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11717701_km030_26_ashish-kumar-satellite-farming-using-remote-sensing-images-drone-images-

Get Input Data The input data was encoded into CSV files. The X_test_sat4.csv flattened the images that were 28 x 28 x 4 that were taken from space. The first three channels are the standard red, green, and blue channels in normal images. The 4th is a near-infrared band. We are using the smaller test set because the training set is too big. After extracting the data from the csv files, we can reshape it into the original images. Then, we can see the images before we train on them. The second file we are loading are the labels for each image. They can be one of 4: barren land, trees, grassland and other. Each row in the file looks like this [1,0,0,0], where only one of the 4 value is 1. If it is one, then it is that class respective to the order I showed above. If it was the above values, the image is a picture of barren land. If it was [0,1,0,0], then it would be trees. If it was [0,0,1,0], then it would be grassland and so on.

2015_schneider_kefi

Online Supplementary Material: 'Spatially heterogeneous pressure raises risk of catastrophic shifts'

2017_thiery_etal_jgr

scripts used for the following publication: Thiery, et al., 2017 JGR

2018-hindsight

A repository containing the source code for the paper "HINDSIGHT: An R-Based Framework Towards Long Short Term Memory (LSTM) Optimization"

2019_reply_matters_arising_nature-communications

Data and codes for the reply to matters arising for "causal decomposition in mutual causation system"

2020_opengeohub

Slides and code for my mapview tutorial at OpenGeoHub Summer School 2020

a-deep-learning-based-app

In this repository i show a app in which a shiny R app interfaces with the python language, by using the keras model to both predict a plant species as well as if they are sick or healthy, the deep learning process was trained using the tensorflow backend and the app expects a jpg image of a plant

abdiv

Alpha and beta diversity measures for community ecology

acm_gpp_et

The Aggregated Canopy Model

acycle

Acycle: Time-series analysis software for paleoclimate research and education

adams-et-al-2020-ncc

Code and data for paper by Adams et al 2020, Nature Climate Change

adgvm1_ccam

aDGVM1 with with CCAM downscaled GCM daily input data

administrative-divisions-of-china

中华人民共和国行政区划：省级（省份直辖市自治区）、 地级（城市）、 县级（区县）、 乡级（乡镇街道）、 村级（村委会居委会） ，**省市区镇村二级三级四级五级联动地址数据 Node.js 爬虫。

adv-python-for-gis-and-rs

Ideas for a more advanced Python class for GIS and Remote Sensing, https://gbrunner.github.io/Advanced_Python_for_GIS_and_RS/

aed

Package accompanying 2009 book by Zuur et. al. (Mixed Effects Models and Extensions in Ecology with R)

aeronetlib

A Remote Sensing data handling library for Deep Learning

africa_future_hydroclimate

The code processes CMIP5 projections of hydroclimatic parameters (e.g. precipitation, runoff, soil moisture) and calculate hydroclimatic change for the 50 largest African basins from 1960-1990 to 2070-2100

africa_poverty

africasoilnutrients

Methodology for producing spatial predictions of soil nutrients for SubSaharan Africa

ag_ecosopt

Ag_EcoSOpt is an open-source software-based decision-support tool that incorporates the concepts of ecosystem modelling, ecosystem services tradeoffs, economic valuation and optimization, and land management and land use change decision making in agricultural production systems. It has two main components with several attached modules. The ecological qualification component is used to quantify changes in ecosystem functions (e.g. greenhouse gas emissions, soil organic matter, water loss, nitrogen leaching, crop yield, and crop residue) in response to changes in human decision making on land management practices or land uses. It integrates two core models: (1) DAYCENT, a biogeochemical model created by the Natural Resources and Ecology Laboratory at Colorado State University; and (2) The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET) created by Argonne National Laboratory. The optimization component includes a spreadsheet-based module or standalone MATLAB application for farm-gate optimization or supply chain optimization of end use products (e.g. grain for cattle feed or biofuel, biomass for electricity or biofuel). Ag_EcoSOpt supports multilevel decision making for crop lands in the United States such as individual farms or landscape, user-defined time frames (1-50 years), and farm-gate or supply chain analysis.

agcurve

R functions for calculating AG-curve from x,y coordinate data

agriwater

an R package for energy balance and actual evapotranspiration retrieving using satellite images and agrometeorological data

agro

Machine learning algorithms for spatial agriculture remote sensed data

alaskashrubs

Dendroecological anlaysis of climate and herbivore impacts on shrub growth in northeastern Alaska.

alphashape3d

The package alphashape3d presents the implementation in R of the alpha-shape of a finite set of points in the three-dimensional space.

an-experimental-approach-to-ecosystem-engineers-biodiversity-and-ecosystem-functions

R script for the study "An experimental approach to assessing the impact of ecosystem engineers on biodiversity and ecosystem functions" published in Ecology by Gianalberto Losapio and colleagues

anamalai_para_git

Parasite community ecology data, analysis and publications

anderegg_ecollet_intraspecificles

Code for analyses contained in Anderegg LDL, Berner LT, Badgley G, Sethi ML, Law BE, HilleRisLamber J (2018) Within-species patterns challenge our understanding of the Leaf Economics Spectrum. Ecology Letters

ann

ANN Matlab code associated with land degradation analysis

anticipating_species_distribution_stoten_sm1

The R code of the model in https://doi.org/10.1016/j.scitotenv.2016.12.038