Finish theme 4

4-smart-agriculture.md

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+# Smart agriculture
+
+## Present state and history
+
+- Urban smart agriculture
+- Evolution of agriculture
+- Smart agriculture: agriculture with ICT
+- Benefits
+    - Informed decision
+    - In depth analysis
+    - All kinds of monitoring, supply chain, crop, water, environmental1
+        - Crop monitoring: the systematic observation and assessment of crops
+          throughout their growth cycle to improve productivity and make
+          informed decisions
+        - Water management: optimize crop yield while minimizing waste of water
+          resource
+        - Plant disease identification: helps in crop yield improvement
+        - Precision agriculture: promise better yield, and less water and
+          fertizers
+        - Environment monitoring: schedule irrigation, and crop protection for
+          bad weather
+- Data sources
+    - Terrestial network
+    - LAP layer
+    - HAP layer
+    - Sattleite layer
+- Using AI to automate and interpret data: ML, DL
+- Challenges:
+    - Data security
+    - Network
+    - Device threats
+    - Privacy
+
+## Summaey
+
+- Earth observations are time series data that offer intelligence to climate and
+  agriculture to make these smart.
+    - Rely on connected airborne platforms and surface based platforms
+- Climate study is connected with change detection
+- Hard to determine change agent
+- Smart agriculture: the most promising to benefit from Earth obervation
+- 7 areas:
+    - Supply chain
+    - Crop monitoring
+    - Water management
+    - Precision agriculture
+    - Environment monitoring
+    - Soil health monitoring
+    - Livestock management
+- Generate big data, hard for manual use, but good for AI

4-smart-climate.md

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+# Smart climate
+
+## Definition
+
+- Climate Data Record: Data to make climate smart:
+    - Time series of measurements
+    - Length, consistency, continuity
+    - Determine climate variability, and change
+- To monitor changes in order to predict / mitiagte the consequences
+- Variability:
+    - El Nino
+    - La Nina
+- Land disturbance: A **event** that triggers disrupts in ecosystems, community
+  or population structure, and changes resources, substance availability or
+  physical environment
+- Climate change: global warming
+- Succession
+    - Process that the structure of a biological community changes over time
+
+## Reason
+
+- Animal-based food production emits GCG
+- Climate change
+- Areas affected by desertification
+
+## Implementation
+
+- Before smart climate
+    - Data collected and managed by governments
+    - Greenhouse gas (GCG) emission relies on self reporting
+        - Calculated based on known fuel consumption
+    - Data is sparse in space and time, also incomplete
+- Earth observation: acquire data from a variety of sensors
+    - Remote sensing:
+        - Capture images in a spectrum
+        - Classify land coverage and use, incl. change over time
+        - Measure the geometry of natural and human made objects
+        - Identify and differenciate species of vegetation
+- Carbon Dioxide Removal (CDR)
+- Change agent characterization:
+    - Change agent: a driver or factor of change
+    - Direct or proxiamte causes
+    - Distal or underlying driving forces
+    - Attribution:
+        - Can happen simultaneously or in proximity
+        - Result in change
+        - Challenging to collect high quality change agent
+
+## Examples
+
+### Detecting fire with remote sensing
+
+### Detecting change agent
+#### Using random forest
+
+### Detecting deforestation with remote sensing
+
+### Frequency of observations
+
+
+
+## Collaborate for better EO information
+- data fusion proces
+    - Input: Hetereogeneous data 
+    - Process: data alignment and data / object correlation
+    - Intemediate output: Alighed and correlated data
+    - Process: Attribute or identity estimation
+    - Result: fusion data
+- Obervation level, feature level, decision level