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社区首页 >专栏 >Google Earth Engine ——美国LANDIFRE火灾数据集LANDFIRE/Fire/PLS/v1_2_0数据集内包含多种数据要素

Google Earth Engine ——美国LANDIFRE火灾数据集LANDFIRE/Fire/PLS/v1_2_0数据集内包含多种数据要素

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发布2024-02-02 09:14:58
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发布2024-02-02 09:14:58
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LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture's Forest Service, U.S. Department of the Interior's Geological Survey, and The Nature Conservancy.

Landfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT). These data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.

The Percent of Low-severity Fire (PLS) layer quantifies the amount of low-severity fires relative to mixed- and replacement-severity fires under the presumed historical fire regime. Low severity is defined as less than 25 percent average top-kill within a typical fire perimeter for a given vegetation type. PLS was derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM). This layer is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE (LF) Biophysical Settings (BPS) layer and BPS Model documentation. This layer is created by linking the BPS Group attribute in the BPS layer with the Refresh Model Tracker (RMT) data and assigning the PLS attribute. This geospatial product should display a reasonable approximation of PLS, as documented in the RMT. PLS is used in landscape assessments.

The LANDIFRE Fire datasets include:

  • Fire Regime Groups (FRG) is intended to characterize presumed historical fire regimes within landscapes based on interactions between vegetation dynamics, fire spread, fire effects, and spatial context
  • Mean Fire Return Interval (MFRI) quantifies the average period between fires under the presumed historical fire regime
  • Percent of Low-severity Fire (PLS) image quantifies the amount of low-severity fires relative to mixed- and replacement-severity fires under the presumed historical fire regime and is defined as less than 25 percent average top-kill within a typical fire perimeter for a given vegetation type
  • Percent of Mixed-severity Fire (PMS) layer quantifies the amount of mixed-severity fires relative to low- and replacement-severity fires under the presumed historical fire regime, and is defined as between 25 and 75 percent average top-kill within a typical fire perimeter for a given vegetation type
  • Percent of Replacement-severity Fire (PRS) layer quantifies the amount of replacement-severity fires relative to low- and mixed-severity fires under the presumed historical fire regime, and is defined as greater than 75 percent average top-kill within a typical fire perimeter for a given vegetation type
  • Succession Classes (SClass) layer characterizes current vegetation conditions with respect to the vegetation species composition, cover, and height ranges of successional states that occur within each biophysical setting
  • Vegetation Condition Class (VCC) represents a simple categorization of the associated Vegetation Departure (VDEP) layer and indicates the general level to which current vegetation is different from the simulated historical vegetation reference conditions
  • Vegetation Departure (VDep) indicates how different current vegetation on a landscape is from estimated historical conditions. VDep is based on changes to species composition, structural stage, and canopy closure.'

LANDFIRE(LF),景观火灾和资源管理规划工具,是美国农业部林务局、美国内政部地质调查局和大自然保护协会的野地火灾管理项目之间的一个共享项目。

土地火灾(LF)历史上的火灾制度、间隔和植被状况是用植被动态发展工具(VDDT)绘制的。这些数据支持《国家统一野地火灾管理战略》、《联邦野地火灾管理政策》和《健康森林恢复法》中的火灾和景观管理规划目标。

低严重性火灾百分比(PLS)层量化了在假定的历史火灾制度下,相对于混合和替代严重性火灾的低严重性火灾的数量。低严重性被定义为在一个给定植被类型的典型火灾周界内,平均顶部杀伤力低于25%。PLS来源于植被和干扰动态模型VDDT(植被动态开发工具)(LF1.0.0 CONUS只使用了植被和干扰动态模型LANDSUM)。该层的目的是在LANDFIRE(LF)生物物理设置(BPS)层和BPS模型文件所代表的更广泛的历史时期背景下,描述历史火灾制度特征的一个组成部分。该层是通过将BPS层中的BPS组属性与Refresh Model Tracker(RMT)数据联系起来,并分配PLS属性而创建的。这个地理空间产品应该显示PLS的合理近似值,如RMT中记载的那样。PLS被用于景观评估。

LANDIFRE火灾数据集包括。

火灾制度组(FRG)旨在根据植被动态、火灾蔓延、火灾影响和空间背景之间的相互作用,描述景观内假定的历史火灾制度的特点。 平均火灾重现间隔(MFRI)量化了假定的历史火灾制度下火灾的平均间隔时间。 低度火灾百分比(PLS)图像量化了在假定的历史火灾制度下,相对于混合和替代性火灾而言,低度火灾的数量,定义为在一个特定植被类型的典型火灾周界内,平均顶部杀伤力低于25%。 混合严重性火灾百分比(PMS)层量化了在假定的历史火灾制度下,相对于低度和替代严重性火灾而言,混合严重性火灾的数量,定义为在特定植被类型的典型火灾周界内,平均有25%至75%的顶部杀伤力。 替代严重性火灾百分比(PRS)层量化了在假定的历史火灾制度下,相对于低度和混合严重性火灾而言,替代严重性火灾的数量,并被定义为在给定植被类型的典型火灾周界内大于75%的平均顶部杀伤。 演替等级(SClass)层描述了当前的植被状况,包括植被物种组成、覆盖度以及每个生物物理环境中出现的演替状态的高度范围。 植被状况等级(VCC)是对相关植被偏离(VDEP)层的简单分类,表明当前植被与模拟的历史植被参考条件不同的总体水平 植被偏离(VDep)表示景观上的当前植被与估计的历史条件的不同程度。VDep是基于物种组成、结构阶段和树冠封闭的变化。

Dataset Availability

2010-01-01T00:00:00 - 2010-12-31T00:00:00

Dataset Provider

U.S. Department of Agriculture's (USDA), U.S. Forest Service (USFS), U.S. Department of the Interior's Geological Survey (USGS), and The Nature Conservancy.

Collection Snippet

ee.ImageCollection("LANDFIRE/Fire/PLS/v1_2_0")

Resolution

30 meters

Bands Table

Name

Description

PLS

Percent Low-severity Fire

Class Table: PLS

Value

Color

Color Value

Description

1

#008200

0-5%

2

#138f00

6-10%

3

#229c00

11-15%

4

#34a600

16-20%

5

#47b300

21-25%

6

#66bf00

26-30%

7

#7ecc00

31-35%

8

#9bd900

36-40%

9

#b8e600

41-45%

10

#e2f200

46-50%

11

#fffb00

51-55%

12

#fadd00

56-60%

13

#f7c600

61-65%

14

#f5a300

66-70%

15

#f08c00

71-75%

16

#eb7100

76-80%

17

#e85d00

81-85%

18

#e63d00

86-90%

19

#e32a00

91-95%

20

#de1200

96-100%

111

#0000ff

Water

112

#c8ffff

Snow / Ice

131

#4e4e4e

Barren

132

#b2b2b2

Sparsely Vegetated

133

#e1e1e1

Indeterminate Fire Regime

数据引用:

The suggested way to cite LANDFIRE products is specific to each product, so the model for citation is provided, with an example for a particular product. Producer. Year released. Product xxxxx:

  • Individual model name.
  • BpS Models and Descriptions, Online. LANDFIRE. Washington, DC. U.S. Department of Agriculture, Forest Service
  • U.S. Department of the Interior; U.S. Geological Survey; Arlington, VA
  • The Nature Conservancy (Producers). Available- URL. Access date.

Example Citation: LANDFIRE Biophysical Settings. 2018. Biophysical setting 14420: South Texas sand sheet grassland. In: LANDFIRE Biophysical Setting Model: Map zone 36, [Online]. In: BpS Models and Descriptions. In: LANDFIRE. Washington, DC: U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior; U.S. Geological Survey; Arlington, VA: The Nature Conservancy (Producers). Available: https://www.landfire.gov/national_veg_models_op2.php [2018, June 27]. Additional guidance on citation of LANDFIRE products can be found here

代码:

代码语言:javascript
复制
var dataset = ee.ImageCollection('LANDFIRE/Fire/PLS/v1_2_0');

var visualization = {
  bands: ['PLS'],
};

Map.setCenter(-121.671, 40.699, 5);

Map.addLayer(dataset, visualization, 'PLS');

混合严重性火灾百分比(PMS)层

代码语言:javascript
复制
var dataset = ee.ImageCollection('LANDFIRE/Fire/PMS/v1_2_0');

var visualization = {
  bands: ['PMS'],
};

Map.setCenter(-121.671, 40.699, 5);

Map.addLayer(dataset, visualization, 'PMS');

替代严重性火灾百分比(PRS)层

代码语言:javascript
复制
var dataset = ee.ImageCollection('LANDFIRE/Fire/PRS/v1_2_0');

var visualization = {
  bands: ['PRS'],
};

Map.setCenter(-121.671, 40.699, 5);

Map.addLayer(dataset, visualization, 'PRS');

演替等级(SClass)层

代码语言:javascript
复制
var dataset = ee.ImageCollection('LANDFIRE/Fire/SClass/v1_4_0');

var visualization = {
  bands: ['SClass'],
};

Map.setCenter(-121.671, 40.699, 5);

Map.addLayer(dataset, visualization, 'SClass');

植被状况等级(VCC)

代码语言:javascript
复制
var dataset = ee.ImageCollection('LANDFIRE/Fire/VCC/v1_4_0');

var visualization = {
  bands: ['VCC'],
};

Map.setCenter(-121.671, 40.699, 5);

Map.addLayer(dataset, visualization, 'VCC');

植被偏离(VDep)

代码语言:javascript
复制
var dataset = ee.ImageCollection('LANDFIRE/Fire/VDep/v1_4_0');

var visualization = {
  bands: ['VDep'],
};

Map.setCenter(-121.671, 40.699, 5);

Map.addLayer(dataset, visualization, 'VDep');
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