Landscape Coffee Yield

Exploring the vast landscape of my PhD research journey, where the cultivation of knowledge meets the art of yielding impactful insights. Navigating the complexities with precision, much like the delicate process of growing the perfect blend. Join me in this academic odyssey where innovation and yield converge, shaping a landscape of discovery. This paragraph is not mine, it is from Chatgpt. I would write something like this is repository where I will store all my workflow for landscape and yield prediction for coffee crop. In this file you will find all the references and methodoloy that I will use. Enjoy :)

Description

This repository contains all scripts and workflows for the analysis landscape.

Here is the workflow for analysis:

Data

Download land use and land cover (LULC) https://code.earthengine.google.com/b99f1904c7a5c5902fa17608e8a08e32 analysis data with Google Earth. Options for downloading this information:

Satellite	Spatial Resolution	Revisit Time	General Description
Landsat 8	30m (visible and IR), 15m (panchromatic)	16 days	Free (USGS), operated by NASA/USGS
Sentinel-2	10m (visible and NIR), 20m (red-edge), 60m (SWIR)	5 days	Free (ESA), operated by ESA
MODIS	250m (bands 1-2), 500m (bands 3-7), 1km (bands 8-36)	Daily (Terra and Aqua)	Free (NASA), operated by NASA
WorldView-2	46cm (panchromatic), 1.8m (multispectral)	Varies, typically several days	Commercial (DigitalGlobe), operated by Maxar Technologies
WorldView-3	31cm (panchromatic), 1.24m (multispectral)	Varies, typically several days	Commercial (DigitalGlobe), operated by Maxar Technologies

For (LULC), we have

Dataset	Satellite	Resolution	Description
Dynamic World	Sentinel-2	10m	Near-real-time (NRT) Land Use/Land Cover (LULC) dataset. Includes class probabilities and label information for nine classes.
MCD12Q1.061	MODIS Terra and Aqua	500m	MODIS Land Cover Type Yearly Global dataset at 500m resolution.

Based on the plot analysis, the MODIS is suitable for this case. For this we have the following options:

LC_Type1 (Annual IGBP classification):

Classes include various types of forests, shrublands, grasslands, wetlands, croplands, urban areas, and more.
Each class has a specific color, and you can choose the one that aligns with the land cover type you are interested in.

LC_Type2 (Annual UMD classification):

Similar to LC_Type1, but with different class definitions and colors.

LC_Type3 (Annual Leaf Area Index classification):

Includes grasslands, shrublands, croplands, forests, wetlands, and urban areas.

LC_Type4 (Annual BIOME-BGC classification):

Differentiates between vegetation types, croplands, and non-vegetated lands.

LC_Type5 (Annual Plant Functional Types classification):

Represents different types of vegetation, including trees, shrubs, grasses, and croplands.

The IGBP (International Geosphere-Biosphere Programme) classification is a system used to categorize land cover types based on their characteristics. It was developed by the IGBP, an international scientific research program that aimed to study global-scale interactions between Earth's biological, chemical, and physical processes.

The IGBP land cover classification system categorizes different types of land cover, including natural vegetation, agricultural areas, urban areas, and more. Each land cover type is assigned a specific class code and label. This classification system has been widely used in remote sensing and environmental research to characterize and analyze the Earth's surface.

The classes in the IGBP classification system cover a broad range of land cover types, providing a comprehensive framework for understanding the distribution of different ecosystems and land uses on a global scale. For example, classes may include evergreen forests, deciduous forests, shrublands, grasslands, croplands, wetlands, and urban areas.

Researchers and scientists use the IGBP classification to interpret satellite imagery, conduct land cover change analysis, and understand the distribution of different land cover types across regions and continents. The classification system is a valuable tool for studying and monitoring the dynamics of the Earth's surface and its changes over time.

Value	Color	Description
1		Evergreen Needleleaf Forests: dominated by evergreen conifer trees (canopy >2m). Tree cover >60%.
2		Evergreen Broadleaf Forests: dominated by evergreen broadleaf and palmate trees (canopy >2m). Tree cover >60%.
3		Deciduous Needleleaf Forests: dominated by deciduous needleleaf (larch) trees (canopy >2m). Tree cover >60%.
4		Deciduous Broadleaf Forests: dominated by deciduous broadleaf trees (canopy >2m). Tree cover >60%.
5		Mixed Forests: dominated by neither deciduous nor evergreen (40-60% of each) tree type (canopy >2m). Tree cover >60%.
6		Closed Shrublands: dominated by woody perennials (1-2m height) >60% cover.
7		Open Shrublands: dominated by woody perennials (1-2m height) 10-60% cover.
8		Woody Savannas: tree cover 30-60% (canopy >2m).
9		Savannas: tree cover 10-30% (canopy >2m).
10		Grasslands: dominated by herbaceous annuals (<2m).
11		Permanent Wetlands: permanently inundated lands with 30-60% water cover and >10% vegetated cover.
12		Croplands: at least 60% of area is cultivated cropland.
13		Urban and Built-up Lands: at least 30% impervious surface area including building materials, asphalt and vehicles.
14		Cropland/Natural Vegetation Mosaics: mosaics of small-scale cultivation 40-60% with natural tree, shrub, or herbaceous vegetation.
15		Permanent Snow and Ice: at least 60% of area is covered by snow and ice for at least 10 months of the year.
16		Barren: at least 60% of area is non-vegetated barren (sand, rock, soil) areas with less than 10% vegetation.
17		Water Bodies: at least 60% of area is covered by permanent water bodies.

There is a parameter to measure the amount of clouds,CLOUDY_PIXEL_PERCENTAGE = 10% (only 2 images for Quindio that meet with this condition and 16 min for downloading), in the zone. In this case, the umbral is 100%, which means that the zone is basically covered by clouds. In Quindio department, they surveys were collected between July and November 2022.

The bands are ['B4', 'B3', 'B2']. According to chatGPT, Land Use and Land Cover (LULC) classification often involves using different combinations of spectral bands to distinguish between different land cover types. While true-color RGB imagery is useful for visualization, for LULC classification, you typically use bands that are sensitive to different land cover characteristics.

For Sentinel-2, common band combinations used for LULC classification include:

Natural Color (RGB): Bands 4 (Red), 3 (Green), and 2 (Blue). False Color Infrared (FCIR): Bands 8 (NIR), 4 (Red), and 3 (Green). Normalized Difference Vegetation Index (NDVI): Bands 8 (NIR) and 4 (Red).

Read Point

Go to layer -> Add Delimited Text.
Click right then filter CRS EPSG 4326

Create Buffers Around Points:

Use the "Buffer" Tool:

Go to the "Vector" menu. Select "Geoprocessing Tools" > "Buffer." Configure Buffer Parameters:

In the Buffer dialog: Select your points layer as the "Input layer." Specify the buffer distance (e.g., 500 meters). Choose an appropriate buffer unit (e.g., meters or kilometers). Specify an output location and file name for the new buffer layer. Click "Run" to create the buffers. Adjust Buffer Settings (Optional):

Reprojecting Data to a Projected CRS:

Identify Current CRS:
- Right-click on your layer in the Layers Panel.
- Select "Properties."
- Go to the "Source" tab to see the current CRS.
Access the "Reproject" Tool:
- In the main QGIS menu, go to "Vector."
- Select "Data Management Tools" > "Reproject Layer."
Configure Reprojection Settings:
- Select your layer as the "Input layer."
- Choose the target CRS that uses meters EPSG Code: EPSG:32618
- Specify an output location and file name for the reprojected layer geopackage.
Run the Tool:
- Click "Run" to execute the tool.
Check the Result:
- Once the tool completes, add the reprojected layer to your map and check its properties to ensure it now uses a projected CRS.

Commands Linux

All process: -ps aux

State of process: -ps aux | grep PID

Run: nohup Rscript /Landscape/scripts/Quindio_FORESTHANSEN_1_UnirForestHansenyLotes.R > /results/myoutput.log 2>&1 &

Kill process: kill PID

All process: ps -u juan

Hansen Global Forest Change v1.10 (2000-2022)

Results from time-series analysis of Landsat images in characterizing global forest extent and change. The 'first' and 'last' bands are reference multispectral imagery from the first and last available years for Landsat spectral bands corresponding to red, NIR, SWIR1, and SWIR2. Reference composite imagery represents median observations from a set of quality-assessed growing-season observations for each of these bands.

Please see the User Notes for this Version 1.10 update, as well as the associated journal article: Hansen, Potapov, Moore, Hancher et al. "High-resolution global maps of 21st-century forest cover change." Science 342.6160 (2013): 850-853.

Name	Units	Min	Max	Wavelength	Description
treecover2000	%	0	100	-	Tree canopy cover for the year 2000, defined as canopy closure for all vegetation taller than 5m in height.
loss	-	-	-	-	Forest loss during the study period, defined as a stand-replacement disturbance.

MYD11A1.061 Aqua Land Surface Temperature and Emissivity Daily Global 1km

The MYD11A1 V6.1 product provides daily land surface temperature (LST) and emissivity values in a 1200 x 1200 kilometer grid. The temperature value is derived from the MYD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is the average of all qualifying observations. Provided along with both the day-time and night-time surface temperature bands and their quality indicator layers are MODIS bands 31 and 32 and six observation layers.

Thermal Infrared Land Surface Temperature (LST) Data

Resolution

1000 meters

Bands

Name	Units	Min	Max	Scale	Offset	Description
LST_Day_1km	K	7500	65535	0.02		Daytime Land Surface Temperature
QC_Day						Daytime LST Quality Indicators
Bitmask for QC_Day:
- Day_view_time	h	0	240	0.1		Local time of day observation
- Day_view_angle	deg	0	130		-65	View zenith angle of day observation
LST_Night_1km	K	7500	65535	0.02		Nighttime Land Surface Temperature
QC_Night						Nighttime LST Quality indicators
Bitmask for QC_Night:
- Night_view_time	h	0	240	0.1		Local time of night observation
- Night_view_angle	deg	0	130		-65	View zenith angle of night observation
Emis_31		1	255	0.002	0.49	Band 31 emissivity
Emis_32		1	255	0.002	0.49	Band 32 emissivity
Clear_day_cov		1	65535	0.0005		Day clear-sky coverage
Clear_night_cov		1	65535	0.0005		Night clear-sky coverage