LBA-ECO CD-08 Tree Inventory Data, Ducke Reserve, Manaus, Brazil: 1999
简介
该数据集包含一个数据文件，其中包含位于巴西亚马逊中部马瑙斯附近杜克保护区一块 5 公顷地块（20 x 2,500 米）内近 3,000 棵树的常用名、直径和计算出的树干质量。测量时间为 1999 年 10 月至 12 月。所有直径测量均在树高（DBH）1.3 米处进行，或在板根或其他树干异常处以上。从这些数据中可以得出森林结构特征，例如生物量密度、树干密度、径级分布以及科级甚至属级的分类信息。
摘要
Column Number Column Heading Units Variable Description
1 Location character Location of plot with reference to the Ducke observation tower.
2 Quad character Indication of the 20 x 20 m quad where the tree was located. The entire plot (east and west) is 20 x 2,500 m or 5 ha. Valid values range from 1 to 64.
3 Tree_num character This is the tag number on the tree -- values range from 1-1000.Values are not unique.
4 Common_name character Species common names used by forest workers in the Brazilian Amazon
5 Diameter cm Diameter (cm) was taken at 1.3 m height, unless noted in Measurement_height column
6 Measurement_ht m Measurement height (m) -- For trees with buttresses or other obstructions, diameter was measured above the standard 1.3 meter height, as noted.
7 Tree_mass kg Tree mass (kg) -- Equation from: Chambers, J. Q., J. Santos, R. J. Ribeiro, and N. Higuchi. 2001. Tree damage, allometric relationships, and above-ground net primary production in a tropical forest. Forest Ecology and Management 152:73-84. Tree mass (kg) =EXP(-0.37 + 0.333LN(Diameter) + 0.933LN(Diameter)^2 - 0.122*LN(Diameter)^3) -- where Diameter is from column (5).
Site (Region) Westernmost Longitude Easternmost Longitude Northernmost Latitude Southernmost Latitude Geodetic Datum
Amazonas (Manaus) - Reserva Ducke (Amazonas (Manaus)) -59.95000 -59.95000 -2.95000 -2.95000 World Geodetic System, 1984 (WGS-84)
代码
!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassify
import pandas as pd
import leafmap
url = "https://github.com/opengeos/NASA-Earth-Data"
df = pd.read_csv(url, sep="\t")
df
leafmap.nasa_data_login()
results, gdf = leafmap.nasa_data_search(
short_name="CD08_Tree_Inventory_Ducke_910",
cloud_hosted=True,
bounding_box=(-55.21, -4.05, -54.91, -2.84),
temporal=("1999-10-01", "1999-12-01"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
