This utility is largely obsolete for newer versions of Hexagon/Intergraph software, which have moved to more modern, cloud-based, or software-only licensing systems Important Note:
An LND emulator utility is not a silver bullet. You must understand its constraints:
If you're exploring or developing on the LND (Lightning Network Daemon) stack, the can be a game changer for testing, learning, and prototyping without touching real bitcoin or mainnet channels.
import mockLnd from 'mock-lnd'; const lndMock = mockLnd.makeLnd( getInfo: () => Promise.resolve( alias: "test-node" ) );
An is a software tool designed to mimic the behavior, APIs, and network interactions of the Lightning Network Daemon (LND) in a controlled, local environment. Instead of connecting to the real Bitcoin blockchain and the live P2P Lightning network, the emulator provides a "sandbox" that simulates these conditions. Commonly used emulators in the LND ecosystem include: Lndmon: A monitoring and emulation tool by Lightning Labs.
: Users typically copy the executable and related system files (like HardlockFilter.sys C:\Windows\System32 directory and run the utility to "Install Emulator".
: Stress-test your node's ability to handle high volumes of concurrent payments or large channel databases. Key Use Cases LApp Development
Most LND emulator utilities are distributed as Docker images or lightweight Node.js/Go binaries. Follow this guide to set up a standard mock LND environment. 1. Prerequisites Ensure you have the following installed on your machine: Docker and Docker Compose Node.js (if using a JavaScript-based tool) or Go 2. Running via Docker
Happy testing!
Developers can instantly simulate channel openings, force-closures, routing failures, and variable fee rates through a command-line interface (CLI) or a configuration file.
If you want to understand how an LND emulator utility functions without using a wrapper like Polar, you can build a basic bash script utility using Docker. Here is how to configure a minimal environment. Prerequisites
Building on the Lightning Network can feel like a high-stakes game. When you’re dealing with real-time payment channels and cryptographic security, a single bug in your code can lead to lost funds. This is where the LND emulator utility becomes a developer’s best friend. What is the LND Emulator?
High-quality LND emulation utilities offer a suite of features designed to replicate production environments accurately:
: Send a request to the emulator's payment endpoint to simulate a user paying the invoice.
You can customize the behavior of the emulator using environmental flags:
This utility is largely obsolete for newer versions of Hexagon/Intergraph software, which have moved to more modern, cloud-based, or software-only licensing systems Important Note:
An LND emulator utility is not a silver bullet. You must understand its constraints:
If you're exploring or developing on the LND (Lightning Network Daemon) stack, the can be a game changer for testing, learning, and prototyping without touching real bitcoin or mainnet channels.
import mockLnd from 'mock-lnd'; const lndMock = mockLnd.makeLnd( getInfo: () => Promise.resolve( alias: "test-node" ) );
An is a software tool designed to mimic the behavior, APIs, and network interactions of the Lightning Network Daemon (LND) in a controlled, local environment. Instead of connecting to the real Bitcoin blockchain and the live P2P Lightning network, the emulator provides a "sandbox" that simulates these conditions. Commonly used emulators in the LND ecosystem include: Lndmon: A monitoring and emulation tool by Lightning Labs.
: Users typically copy the executable and related system files (like HardlockFilter.sys C:\Windows\System32 directory and run the utility to "Install Emulator".
: Stress-test your node's ability to handle high volumes of concurrent payments or large channel databases. Key Use Cases LApp Development
Most LND emulator utilities are distributed as Docker images or lightweight Node.js/Go binaries. Follow this guide to set up a standard mock LND environment. 1. Prerequisites Ensure you have the following installed on your machine: Docker and Docker Compose Node.js (if using a JavaScript-based tool) or Go 2. Running via Docker
Happy testing!
Developers can instantly simulate channel openings, force-closures, routing failures, and variable fee rates through a command-line interface (CLI) or a configuration file.
If you want to understand how an LND emulator utility functions without using a wrapper like Polar, you can build a basic bash script utility using Docker. Here is how to configure a minimal environment. Prerequisites
Building on the Lightning Network can feel like a high-stakes game. When you’re dealing with real-time payment channels and cryptographic security, a single bug in your code can lead to lost funds. This is where the LND emulator utility becomes a developer’s best friend. What is the LND Emulator?
High-quality LND emulation utilities offer a suite of features designed to replicate production environments accurately:
: Send a request to the emulator's payment endpoint to simulate a user paying the invoice.
You can customize the behavior of the emulator using environmental flags:
Data Dictionary: USDA National Agricultural Statistics Service, Cropland Data Layer
Source: USDA National Agricultural Statistics Service
The following is a cross reference list of the categorization codes and land covers.
Note that not all land cover categories listed below will appear in an individual state.
Raster
Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0
Categorization Code Land Cover
"0" Background
Raster
Attribute Domain Values and Definitions: CROPS 1-60
Categorization Code Land Cover
"1" Corn
"2" Cotton
"3" Rice
"4" Sorghum
"5" Soybeans
"6" Sunflower
"10" Peanuts
"11" Tobacco
"12" Sweet Corn
"13" Pop or Orn Corn
"14" Mint
"21" Barley
"22" Durum Wheat
"23" Spring Wheat
"24" Winter Wheat
"25" Other Small Grains
"26" Dbl Crop WinWht/Soybeans
"27" Rye
"28" Oats
"29" Millet
"30" Speltz
"31" Canola
"32" Flaxseed
"33" Safflower
"34" Rape Seed
"35" Mustard
"36" Alfalfa
"37" Other Hay/Non Alfalfa
"38" Camelina
"39" Buckwheat
"41" Sugarbeets
"42" Dry Beans
"43" Potatoes
"44" Other Crops
"45" Sugarcane
"46" Sweet Potatoes
"47" Misc Vegs & Fruits
"48" Watermelons
"49" Onions
"50" Cucumbers
"51" Chick Peas
"52" Lentils
"53" Peas
"54" Tomatoes
"55" Caneberries
"56" Hops
"57" Herbs
"58" Clover/Wildflowers
"59" Sod/Grass Seed
"60" Switchgrass
Raster
Attribute Domain Values and Definitions: NON-CROP 61-65
Categorization Code Land Cover
"61" Fallow/Idle Cropland
"62" Pasture/Grass
"63" Forest
"64" Shrubland
"65" Barren
Raster
Attribute Domain Values and Definitions: CROPS 66-80
Categorization Code Land Cover
"66" Cherries
"67" Peaches
"68" Apples
"69" Grapes
"70" Christmas Trees
"71" Other Tree Crops
"72" Citrus
"74" Pecans
"75" Almonds
"76" Walnuts
"77" Pears
Raster
Attribute Domain Values and Definitions: OTHER 81-109
Categorization Code Land Cover
"81" Clouds/No Data
"82" Developed
"83" Water
"87" Wetlands
"88" Nonag/Undefined
"92" Aquaculture
Raster
Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195
Categorization Code Land Cover
"111" Open Water
"112" Perennial Ice/Snow
"121" Developed/Open Space
"122" Developed/Low Intensity
"123" Developed/Med Intensity
"124" Developed/High Intensity
"131" Barren
"141" Deciduous Forest
"142" Evergreen Forest
"143" Mixed Forest
"152" Shrubland
"176" Grassland/Pasture
"190" Woody Wetlands
"195" Herbaceous Wetlands
Raster
Attribute Domain Values and Definitions: CROPS 195-255
Categorization Code Land Cover
"204" Pistachios
"205" Triticale
"206" Carrots
"207" Asparagus
"208" Garlic
"209" Cantaloupes
"210" Prunes
"211" Olives
"212" Oranges
"213" Honeydew Melons
"214" Broccoli
"215" Avocados
"216" Peppers
"217" Pomegranates
"218" Nectarines
"219" Greens
"220" Plums
"221" Strawberries
"222" Squash
"223" Apricots
"224" Vetch
"225" Dbl Crop WinWht/Corn
"226" Dbl Crop Oats/Corn
"227" Lettuce
"228" Dbl Crop Triticale/Corn
"229" Pumpkins
"230" Dbl Crop Lettuce/Durum Wht
"231" Dbl Crop Lettuce/Cantaloupe
"232" Dbl Crop Lettuce/Cotton
"233" Dbl Crop Lettuce/Barley
"234" Dbl Crop Durum Wht/Sorghum
"235" Dbl Crop Barley/Sorghum
"236" Dbl Crop WinWht/Sorghum
"237" Dbl Crop Barley/Corn
"238" Dbl Crop WinWht/Cotton
"239" Dbl Crop Soybeans/Cotton
"240" Dbl Crop Soybeans/Oats
"241" Dbl Crop Corn/Soybeans
"242" Blueberries
"243" Cabbage
"244" Cauliflower
"245" Celery
"246" Radishes
"247" Turnips
"248" Eggplants
"249" Gourds
"250" Cranberries
"254" Dbl Crop Barley/Soybeans