Research progress of forest ecological quality assessment methods
Vol 4, Issue 2, 2021
VIEWS - 944 (Abstract) 305 (PDF)
Abstract
Forests have ecological functions in water conservation, climate regulation, environmental purification, soil and water conservation, biodiversity protection and so on. Carrying out forest ecological quality assessment is of great significance to understand the global carbon cycle, energy cycle and climate change. Based on the introduction of the concept and research methods of forest ecological quality, this paper analyzes and summarizes the evaluation of forest ecological quality from three comprehensive indicators: forest biomass, forest productivity and forest structure. This paper focuses on the construction of evaluation index system, the acquisition of evaluation data and the estimation of key ecological parameters, discusses the main problems existing in the current forest ecological quality evaluation, and looks forward to its development prospects, including the unified standardization of evaluation indexes, high-quality data, the impact of forest living environment, the acquisition of forest level from multi-source remote sensing data, the application of vertical structural parameters and the interaction between forest ecological quality and ecological function.
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1. Achard F, Eva HD, Stibig HJ, et al. Determination of deforestation rates of the world's humid tropical forests. Science 2002; 297(5583): 999–1002.
2. Chave J, Davies SJ, Phillips OL, et al. Ground data are essential for biomass remote sensing missions. Surveys in Geophysics 2019; 40(4): 863–880.
3. Dudley N, Schlaepfer R, Jackson W, et al. Forest quality: Assessing forests at a landscape scale. London: Routledge; 2012.
4. Shi C, Wang L. Connotation of forest resources quality. Issues of Forestry Economics 2007; 27(3): 221–224.
5. Bull GQ, Boedhihartono AK, Buenoueno G, et al. Global forest discourses must connect with local forest realities. International Forestry Review 2018; 20(2): 160–166.
6. Sarker LR, Nichol JE. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sensing of Environment 2011; 115(4): 968–977.
7. Riedler B, Pernkopf L, Strasser T, et al. A composite indicator for assessing habitat quality of riparian forests derived from earth observation data. International Journal of Applied Earth Observation and Geoinformation 2015; 37(9): 114–123.
8. Zhao Q, Yu S, Zhao F, et al. Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments. Forest Ecology and Management 2019; 434(12): 224–234.
9. Wu G. The study on indicator system and evaluation method of forest resources quality at county-level [PhD thesis]. Beijing: Beijing Forestry University; 2010. p. 111.
10. Zhao T, Ouyang Z, Zheng H, et al. Forest ecosystem services and their valuation in China. Journal of Natural Resources 2004; 19(4): 480–491.
11. Feng J, Ding L, Wang J, et al. Case-based evaluation of forest ecosystem service function in China. Chinese Journal of Applied Ecology 2016; 27(5): 13750–1382.
12. Guo X. Multi-scale assessments on ecological quality of urban forest in Shanghai [PhD thesis]. Shanghai: East China Normal University; 2017. p. 191.
13. He D. The evaluation of forest ecological quality in Liling of Hunan Province [PhD thesis]. Hunan: Central South University of Forestry and Technology; 2017. p. 116.
14. Feng J, Wang J, Yao S, et al. Dynamic assessment of forest resources quality at the provincial level using AHP and cluster analysis. Computers and Electronics in Agriculture 2016; 124(4): 184–193.
15. Du Z, Gan S, Hu J. Comprehensive evaluation of forest resources quality in China. Central South Forest Inventory and Planning 2018; 37(3): 1–5.
16. Li H, Lei Y, Zeng W. Forest carbon storage in China estimated using forestry inventory data. Scientia Silvae Sinicae 2011; 47(7): 7–12.
17. Zhao M, Zhou G. Estimation of biomass and net primary productivity of major planted forests in China based on forest inventory data. Forest Ecology and Management 2005; 207(3): 295–313.
18. Liu Q, Ouyang Z, Li A, et al. Study on spatial differentiation characteristics of forest vegetation biomass and carbon stock in Chongqing city. Research of Soil and Water Conservation 2016; 23(06): 221–226, 381.
19. Du H, Cui R, Zhou G, et al. The responses of Moso bamboo (Phyllostachys heterocycla var. Pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI. Acta Ecological Sinica 2010; 30(5): 257–263.
20. Qiu S. The research of regional forest above ground biomass inversion combining ICESat-GLAS waveform and HJ-1A hyperspectral imageries [PhD thesis]. Harbin: Northeast Forestry University; 2016. p. 128.
21. Zhang C, Denka S, Cooper H, et al. Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data. Remote Sensing of Environment 2018; 204(10): 366–379.
22. Zhao Y. Yaogan yingyong fenxiyuanli yu fangfa (Chinese) [Principles and methods of remote sensing application analysis]. Beijing: Science Press; 2013: 36–163.
23. Zhang L, Shao Z, Diao C. Synergistic retrieval model of forest biomass using the integration of optical and microwave remote sensing. Journal of Applied Remote Sensing 2015; 9(1): 18.
24. Chen E. Development of forest biomass estimation using SAR data. World Forestry Research 1999; 12(6): 18–23.
25. Minh DHT, Ndikumana E, Vieilledent G, et al. Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar. Remote Sensing of Environment 2018; 213(4): 206–214.
26. Hudak AT, Lefsky MA, Cohen WB, et al. Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height. Remote sensing of Environment 2002; 82(2/3): 397–416.
27. Chen Q, Mcroberts RE, Wang C, et al. Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference. Remote Sensing of Environment 2016; 184(7): 350–360.
28. Wang P, Wan R, Yang G. Advance in classification and biomass estimation of plants in westlands based on multi-source remote sensing data. Wetland Science 2017; 15(1): 117–127.
29. Liu T, Kong Y, Wu Y, et al. Provincial forest ecological security evaluation in China based on the entropy weight of the fuzzy matter-element model. Acta Ecologica Sinica 2017; 37(15): 4946–4955.
30. Yu J, Fang L, Cang D, et al. Evaluation of land eco-security in Wanjiang district based on entropy weight and matter element model. Transactions of the Chinese Society of Agricultural Engineering 2012; 28(5): 267–273.
31. Xie Y, Gong J, Qi S, et al. Assessment and spatial variation of biodiversity in the Bailong River Watershed of the Gansu Province. Acta Ecologica Sinica 2017; 37(19): 6448–6456.
32. Wang Y, Wu H, Xu H. Biological and ecological bases of using insect as a bio-indicator to asses forest health. Chinese Journal of Applied Ecology 2008; 19(7): 1625–1630.
33. Zhou J, Wan R. Advances in methods of wetland ecosystem health evaluation. Ecological Science 2018; 37(6): 209–216.
34. Wu Y, Li Y, Zhang L, et al. Assessment of lakes ecosystem health based on objective and subjective weighting combined with fuzzy comprehensive evaluation. Journal of Lake Sciences 2017; 29(5): 1091–1102.
35. Tan F, Zhang M, Li H, et al. Assessment on coordinative ability of sustainable development in Beijing-Tianjin-Hebei region based on set pair analysis. Acta Ecologica Sinica 2014; 34(11): 3090–3098.
36. Cooner AJ, Shao Y, Campbell JB. Detection of urban damage using remote sensing and machine learning algorithms: Revisiting the 2010 Haiti earthquake. Remote Sensing 2016; 8(10): 851–868.
37. Crowther TW, Glick HB, Covey KR, et al. Mapping tree density at a global scale. Nature 2015; 525(7568): 201–205.
38. Were K, Bui DT. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators 2015; 52(12): 394–403.
39. Zhu X, Liu D. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing 2015; 102(8): 222–231.
40. Rodrigues-Veiga P, Quegan S, Carreiras J, et al. Forest biomass retrieval approaches from earth observation in different biomes. International Journal of Applied Earth Observation and Geoinformation 2019; 77(12): 53–68.
41. Wang Y, Lu C, Zuo C. Coal mine safety production forewarning based on improved BP neural network. International Journal of Mining Science and Technology 2015; 25(2): 319–324.
42. Ma D, Zhou T, Chen J, et al. Supercritical water heat transfer coefficient prediction analysis based on BP neural network. Nuclear Engineering and Design 2017; 320(06): 400–408.
43. Tang X. Estimation of forest aboveground biomass by integrating ICESat/GLAS waveform and TM Data [PhD thesis]. Harbin: University of Chinese Academy of Sciences; 2013. p. 1–127.
44. Dube T, Mutanga O. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in umgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing 2015; 101(11): 36–46.
45. Srinet R, Nandy S, Patel NR. Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India. Ecological Informatics 2019; 52(5): 94–102.
46. Houghton RA, Hall F, Goetz SJ. Importance of biomass in the global carbon cycle. Journal of Geophysical Research: Biogeosciences 2009; 114: 1–13.
47. Le Toan T, Quegan S, Davidson MWJ, et al. The biomass mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sensing of Environment 2011; 115(11): 2850–2860.
48. Hui G. Studies on the application of stand spatial structure parameters based on the relationship of neighborhood trees. Journal of Beijing Forestry University 2013; 10(4): 1–8.
49. Paul KI, Larmour J, Specht A, et al. Testing the generality of below-ground biomass allometry across plant functional types. Forest Ecology and Management 2019; 432(8): 102–114.
50. Paul KI, Roxburgh SH, Chave J, et al. Testing the generality of above-ground biomass allometry across plant functional types at the continent scale. Global Change Biology 2016; 22(6): 2106–2124.
51. Frampton WJ, Dash J, Watmough G, et al. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing 2013; 82(4): 83–92.
52. Haralick RM. Statistical and structural approaches to texture. Proceedings of the IEEE 1979; 67(5): 786–804.
53. Eckert S. Improved forest biomass and carbon estimations using texture measures from worldview-2 satellite data. Remote Sensing 2012; 4(4): 810–829.
54. Zhao P, Lu D, Wang G, et al. Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data. International Journal of Applied Earth Observation and Geoinformation 2016; 53(8): 1–15.
55. Jordan CF. Derivation of leaf-area index from quality of light on the forest floor. Ecology 1969; 50(4): 663–666.
56. Wang Z, Liu C, Alfredo H. From AVHRR-NDVI to MODIS-EVI: Advances in vegetatoion index research. Acta Ecologica Sinica 2003; 23(5): 979–987.
57. Zhang X, Wu B. A temporal transformation method of fractional vegetation cover derived from high and moderate resolution remote sensing data. Acta Ecologica Sinica 2015; 35(4): 1155–1164.
58. Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing and Environment 1979; 8(2): 127–150.
59. Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988; 25(3): 295–309.
60. Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 1991; 35(2/3): 161–173.
61. Qi J, Chehbouni A, Huete AR, et al. A modified soil adjusted vegetation index (MSAVI). Remote Sensing of Environment 1994; 48(2): 119–126.
62. Venancio LP, Mantovani EC. Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agricultural Water Management 2019; 225(20): 1–16.
63. Huete AR, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002; 83(1/2): 195–213.
64. Richardson AJ, Wiegand CL. Distinguishing vegetation from soil background information. Photogram-metric Engineering and Remote Sensing 1977; 43(12): 1541–1552.
65. Zhou J, Zhao Z, Zhao Q, et al. Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data. Journal of Applied Remote Sensing 2013; 7(1): 073484.
66. Xu H, Pan P, Ning J, et al. Remote sensing estimation of forest aboveground biomass based on multiple linear regression and neural network model. Journal of Northeast Forestry University 2018; 46(1): 63–67.
67. Li M, Wang B, Fan W, et al. Simulation of forest net primary production and the effects of fire disturbance in Northeast China. Chinese Journal of Plant Ecology 2015; 39(4): 322–332.
68. Wang J, Xiao X, Baigain R, et al. Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing 2019; 154(6): 189–201.
69. Li W, Niu Z, Liang X, et al. Geostatistical modeling using lidar-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling. International Journal of Applied Earth Observation and Geoinformation 2015; 41(4): 88–98.
70. Chi H, Huang J, Qiu J, et al. Estimation of forest aboveground biomass using ICESat/GLAS data and Landsat/ETM+ imagery. Science of Surveying and Mapping 2018; 43(4): 9–16.
71. yuan W, Cai W, Liu D, et al. Satellite-based vegetation production models of terrestrial ecosystem: An overview. Advances in Earth Science 2014; 29(5): 541–550.
72. Li D, Zhang C, Ju W, et al. Forest net primary productivity dynamics and driving forces in Jiangxi Province, China. Chinese Journal of Plant Ecology 2016; 40(7): 643–657.
73. Fang O, Wang Y, Shao X. The effect of climate on the net primary productivity (NPP) of Pinus koraiensis in the Changbai Mountains over the past 50 years. Trees 2016; 30(1): 281–294.
74. Kruse J, Rrnnenberg H, Adams MA. Three physiological parameters capture variation in leaf respiration of Eucalyptus grandis, as elicited by short-term changes in ambient temperature, and differing nitrogen supply. Plant, Cell & Environment 2018; 41(6): 1369–1382.
75. pan H, Huang P, Xu J. The spatial and temporal pattern evolution of vegetation NPP and its driving forces in the middle-lower areas of Min River based on geographical detector analyses. Acta Ecologica Sinica 2019; 39(20): 1–11.
76. Piao S, Zhang X, Chen A, et al. The impacts of climate extremes on the terrestrial carbon cycle: A review. Science China Earth Sciences 2019; 62(10): 1551–1563.
77. Yu Y, Chen J, Yang X, et al. Influence of site index on the relationship between forest net primary productivity and stand age. PloS One 2017; 12(5): 1–20.
78. Wu W, Yao S, Xu Z. Study on NPP of main forest types based on national forest inventory data in Jiangxi Province, China. Journal of Nanjing Forestry University (Natural Sciences Edition) 2019; 43(5): 193–198.
79. Riutta T, Malhi Y, Kho LK, et al. Logging disturbance shifts net primary productivity and its allocation in Bornean tropical forests. Global Change Biology 2018; 24(7): 2913–2928.
80. Wang B, Liu M, Zhang B. Dynamics of net production of Chinese forest vegetation based on forest inventory data. Forestry Resources Management 2009; (1): 35–43.
81. Cheng F, Liu S, Zhang Y, et al. Effects of land-use change on net primary productivity in Beijing based on the MODIS series. Acta Ecologica Sinica 2017; 37(18): 5924–5934.
82. Zheng Y, Zhou G. A forest vegetation NPP model based on NDVI. Chinese Journal of Plant Ecology 2000; 24(1): 9–12.
83. Lieth H. Modeling the primary productivity of the world. Nature and Resources 1972; 8(2): 5–10.
84. Zhou G, Zhang X. A natural vegetation NPP model. Chinese Journal of Plant Ecology 1995; 19(3): 193–200.
85. Chen X, Zeng Y. Spatial and temporal variability of the net primary production (NPP) and its relationship with climatic factors in subtropical mountainous and hilly regions of China: A case study of Hunan province. Acta Geographica Sinica 2016; 71(1): 35–48.
86. Yan H, Wang S, Billesbach D, et al. Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants. Ecological Modelling 2015; 297(11): 42–59.
87. Behera SK, Tripathi P, Behera MD, et al. Modeling net primary productivity of tropical deciduous forests in North India using bio-geochemical model. Biodiversity and Conservation 2019; 28(8/9): 1–17.
88. Fang J, Liu G, Xu S. Biomass and net production of forest vegetation in China. Acta Ecologica Sinica 1996; 16(5): 497–508.
89. Zhou G, Wang Y, Jiang Y, et al. Estimating biomass and net primary production from forest inventory data: A case study of China’s Larix forests. Forest Ecology and Management 2002; 169(1/2): 149–157.
90. Shen G, Zhai M. Senlin peiyuxue (Chinese) [Forest cultivation]. Beijing: China Forestry Press; 2011.
91. Hansen MC, Potapoy PV, Goetz SJ, et al. Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data. Remote Sensing of Environment 2016; 185(2): 221–232.
92. Liao K, Qi S, Wang C, et al. Estimation of forest aboveground biomass and canopy height in Jiangxi province using GLAS and landsat TM images. Remote Sensing Technology and Application 2018; 33(4): 713–720.
93. Jin S, Su Y, Gao S, et al. The transferability of random forest in canopy height estimation from multi-Source remote sensing data. Remote Sensing 2018; 10(8): 1008–1183.
DOI: https://doi.org/10.24294/sf.v4i2.1606
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