News outside of mapme.biodiversity that can be of interest

[fBedecarrats]

I propose to open this thread so all contributors can post links to news that can be inspiring for the development of this package and the underlying framework.

[fBedecarrats]

@cboettig recently posted about the Source Cooperative in #186

[fBedecarrats]

This video showcases the use of wind data from the ERA5 dataset using the ecmwfr package

[cboettig]

Thanks! In this spirit, have you seen the duckdb spatial extension ? A lot of remote assets are in tabular form with lat/lon columns in csv etc. I've been exploring how we can use duckdb to access and perform spatial operations on those data without downloading them.

Here's a trivial example:

library(duckdbfs)
library(dplyr)
spatial_csv <- paste0("https://raw.githubusercontent.com/cboettig/duckdbfs/",
                     "7189ed7e0456927a55a877acf77aaadc074ba01b/",
                     "inst/extdata/spatial-test.csv") |>
  open_dataset(format = "csv") 

spatial_csv |>
  mutate(geometry = ST_Point(longitude, latitude)) |>
  to_sf()

For an ambitious example, here I try and do a spatial polygon filter on the GBIF data on AWS. This takes about 18min on my machine, but even a non-spatial filter, e.g. by bounding box, takes about 8min since GBIF is quite large. (This uses my thin wrapper around duckdb to streamline some of the syntax).

library(dplyr)
library(sf)
library(spData)
library(duckdbfs) #  remotes::install_github("cboettig/duckdbfs") 

# let's say we have some polygon to filter by:
costa_rica <- world |> filter(grepl("Costa Rica", name_long)) |> pull(geom) |> st_as_text()

# connect to the dataset
gbif <- duckdbfs::open_dataset("s3://gbif-open-data-us-east-1/occurrence/2022-12-01/occurrence.parquet", s3_region="us-east-1")

# Create a geometry column from the point data, then use it in filters
cr_species <- 
  gbif |> 
  mutate(geometry = ST_Point(decimallongitude, decimallatitude)) |>  
  filter(class == "Mammalia") |>
  filter(ST_Within(geometry, ST_GeomFromText({costa_rica}))) |>
  to_sf()

Haven't tested this that extensively, but curious what you think and if it could be relevant here. The docs for the spatial extension have richer examples and notes on all the supported spatial functions

[goergen95]

Iconnect is an R package to analyse landscape connectivity that also works with vector data.

[goergen95]

Chelsea is a high-resolution (~1km) climatology data set with a very high number of pre-computed variables.

[goergen95]

WorldCereal, a dynamic, 10m crop and irrigation product developed by ESA. Might be interesting for agricultural use-cases.

[fBedecarrats]

That looks great! For now it seems that they only have 2021, right? I am a bit surprised by the agro-ecological zones that they used to stratify the analysis: they seem really coarse to me (eg. only one zone for Madagascar when there are at least 3 different climates with very disting croping calendars in Madagascar...).

[goergen95]

Clay is a foundational model of Earth. It uses a Vision Transformer architecture adapted to 
understand geospatial and temporal relations on Earth Observation data. The model is 
trained via Self-supervised learning (SSL) using a Masked Autoencoder (MAE) method.

The Clay model can be used in three main ways:

   - Generate semantic embeddings for any location and time.

   - Fine-tune the model for downstream tasks such as classification, regression, and generative tasks.

   - Use the model as a backbone for other models.

https://clay-foundation.github.io/model/

[goergen95]

ACLED released a conflict exposure tool based on WorldPop. Could be an inspiration for a new indicator with UCDP GED. https://www.worldpop.org/blog/conflict-exposure-a-new-tool-to-measure-the-impact-of-conflict/

[goergen95]

Copernicus Marine Data Store

[goergen95]

New portal with WAPOR V3 released.

[goergen95]

There is a new OGC Standard Working Group currently in the process of formation on the topic of Geospatial Indicator Reporting in the context of SDGs.

[goergen95]

Upcoming GDAL 3.9 will have a feature implemented that sorts geoparquet files by bounding-box for faster reads when spatially filtering (see here). Could be useful when evaluating performant formats/file organizations to translate vector resources to in the next months.

[goergen95]

New coastal mid-res digital terrain model with low errors here.

[goergen95]

JRC's technical report on global forest map for 2020.

[goergen95]

Near-real-time estimation of carbon dioxide and water flux from geostationary satellites from the ALIVE project.

[goergen95]

1m resolution with global coverage of tree heights from Meta and WRI -> here... Absolutely crazy!

[fBedecarrats]

I understand from you posts on Linkedin and other comments on bluesky that there are still questions about the reliability of this dataset. Do you have an opinion on this now?

[goergen95]

I have not validated the data myself, but there is an interesting analysis by Iain Woodhouse available here.

There are two main take-aways from my POV:

• its a global dataset which comes with the usual caveats, i.e. the reported global accuracy does not uphold to ones expectations when comparing it with local data sets derived from e.g. airborne LiDAR or similar. However, it can provide valuable information for areas where no higher quality measurements are available or for continental/global comparisons.

• the canopy height layer was generated from image mosaics with very different time stamps between 2009 (!) - 2020. In the first release the temporal information was missing but in my understanding it is now available. However, the temporal distribution is quite patchy, especially in the tropics due to persistent cloud cover, so that properly harmonizing this data with e.g. GFW will be really challenging in my view.

[cboettig]

Hi friends, have you seen the beautiful work of Gilberto Camara and colleagues in the sits package and accompanying text? https://e-sensing.github.io/sitsbook/ . I haven't delved too much into it yet but they appear to have wrapped around rstac and gdalcubes rather nicely.

[fBedecarrats]

Thanks @cboettig ! I understand that this as some kind of extension of the {stars} approach on data cubes, applied to landcover classification and with a strong focus on accessing raster data that sits on the cloud.

[fBedecarrats]

This paper is not that new but I found it interesting regarding the appropriate scale of analysis to use for spatial impact evaluation: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0167945
Briefly, it says that smaller area of analysis is not always better and one should take into account the scale at which the process of interest is hapenning (eg. deforestation, land use change...) and the resolution of the covariates used for analysis.

[goergen95]

Hi,

this simulation study is gold, thanks for sharing!

I have a few thoughts in my head with some question marks of how to relate
this study to what we are trying to do here:

1. The simulation study assumes a grid-based analysis which corresponds
   well with the honeycomb procedure of the conducted impact assessments.
   I wonder if there are similar studies when the unit of analysis is vector
   based?

2. Depending on the scale of analysis, significant bias can be introduced when
   treatment is represented as a discrete variable rather than on a continuous scale.
   I wonder if this holds with our exactextract approach with which for most
   variables we extract area-weighted values based on the overlap between a
   vector with individual cells.

3. The main recommendation, in my understanding, goes something like
   think about the scale of the process you are modelling to decide on an
   appropriate scale of analysis. This does not translate well to
   global-portfolio based analysis, does it? When we are looking at globally
   distributed PAs, I assume that there are very different variables and
   relationships between those driving the underlying processes. Selecting
   a scale would assume that a) the process can be observed globally and b)
   the scale of this process does not change with space. What kind of processes
   are we interested in where we can reasonably assume this?