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Forest Degradation Estimation Through Trend Analysis of Annual Time Series NDVI, NDMI and NDFI (2010–2020) Using Landsat Images

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Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Forest degradation plays an important role in greenhouse gas (GHG) emissions and climate change. Previous research has shown that more GHG has been emit-ted through forest degradation than deforestation. Therefore, its monitoring and estimation is important for strategy design to combat climate change. In this work, we intend to estimate forest degradation in Ayuquila River Basin, Mexico through vegetation trend analysis using annual time series vegetation indices (2010–2020) specifically, NDVI (normalized difference vegetation index), NDMI (normalized difference moisture index), and NDFI (normalized difference fraction index) derived from Landsat images. The vegetation trend analysis was carried out using a linear regression model and tested by Mann-Kendall for significance. Slope coefficient was used to indicate the vegetation trend: positive slope indi-cates vegetation regrowth and negative slope indicates vegetation degradation. For forest degradation, only significant trends with negative slope were analyzed (p < 0.05). To discard negative trends due to deforestation, a forest mask was ap-plied both at the beginning and at the end of the analysis. The accuracy assessment showed that the forest degradation estimation by time series NDVI obtained the highest overall accuracy of 81.33%, followed by NDMI with 73.33% and fi-nally NDFI with 72%.

Keywords

  • Forest Degradation
  • Time series analysis
  • Linear regression model
  • Spectral mixture analysis
  • NDVI
  • NDMI
  • NDFI

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References

  • Anand A, Singh SK, Kanga S (2018) Estimating the change in forest cover density and predicting NDVI for west Singhbhum using linear regression. Int J Environ Rehabil Conserv 9:193–203

    Google Scholar 

  • Asner GP, Knapp DE, Broadbent EN, Oliveira PJC, Keller M, Silva JN (2005) Selective logging in the Brazilian amazon. Science 310:480–482

    Google Scholar 

  • Baccini A, Walker W, Carvalho L, Farina M, Sulla-Menashe D, Houghton RA (2017) Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358(6360):230–234

    MathSciNet  CrossRef  Google Scholar 

  • Blanc L, Gond V, Minh DH (2016) Remote sensing and measuring deforestation

    Google Scholar 

  • Bullock EL, Woodcock CE, Olofsson P (2020) Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis. Remote Sens Environ 238:110968

    Google Scholar 

  • Congalton K, Green RG (2019) Assessing the accuracy of remotely sensed data - principles and practices, 3rd edn. CRC Press, Boca Raton, USA

    Google Scholar 

  • Cuevas RNNMGFSM (1998) El bosque tropical caducifolio en la reserva de la biosferasierra manantlan, jalisco-colima, méxico. Bol, IBUG

    Google Scholar 

  • Defries RS, Hansen MC (2000) Global continuous fields of vegetation characteristics: a linear mixture model applied to multi-year 8 km AVHRR data. Int J Remote Sens 21(6–7):1389–1414

    CrossRef  Google Scholar 

  • Dupuis Chloé, Lejeune Philippe, Michez Adrien, Fayolle Adeline (2020) How can remote sensing help monitor tropical moist forest degradation? A systematic review. Remote Sens 12:1087

    CrossRef  Google Scholar 

  • Dutrieux LP, Jakovac CC, Latifah SH, Kooistra L (2016) Reconstructing land use history from Landsat time-series. case study of a swidden agriculture system in Brazil. Int J Appl Earth Obs Geoinf 47:112–124

    Google Scholar 

  • Food and Agriculture Organization (2010) Global forest resources assessment. FAO

    Google Scholar 

  • Food and Agriculture Organization (2011) Assessing forest degradation, towards the development of globally applicable guidelines. FAO

    Google Scholar 

  • Gilbert RO (1987) Statistical methods for environmental pollution monitoring

    Google Scholar 

  • GOFI (2016) Integration of remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests: methods and guidance. Global forest observation initiative

    Google Scholar 

  • Grogan Kenneth, Pflugmacher Dirk, Hostert Patrick, Verbesselt Jan, Fensholt Rasmus (2016) Mapping clearances in tropical dry forests using breakpoints, trend, and seasonal components from MODIS time series: Does forest type matter? Remote Sens 8:657

    CrossRef  Google Scholar 

  • Kendall MG (1975) Rank correlation methods

    Google Scholar 

  • Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly landsat time series: 1. landtrendr - temporal segmentation algorithms. Remote Sens Environ 114:2897–2910

    Google Scholar 

  • Mann HB (1945) Non-parametric tests against trend, econometrica

    Google Scholar 

  • Olofsson P (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ

    Google Scholar 

  • Pearson TR, Bernal B, Hagen SC, Walker SM, Melendy LK, Delgado G (2018) Remote assessment of extracted volumes and greenhouse gases from tropical timber harvest. Res Lett 13:065010

    Google Scholar 

  • Priyanka JFV (2020) “freygeospatial,". https://freygeospatial.github.io/PM25-TimeSeries-R-Tutorial/. Accessed 18 Mayo 2021

  • Skutsch M, Martinez R, Morfin J, Allende T, Vega E, Morales J, Ghilardi A, Jardel E (2012) Analisis de cambio de cobertura y uso del suelo, escenario de referencia de car-bono y diseño preliminar del mecanismo de monitoreo, reporte y verification en los diez municipios de la junta intermunicipal de rio ayuquila [land cover and land use change analysis, reference scenario of carbon and preliminary design of the monitoring, reporting and verification system in the ten municipals of rio ayuquila], jalisco. Report

    Google Scholar 

  • Tarazona Y, Miyasiro-López M (2020) Monitoring tropical forest degradation using remote sensing. challenges and opportunities in the Madre de Dios region, Peru. Remote Sens Appl Soc Environ 19:100337

    Google Scholar 

  • Team RC (2020) A language and environment for statistical computing. R foundation for statistical computing

    Google Scholar 

  • Zhu Zhe, Yingchun Fu, Woodcock Curtis E, Olofsson Pontus, Vogelmann James E, Holden Christopher, Wang Min, Dai Shu, Yang Yu (2016) Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: a case study from Guangzhou, china (2000–2014). Remote Sens Environ 185:243–257

    CrossRef  Google Scholar 

Download references

Acknowledgements

This work was funded by the Consejo Nacional de Ciencia y Tecnología (CONACYT) ‘Ciencia básica’ SEP-285349 “Análisis del patrón espacial de la degradación en selvas y bosques de México con percepción remota en múltiples escalas en el tiempo y espacio”. The authors are grateful of the help from Hind Taud in compiling the PDF file in Latex.

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Delgado-Moreno, D., Gao, Y. (2022). Forest Degradation Estimation Through Trend Analysis of Annual Time Series NDVI, NDMI and NDFI (2010–2020) Using Landsat Images. In: Tapia-McClung, R., Sánchez-Siordia, O., González-Zuccolotto, K., Carlos-Martínez, H. (eds) Advances in Geospatial Data Science. iGISc 2021. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-98096-2_11

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