# Tree Canopy Observations — Date×Category Pivot + Missing-Value Fill

> Tree Canopy Observations — Date×Category Pivot + Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Pivots tree canopy observation records by date and station group, then fills missing values using zero, mean, or median imputation — returning two analytical reports per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-17-189?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/tree-canopy-observations-date-category-pivot-missing-value-fill-72cb3862
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_k95BH0LVy0bAuMa6QbkdG

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability tree-canopy-observations-date-category-pivot-missing-value-fill-72cb3862 -d '<json body>'
```

Example prompt: I have up to 200 rows of tree canopy observation records with fields row_id, station_group, date, canopy_m2, and plot_m2 — can you pivot them by date and station group, and fill any missing canopy_m2 values using the median method?

## When to prefer this

Choose this endpoint when you have raw tree canopy observation records (canopy_m2, plot_m2) that need to be restructured into a date-by-category pivot and have missing values imputed in a single low-cost call. It is ideal for environmental monitoring workflows, urban forestry reporting, or any scenario where canopy and plot area measurements are collected across multiple station groups over time and gaps need to be handled analytically without external lookups or predictions.

## Known failure modes

- Fewer than 1 row or more than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) causes row-level validation failure
- Unrecognized fill method value outside zero/mean/median enum
- Missing required fields (row_id, station_group, date) cause schema rejection
- All values for selected field are null — mean/median fill may return zero or undefined
- Payment not confirmed — endpoint returns 402 status

## How this service works

Tree canopy observations — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill 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

Two analytical reports derived from the supplied data: (1) a date-by-station-group pivot table for the selected field (canopy_m2 or plot_m2), and (2) the same dataset with explicit missing-value fill applied using the chosen method (zero, mean, or median). No external data is fetched; all computation runs on the submitted rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "station_group",
     "date",
     "canopy_m2",
     "plot_m2"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "plot_m2": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "canopy_m2": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "station_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "plot_m2": 1,
     "canopy_m2": 10,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "plot_m2": 2,
     "canopy_m2": 20,
     "station_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "plot_m2": 3,
     "canopy_m2": 15,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "plot_m2": 4,
     "canopy_m2": 30,
     "station_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "canopy_m2",
    "plot_m2"
   ],
   "type": "string",
   "default": "canopy_m2"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/tree-canopy-observations-date-category-pivot-missing-value-fill-72cb3862/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)