Output the spatial domain of the following article in JSON array, it might be a place name or a lat/lon coordinate. The JSON objects like this:
{ "location_name": "foo", "lat": <number/null>, "lon": <number/null> }
Don't say anything else except the JSON Array.


From a Shannon's entropy perspective, such matrices are equal in
terms of heterogeneity. The Shannon's entropy is indeed based on the
relative abundance (and richness) of a sample, and its value is 2.197
for both the matrices. This value, equalling the natural logarithm of
the number of classes (pixel values), is also Shannon's maximum theoretical value given a 3 × 3 matrix, due to the lack of identical numbers
in the matrices. This example explicitly shows that accounting for the
distance among values and their relative abundance is crucial to discriminate among areas in terms of measured (modelled) heterogeneity.
One of the metrics accounting for both the abundance and the
pairwise spectral distance among pixels is the NAME_1's Q diversity index,
as follows:
where dij = spectral distance among pixels i and j and p = proportion
of occupied area.
Hence, NAME_1's Q is capable of discriminating among the ecological diversity of matrices (3) and (4), turning out to be 4.59 and 90.70,
respectively. Appendix S1 provides an example spreadsheet to perform the calculation while the complete R code is stored in the GitHub
repository https://github.com/NAME_2/spectralrao.
We decided to make use of a case study to highlight the importance of considering the distance among pixel values in remote sense
ecological application. The performance of NAME_1's Q index in describing landscape diversity was tested in a complex agro-forestry landscape located in southern Portugal. A test site with an area of about
10 × 10 km2 (centroid located at 38∘
39′10.74′′N; 8∘
12′52.30′′W) was
selected to conduct the analysis. In this area, a savanna-like ecosystem
called montado occupies about 40% of the test site, followed by traditional olive groves, pastures, vineyards, and irrigated monocultures
(e.g., corn fields). Montado is spatially characterized by the variability
of its tree density (e.g., NAME_3, NAME_4, Guiomar, NAME_5, & Pinto-Correia,
2016), and the gradient between low and high tree density over space
can lead to different structural heterogeneity and habitat diversity.
Within the test site, polyculture under the small farming context (e.g., vegetable gardens, orchards, and cereal crops) is an important feature of this landscape by generating a high compositional
and configurational s I want the entire output in JSON format.
user