Dynamic monitoring of forest carbon stocks based on the InVEST model

HE Tao; SUN Yujun

doi:10.11833/j.issn.2095-0756.2016.03.002

Volume 33 Issue 3

Oct. 2016

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HE Tao, SUN Yujun. Dynamic monitoring of forest carbon stocks based on the InVEST model[J]. Journal of Zhejiang A&F University, 2016, 33(3): 377-383. doi: 10.11833/j.issn.2095-0756.2016.03.002

Citation:

HE Tao, SUN Yujun. Dynamic monitoring of forest carbon stocks based on the InVEST model[J]. Journal of Zhejiang A&F University, 2016, 33(3): 377-383. doi: 10.11833/j.issn.2095-0756.2016.03.002

Dynamic monitoring of forest carbon stocks based on the InVEST model

doi: 10.11833/j.issn.2095-0756.2016.03.002

HE Tao^{1,2
,},
SUN Yujun^{1
,
,}

1.
College of Forestry, Beijing Forestry University, Beijing 100083, China
2.
Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Zhejiang A & F Unirersity, Lin’an 311300, Zhejiang, China

Received Date: 2015-06-08
Rev Recd Date: 2015-09-09
Publish Date: 2016-06-01

Abstract

Forest carbon storage is almost estimated by the ground survey data, there is a difficulty of huge statistical work and modeling complexity. To calculate forest carbon stocks in a quick and accurate way, which has been the current research focus in forestry both inside and outside of China. A new method is proposed by combining a little input parameters of the InVEST model with a large monitoring scale of remote sensing data. First, InVEST model was used to estimate regional carbon stocks according to forest type carbon data and according to raster data. Then this data was compared to the multistage carbon data from remote sensing data to achieve a dynamic monitoring of forest carbon stocks. This paper estimated and mapped carbon stock of the Qingyuan (a county) in 2009, according to the administrative division map can estimate the carbon storage of the scale of town and village. After estimated carbon reserves of Kengxi (a village) in 2009 and 2014, carbon difference method was adopted to dynamic monitoring of carbon sink. Results showed that the carbon sink estimated with the new method for Qiyuan(a county), Zhejiang, China was 3.274 3×10⁷ Mg in 2009. Also, the carbon stock for Kengxi (a village) increased 1.780 3×104 Mg from 2009 to 2014. Compared to forest average carbon density, adding forest species average carbon storage and carbon sinks to forest carbon stock monitoring improved estimation precision of forest carbon stocks. This dynamic multi-dimensional method for monitoring forest carbon sinks being simple, convenient, user-friendly, and advantageous because it used less data input and visual output in the model, could be used in the county, township (town) and village forest carbon monitoring. [Ch, 5 fig. 4 tab. 32 ref.]
- forest ecology,
- forest carbon stock,
- remote sensing data,
- InVEST,
- dynamic monitoring

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森林是陆地生态系统的主体，在调控全球碳循环和气候变化中扮演着重要的角色^[1-4]。2000年，联合国政府间气候变化专门委员会(IPCC)发表报告指出，森林面积占全球陆地面积的27.6%，森林植被的碳储量约占全球植被碳储量的77.0%，森林土壤的碳储量约占全球土壤碳储量的39.0%，整个森林生态系统碳储量占陆地生态系统碳储量的57.0%^[5]。因此，森林生态系统是陆地上最大的碳储库和最经济的吸碳器^[6]，在调节全球气候、维持全球碳平衡中具有重大作用，森林碳储量的监测对于全球气候变化等问题的预测也有着十分重要的意义。迄今为止，森林碳储量的监测是一项耗时耗力耗财的工作，如何快速准确估测森林碳储量仍是亟待解决的一个问题。森林碳储量计量方法主要有以下3类：①利用微气象原理和技术测定二氧化碳通量的方法；②基于生物量估算法；③遥感信息模型法^[7]。上述3种计量方法中，第1种方法获得的碳储量数据最为准确^[8]，但成本也最高^[9]，只适合在较小尺度范围监测碳储量。基于生物量的方法较适合大尺度范围的计量，但这种方法需要大量的地面调查，整理汇总数据的内业耗费时间也多。上述2种方法均无法计算出森林植被的空间位置和碳储量的定量分布^[10]。森林碳储量的动态监测主要方法^[11-12]有：①基于增加或损失的方法；②基于库的替代方法。为了减少外业调查工作量，本研究结合遥感数据获取信息便捷、数据成本低和InVEST(integrated valuation of ecosystem services and tradeoffs)模型碳储量模块输入数据量少、输出结果可视化的特点，提出了一种基于InVEST模型结合遥感数据实现县、镇(乡)、村庄等多尺度森林碳储量估测和动态监测的方法。该方法可快速便捷地得出区域森林碳储量并实现动态监测。

1. 研究区概况

庆元县，位于浙江省西南部，27°25′~27°51′N，118°50′~119°30′E，北面与浙江省龙泉市、景宁畲族自治县接壤，东、西、南面与福建省寿宁、松溪、政和等3县交界。县城所在地松源镇距丽水市233 km，距杭州市532 km。全县南北长49 km，东西宽67 km，总面积为1 898 km²^[13]。

庆元县自然资源十分丰富，在全县1.897 6×10⁵ hm²土地面积中，林业用地1.623 3×10⁵ hm²，占85.5%；农业耕地1.070 7×10⁴ hm²，占5.6%；河流2.300 0×10³ hm²，占1.2%；房屋、道路及其他用地1.442 0×10⁴ hm²，占7.7%，全县森林覆盖率达到86%^[14]。2003年通过国家级生态示范区创建验收，2004年成为“中国生态环境第一县”。

2. 模型和数据

2.1. 模型概况

InVEST模型由美国斯坦福大学、世界自然基金会和大自然保护协会联合开发，主要用于生态系统服务功能评估的模型系统，旨在通过模拟预测不同土地利用情景下生态系统服务功能物质量和价值量的变化，为决策者权衡人类活动的效益和影响提供科学依据^[15]。InVEST模型实现碳储量动态监测的方法是基于库的替代方法，主要根据土地利用分类现状、木材采伐率、植被降解率和4个碳库的碳储量来计量出某一区域的碳储量现状和预测该区域未来的碳储量。区域内的碳储量主要取决于4个碳库的碳储量，分别为地上生物量、地下生物量、土壤碳储量和死亡有机物的碳储量^[16]。

碳储量模块运行基于地理信息系统(GIS)栅格数据的网格地图，每个栅格数据都代表一种土地利用和土地覆盖类型，如森林、牧场或农业用地等。对于每种土地类型，模型至少需要上述4个碳库中的1个碳库的碳储量才能运行，当然如果能够提供更多碳库，那么模型的结果也将更完善、更准确。

模型运行的基本输入数据必须有以下2项：①栅格数据集，每个栅格单元都代表 1种土地利用分类图的代码；②存储每个土地利用分类图的4个碳库的数据表。模型输出值表示每个网格单元的碳储量，默认单位为t，也可以将单位换算成美元，即表示每个网格单元碳储量的经济价值。

遥感数据源日期含云量/%

Landsat-5 TM 2009-06-06 0.01

Landsat-8 OLI_TIRS 2004-07-22 4.02

Table 1. Remote sensing data set

为了估算碳储量的动态变化情况，模型主要对2期地图以上的像元进行简单的差值运算。如果有多个未来土地利用分类图，那么每个未来土地利用分类图都可以和现在的土地利用图进行比对，得到一个时间段内碳储量的动态变化。

2.2. 遥感数据源

本研究所用的遥感数据主要是从中国科学院计算机网络信息中心国际科学数据镜像网站(http://www.gscloud.cn)中下载，覆盖研究区的陆地卫星TM5和陆地卫星TM8数据都为条带号119，行编号41的遥感数据。由于该区域的森林植被在每年的七八月达到生长最旺季，为了更好地目视判别该区域的植被，选取该时间段的遥感数据是最佳的，且遥感数据中的总云量应小于5%。最终采集的遥感数据集信息如表 1所示。

2.3. 地面调查数据

将收集到的2008年的研究区森林资源二类调查数据落到2009年的TM遥感卫星图像上并进行栅格化，文件格式采用标准GIS栅格文件，每个栅格表示森林类型编码(例如，1代表杉柏林，3代表松树林等)。研究使用的数据集投影坐标单位为米，大地坐标系采用西安80坐标系。具体的调查地类和小班数详见表 2。

编码森林类型小班数/个

1 杉柏林 6 342

2 阔叶林 4 791

3 松树林 3 911

4 竹林 3 394

5 经济林 817

7 未成林 176

8 灌木林 139

9 茶园 110

10 火烧迹地 27

11 其他林地 17

Table 2. Survey of forest class data in the study area

2.4. 各森林类型的碳储量密度

通过查阅文献资料，整理得出研究区内11种森林类型的平均碳储量密度。根据IPCC(2006)资料：当研究区域生物量小于125.0 t·hm^-2时，地下生物量根据地上生物量折算的时候，一般采用的根茎比为0.20^[17]。因此，本研究的地下碳储量都是通过地上碳储量乘以0.20的根茎比系数折算得出。由于InVEST模型里没有单独计算枯死木碳库，因此枯死木碳库在本研究中计算到凋落物碳库中。非林地和火烧迹地在本文研究中只计算土壤碳储量。最终得出研究区内各森林类型的4大碳库的碳储量如表 3所示。从表 3中可看出：各森林类型的土壤碳储量差别明显，0~1.0 m深的土壤碳储量在88.60~166.93 Mg·hm^-2，这与相关研究得出的全国土壤平均碳密度105.30 Mg·hm^-2相符合^[18]。在各碳池中，阔叶林类型包含最大的碳储总量，为225.10 Mg·hm^-2，非林地和火烧迹地碳储量最小，只有88.60 Mg·hm^-2。研究区的各森林类型的总碳储量低于相关研究中中国森林生态系统碳密度258.83 Mg·hm^-2的平均水平^[19]。

地类代码	地类名称	地上碳密度/ (Mg·hm^-2)	地下碳密度/ (Mg·hm^-2)	土壤碳密度/ (Mg·hm^-2)	凋落物碳密度/(Mg·hm^-2)	总碳密度/ (Mg·hm^-2)	文献出处
1	杉柏林	26.83	5.37	159.77	1.52	193.49	^[20]
2	阔叶林	44.59	8.92	166.93	4.66	225.10	^[20]
3	松树林	25.62	5.12	119.47	2.72	152.93	^[20]
4	竹林	23.09	4.61	110.10	0.66	138.46	^{[21, 22]}
5	经济林	8.70	1.64	101.23	1.20	112.77	^{[23, 24]}
6	非林地	0	0	88.60	0	88.60	^[18]
7	未成林	8.10	1.62	91.70	3.48	104.90	参考灌木林
8	灌木林	8.10	1.62	91.70	3.48	104.90	^{[25, 26]}
9	茶园	21.21	4.24	137.50	4.91	167.86	^[27]
10	火烧迹地	0	0	88.60	0	88.60	参考非林地
11	其他林地	35.03	7.01	142.58	3.75	188.37	^[20]

Table 3. Survey carbon data of land classes in the study area

5. 结论与讨论

本研究主要根据遥感图像数据结合森林资源地面调查数据，利用InVEST模型对研究区不同空间尺度[县、乡(镇)、村级]的森林碳储量进行了量化，估算了同一区域不同时期碳储量的变化值，同时绘制出了森林碳储量的空间分布状态图。主要结论如下：①目前，InVEST模型在森林碳储量动态监测方面的应用还较少。通过将遥感图像数据和森林资源地面调查数据作为InVEST模型的土地利用类型输入数据源，可实现区域森林碳储量的估测，监测方法简便，可操作性强。②InVEST模型需要录入的数据量较少，只需要输入土地利用类型和土地利用类型的碳储量数据表，大大降低了地面调查和室内汇总整理工作。同时，结合遥感图像数据可提高碳储量估算结果的准确性。③利用InVEST模型不仅可以通过估算出某区域2个以上时期碳储量数据实现对该区域森林碳储量的动态监测，获得树种的年度碳储量增长量，还可预测若干年后该区域的碳储量变化和空间分布状况。这对地方政府和林业主管部门制定相关方针政策有着十分重要的参考价值。同时，利用InVEST模型可将量化的数据直观地反映在研究区森林碳储量分布状况图上，方便地方政府和林业主管部门直观理解碳储量薄弱区域。

今后，如果输入模型的数据采用更为详细的土地利用/土地覆盖图和更为精准的碳储量数据表，那么将会提升模型的精度和准度。国内外也有相当多的研究集中在如何建立遥感图像与森林碳储量之间的模型，因此，如果建立起区域碳储量与遥感图像的模型，而不是输入森林类型的平均碳储量，那么森林碳储量监测精度将会更高，且可提高工作效率，减少目视判别工作量。以上这些方面都是今后的研究方向和研究重点。

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Dynamic monitoring of forest carbon stocks based on the InVEST model

doi: 10.11833/j.issn.2095-0756.2016.03.002