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googleearthengine

Google Earth Engine Tutorial

googleearthengine-global-forest-change

Results from time-series analysis of Landsat images characterizing forest extent and change. Trees are defined as vegetation taller than 5m in height and are expressed as a percentage per output grid cell as ‘2000 Percent Tree Cover’. ‘Forest Cover Loss’ is defined as a stand-replacement disturbance, or a change from a forest to non-forest state, during the period 2000–2016. ‘Forest Cover Gain’ is defined as the inverse of loss, or a non-forest to forest change entirely within the period 2000–2012. ‘Forest Loss Year’ is a disaggregation of total ‘Forest Loss’ to annual time scales. Reference 2000 and 2016 imagery are median observations from a set of quality assessment-passed growing season observations.

greeninghub

This is a GitHub repository for 'Complexity revealed in the greening of the Arctic'.

ieee_spider

IEEE网站UHF RFID论文爬虫

ilwisobjects

image-downloader

Download images from Google, Bing, Baidu. 谷歌、百度、必应图片下载.

imageprocessing

Assignments of Image processing in Physics TUM SS2019

insar-simulation-and-studies

Some work I did during my graduate studies (Matlab Code) : (2) InSAR Simulation and Studies.

isce2

isce2-1

InSAR Scientific Computing Environment version 2

isce2-docs

Documents and tutorials for isce2

kapok

Kapok: Python Library for PolInSAR Forest Height Estimation Using UAVSAR Data

landsat-extract-gee

Get Landsat surface reflectance time-series from google earth engine

landtrendr-2012

LandTrendr Code (from /projectnb/trenders/code/LandTrendr2012)

lantern-vpn

PC蓝灯破解版在这里,重启程序重置500M流量,屏蔽自动更新,永久使用

lastools

efficient tools for LiDAR processing

learning-geospatial-analysis-with-python-third-edition

Learning Geospatial Analysis with Python – Third Edition, published by Packt

libsvm

lidar

Terrain and hydrological analysis using LiDAR-derived digital elevation models (DEM)

lidar-heights

Extract building heights using building footprints and lidar data with postgis

lidr

R package for airborne LiDAR data manipulation and visualisation for forestry application

lisflood-code

Lisflood OS (LISFLOOD)

lpj-wsl_v2.0

Dynamic global vegetation model with land use and land cover change and forest demography.

lt-gee

Google Earth Engine implementation of the LandTrendr spectral-temporal segmentation algorithm. For documentation see:

maap-documentation

machine-learning

Machine learning

maespa.github.io

Maespa and Maestra manuals

mann-kendall-trend

Simple python function to run Mann-Kendall Test for Trend

mannkendall_sen_rainfall

Mann Kendall Test and Sen's Slope Esitmate on Daily Precipitation Raster Data

mapping-colombia-wetlands