Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series.

[1]  José M. C. Pereira,et al.  Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires , 2017, Remote. Sens..

[2]  Rob J Hyndman,et al.  Phenological change detection while accounting for abrupt and gradual trends in satellite image time series , 2010 .

[3]  S. Flasse,et al.  An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah , 2001 .

[4]  E. Sano,et al.  The spectral changes of deforestation in the Brazilian tropical savanna , 2014, Environmental Monitoring and Assessment.

[5]  Joanne C. White,et al.  Forest Monitoring Using Landsat Time Series Data: A Review , 2014 .

[6]  C. Schaaf,et al.  Relationships between vegetation indices, fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation , 2017 .

[7]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[8]  Yuhan Rao,et al.  Land cover change detection by integrating object-based data blending model of Landsat and MODIS , 2016 .

[9]  S. Montenegro,et al.  Dinâmica do uso e ocupação do solo em uma bacia hidrográfica do semiárido brasileiro , 2014 .

[10]  Daniel Mauricio,et al.  Sinopsis taxonómica de las moscas parasitoides (Diptera: Tachinidae) de Colombia , 2020 .

[11]  J. F. Ribeiro,et al.  Fitofisionomias do bioma cerrado. , 1998 .

[12]  Nicholas J. Tate,et al.  A critical synthesis of remotely sensed optical image change detection techniques , 2015 .

[13]  Jan Verbesselt,et al.  Using spatial context to improve early detection of deforestation from Landsat time series , 2016 .

[14]  Carlos C. DaCamara,et al.  A User-Oriented Simplification of the ( $V,W$ ) Burn-Sensitive Vegetation Index System , 2016, IEEE Geoscience and Remote Sensing Letters.

[15]  N. Benson,et al.  Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio , 2006 .

[16]  Michael Schultz,et al.  Performance of vegetation indices from Landsat time series in deforestation monitoring , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[18]  Kenton Lee,et al.  The Spectral Response of the Landsat-8 Operational Land Imager , 2014, Remote. Sens..

[19]  Rosana Cristina Grecchi,et al.  Assessing the spatio-temporal rates and patterns of land-use and land-cover changes in the Cerrados of southeastern Mato Grosso, Brazil , 2013 .

[20]  Inácio T. Bueno,et al.  Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series , 2018, Remote. Sens..

[21]  Dongmei Chen,et al.  Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .

[22]  Michele Dalponte,et al.  Tree Species Classification in Boreal Forests With Hyperspectral Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[23]  L. Ferreiraa,et al.  Seasonal landscape and spectral vegetation index dynamics in the Brazilian Cerrado : An analysis within the Large-Scale Biosphere – Atmosphere Experiment in Amazônia ( LBA ) , 2003 .

[24]  Fausto Weimar Acerbi Júnior,et al.  Object-based land-cover change detection applied to Brazilian seasonal savannahs using geostatistical features , 2018 .

[25]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[26]  Xuehong Chen,et al.  A spectral gradient difference based approach for land cover change detection , 2013 .

[27]  O. Mutanga,et al.  Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review , 2010, Wetlands Ecology and Management.

[28]  D. Barrett,et al.  Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. , 2009 .

[29]  Jixian Zhang.  Multi-source remote sensing data fusion: status and trends , 2010 .

[30]  Warren B. Cohen,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation , 2010 .

[31]  Zhiqiang Yang,et al.  A LandTrendr multispectral ensemble for forest disturbance detection , 2018 .

[32]  Jan Verbesselt,et al.  Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series , 2016, PloS one.

[33]  Rosana Cristina Grecchi,et al.  Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach , 2015 .

[34]  Antonio Roberto Formaggio,et al.  Land use and land cover changes in the Brazilian Cerrado: A multidisciplinary approach to assess the impacts of agricultural expansion , 2014 .

[35]  Alfredo Huete,et al.  Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices , 2004 .

[36]  J. Pereira,et al.  On a new coordinate system for improved discrimination of vegetation and burned areas using MIR/NIR information , 2011 .

[37]  D. Roberts,et al.  Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .

[38]  J. A. Ratter,et al.  The Brazilian Cerrado Vegetation and Threats to its Biodiversity , 1997 .

[39]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[40]  Stephen V. Stehman,et al.  Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss , 2008 .

[41]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[42]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[43]  G. Fernandes,et al.  ENVIRONMENTAL HETEROGENEITY AND NATURAL REGENERATION IN RIPARIAN VEGETATION OF THE BRAZILIAN SEMI-ARID REGION , 2012 .

[44]  Geoffrey J. Hay,et al.  Object-based change detection , 2012 .

[45]  Pierre Defourny,et al.  Monitoring dry vegetation masses in semi-arid areas with MODIS SWIR bands , 2014 .

[46]  M. Herold,et al.  Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia , 2015 .

[47]  D. Lu,et al.  Change detection techniques , 2004 .

[48]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[49]  C. Brannstrom,et al.  Land change in the Brazilian Savanna (Cerrado), 1986–2002: Comparative analysis and implications for land-use policy , 2008 .

[50]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[51]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[52]  M. Herold,et al.  Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2 , 2018 .

[53]  Leonardo F. Peres,et al.  Retrieving Middle-Infrared Reflectance Using Physical and Empirical Approaches: Implications for Burned Area Monitoring , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Patrick Bogaert,et al.  Forest change detection by statistical object-based method , 2006 .

[55]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[56]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[57]  Tian Han,et al.  Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data , 2009 .

[58]  Pierre Hiernaux,et al.  Dry-season vegetation mass and cover fraction from SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the Sahel , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[59]  Qingxi Tong,et al.  Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance , 2014 .

[60]  J. Vogelmann,et al.  Monitoring forest changes in the southwestern United States using multitemporal Landsat data , 2009 .

[61]  R. Machado,et al.  Conservation of the Brazilian Cerrado , 2005 .

[62]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[63]  Zhe Zhu,et al.  Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications , 2017 .

[64]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[65]  Hurtado Abril,et al.  Generación de un índice espectro-temporal para la identificación de zonas afectadas por deforestación usando imágenes Landsat. , 2020 .