chain Traceability
- Water-Energy-Food Nexus Studies
University of Maryland, College Park
2016-2025
Park University
2024
• Crop Monitor provides consensus crop assessments for countries at risk. The goal is to reduce ambiguity in food security decisions. Achieved through international coordination sharing of data, methods and expertise EO play key role early warning especially Early reduced production component SDG2 Zero Hunger.
With the ever-growing urgency of food insecurity and threat climate change, there is increasing interest in using artificial intelligence for Earth observations (AI-EO) agriculture, particularly Sub-Saharan Africa (SSA). This paper provides an overview primary research areas within AI-EO agriculture SSA. %(cropland crop type classification, field boundary delineation, yield estimation, pest/disease monitoring). We discuss examples limitations current opportunities future work. In addition,...
Abstract Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions food production. Such monitoring systems are essential in Africa, where insecurity is high agricultural statistics sparse. EO-based require accurate cropland maps to about croplands, but there a lack of data determine which the many available land cover most accurately identify African countries. This study provides quantitative evaluation intercomparison 11 publicly assess...
The Tigray War was an armed conflict that took place primarily in the region of northern Ethiopia from November 3, 2020 to 2, 2022. Given importance agriculture livelihoods and food security, determining impact war on cultivated area is critical. However, quantifying this difficult due restricted movement within into conflict-driven insecurity blockages. Using satellite imagery statistical estimation techniques, we assessed changes crop cultivation before during war. Our findings show...
There are many labelled datasets relating to land cover and crop type mapping that diverse geographies, agroecologies uses. However, these labels often extremely sparse, particularly in low- middle-income regions, with as few tens of examples for certain types. This makes it challenging train supervised machine learning models detect specific crops satellite observations regions. We investigate the utility model-agnostic meta-learning (MAML) learn from global improve performance data-sparse...
Climate change impacts manifest differently worldwide, with many African countries, including Senegal, being particularly vulnerable. The decline in ground observations and limited access to these continue impede research efforts understand, plan, mitigate the current future of climate change. This occurs at a time rapid growth Earth (EO) data, methodologies, computational capabilities, which could potentially augment studies data-scarce regions. In this study, we utilized satellite remote...
The preference for simple explanations, known as the parsimony principle, has long guided development of scientific theories, hypotheses, and models. Yet recent years have seen a number successes in employing highly complex models ...
Accurate, up-to-date agricultural monitoring is essential for assessing food production, particularly in countries like Kenya, where recurring climate extremes, including floods and droughts, exacerbate insecurity challenges. In regions dominated by smallholder farmers, a significant obstacle to effective the limited availability of current, detailed crop-type maps. Creating maps requires extensive field data. However, high costs associated with data collection campaigns often make them...
There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we three recent products (ESA-CCI, GlobeLand30, FROM-GC) one regional product (Tanzania Land Cover 2010 Scheme II) using validation data set that was collected by students through the Geo-Wiki tool. The ultimate aim understand usefulness of these monitoring. Data were wall-to-wall Kilosa district sample across Tanzania. results...
Spatial information on cropland distribution, often called or crop maps, are critical inputs for a wide range of agriculture and food security analyses decisions. However, high-resolution maps not readily available most countries, especially in regions dominated by smallholder farming (e.g., sub-Saharan Africa). These times crisis when decision makers need to rapidly design enact agriculture-related policies mitigation strategies, including providing humanitarian assistance, dispersing...
<title>Abstract</title> Flooding is a recognized form of natural disaster that can lead to loss life, destruction critical infrastructure with consequences impacting sectors including agriculture and health. This study aims map out flood susceptible areas within the Ala River basin Ondo State, Nigeria by integrating Analytical Hierarchy Process (AHP) Multi-Criteria Decision Analysis (MCDA) technique Support Vector Machines (SVM) Machine Learning (ML) model. Nineteen factors elevation, slope,...
Abstract Uganda is the third-largest refugee-hosting country partly due to its open-door policy—deemed one of most progressive. When new refugees arrive, refugee settlements are established rapidly, and irreversible changes landscape inevitable. We utilize satellite data map land cover (LC), use, burned area (BA) assess their relationship in context large-scale resettlement Bidi Bidi—Uganda’s largest settlement. show inevitable dramatic LC, e.g. built-up residential zones increased from 1.8%...
Purpose As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible easy to use. The purpose of this paper is demonstrate scalable replicable methods advance integrate use earth observation (EO), specifically ongoing efforts within Group on Earth Observations (GEO) Work Programme Committee Observation Satellites (CEOS) Plan, support risk-informed...
Accurate crop type maps provide critical information for ensuring food security, yet there has been limited research on classification smallholder agriculture, particularly in sub-Saharan Africa where risk of insecurity is highest. Publicly-available ground-truth data such as the newly-released training dataset types Kenya (Radiant MLHub) are catalyzing this research, but it important to understand context when, where, and how these datasets were obtained when evaluating performance using...
An innovative program focused on collaboration and capacity building is looking to improve outcomes for smallholder farmers, reduce hunger, alleviate food insecurity in sub-Saharan Africa.
The 2019–2020 Desert Locust (DL) upsurge in East Africa threatened food security for millions the region. This highlighted need to track and quantify damaging impacts of swarming insects on cropland rangelands. Satellite Earth observations (EO) data have potential contribute DL damage assessments that can inform control measures, aid distribution recovery efforts. EO complement traditional ground based surveys (which are currently further limited due COVID-19), by rapidly cost effectively...
The desired output for most real-world tasks using machine learning (ML) and remote sensing data is a set of dense predictions that form predicted map geographic region. However, prior work involving ML follows the traditional practice reporting metrics on independent, geographically-sparse samples does not perform predictions. To reduce labor producing prediction maps, we present OpenMapFlow---an open-source python library rapid creation with data. OpenMapFlow provides 1) processing...
Major strides have been made in the development of remote sensing, reanalysis, and model-based earth observations (EOs), which can be used for long-term climate variability mapping, as well real-time environmental monitoring forecasting. Such EOs are particularly valuable decision-making (e.g., resources management disaster mitigation) Eastern Southern Africa (E &amp; SA) region, where ground-based sparse. Nonetheless, operational application those to inform region remains limited. This...