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Tree Canopy Observations — Trailing Mean Series + Adjacent Row Changes
x402
Tree Canopy Observations — Trailing Mean Series + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-27).
Computes a trailing mean time series and adjacent row changes for user-supplied tree canopy observation records (canopy_m2 or plot_m2), returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tree canopy observations — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes for supplied tree canopy observations records (canopy_m2, plot_m2). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two analytical reports: (1) a trailing mean series computed over the selected field (canopy_m2 or plot_m2) using the specified window size, and (2) a set of adjacent row changes showing the difference between consecutive observation records. Results cover up to 200 input rows and are computed entirely from the supplied data.
Example use cases
Urban tree canopy trend analysis
“I've got 120 rows of tree canopy survey data from our city parks, each with a date, station group, canopy area in square meters, and plot size. Run a trailing mean series with a window of 10 and show me the adjacent row changes for the canopy_m2 field.”
Ecological plot monitoring over season
“We recorded canopy and plot measurements at three station groups across 90 dates this growing season. Can you compute a 7-period trailing mean and adjacent change report for the plot_m2 values so we can see growth trends between observations?”
Forestry survey QA for consecutive differences
“I need to audit our forestry plot data for sudden jumps — I have 200 rows with dates, row IDs, station groups, and canopy_m2 measurements. Please generate a trailing mean (window size 4) and an adjacent row change report on canopy_m2 so I can spot anomalies.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-17-146?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have user-supplied tree canopy observation records and need both a rolling/trailing mean time series and adjacent row difference analysis in a single API call at low cost. It is well-suited for ecological, forestry, or urban planning datasets with up to 200 rows where no external data lookup, live sensor integration, or predictive modeling is required — purely local analytical computation on your submitted data.
Side effects
- Fewer than 1 row or more than 200 rows submitted — rejected with validation error
- Missing required fields (row_id, station_group, date, canopy_m2, plot_m2) on any row — schema validation failure
- Date field does not match YYYY-MM-DD pattern — validation error
- Window size smaller than 2 or larger than 200 — rejected
- Null values in canopy_m2 or plot_m2 may affect mean/change calculations for affected rows
- Invalid field enum value (not 'canopy_m2' or 'plot_m2') — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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