Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Hirschmugl, Manuela; Sobe, Carina; Deutscher, Janik; Schardt, Mathias

doi:10.3390/land7040116

Cited by 22 publications

(18 citation statements)

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“…Combining optical and radar time series also significantly improved our land cover classification, with radar data complementing optical data in clouded areas well. Our results support the conclusions of Hirschmugl et al () who reported improved accuracy in the detection of deforestation events in Malawi using time series of Sentinel‐2 and/or Sentinel‐1 data compared to monotemporal data. Steinhausen, Wagner, Narasimhan, and Waske () also found an improved accuracy of land cover mapping in monsoon regions in India when increasing the number of Sentinel‐1 scenes to one Sentinel‐2 scene, but Mercier et al () found little improvement when adding Sentinel‐1 time series to the one Sentinel‐2 scene considered for mapping land cover in a forest–agriculture mosaic in Brazil.…”

Section: Discussionsupporting

confidence: 92%

“…Temporal compositing (Griffiths, van der Linden, Kuemmerle, & Hostert, ; Vancutsem, Pekel, Bogaert, & Defourny, ) of a time series has sometimes been considered to inform land cover mapping, but a large amount of temporal information is lost during this process. Hence, the utility of combining optical and radar satellite image time series for land cover mapping has hardly ever been assessed; known applications include the mapping of agricultural landscapes and the detection of deforestation events (Hirschmugl, Sobe, Deutscher, & Schardt, ; Inglada et al, ; Kuenzer et al, ; Reiche, Verbesselt, Hoekman, & Herold, ). To our knowledge, this type of approach has never been considered for mapping land cover in tropical regions of conservation interest with persistent cloud cover and limited level of seasonality.…”

Section: Introductionmentioning

confidence: 99%

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Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series

et al. 2020

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The recent availability of high spatial and temporal resolution optical and radar satellite imagery has dramatically increased opportunities for mapping land cover at fine scales. Fusion of optical and radar images has been found useful in tropical areas affected by cloud cover because of their complementarity. However, the multitemporal dimension these data now offer is often neglected because these areas are primarily characterized by relatively low levels of seasonality and because the consideration of multitemporal data requires more processing time. Hence, land cover mapping in these regions is often based on imagery acquired for a single date or on an average of multiple dates. The aim of this work is to assess the added value brought by the temporal dimension of optical and radar time series when mapping land cover in tropical environments. Specifically, we compared the accuracies of classifications based on (a) optical time series, (b) their temporal average, (c) radar time series, (d) their temporal average, (e) a combination of optical and radar time series and (f) a combination of their temporal averages for mapping land cover in Jambi province, Indonesia, using Sentinel‐1 and Sentinel‐2 imagery. Using the full information contained in the time series resulted in significantly higher classification accuracies than using temporal averages (+14.7% for Sentinel‐1, +2.5% for Sentinel‐2 and +2% combining Sentinel‐1 and Sentinel‐2). Overall, combining Sentinel‐2 and Sentinel‐1 time series provided the highest accuracies (Kappa = 88.5%). Our study demonstrates that preserving the temporal information provided by satellite image time series can significantly improve land cover classifications in tropical biodiversity hotspots, improving our capacity to monitor ecosystems of high conservation relevance such as peatlands. The proposed method is reproducible, automated and based on open‐source tools satellite imagery.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series

et al. 2020

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“…We built RF disturbance probability models for each observation of Landsat 8 and Sentinel-1. Because RF classifiers can be used to predict the probabilities of binary responses [70], several studies have used them for probability mapping with remote sensing data (e.g., References [50,71]). In this study, we predicted the probability of forest disturbances, defined as any discrete event that caused a reduction of forest canopy.…”

Section: Rf Disturbance Probability Models For Landsat 8 and Sentinel-1mentioning

confidence: 99%

“…Several studies have investigated the combination of dense time series optical and SAR data using different approaches (e.g., [22,35,48,49]). One approach is to fuse the time series signals from optical and SAR data in the same model [50]. Reiche et al [51] fused Landsat Normalized Difference Vegetation Index (NDVI) and ALOS/PALSAR backscatter to detect deforestation using the correlation between NDVI-PALSAR time series.…”

Section: Introductionmentioning

confidence: 99%

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

2019

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The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

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“…By using multi-source data, the classification accuracy is improved compared to single data source. This has been shown, for example, with a combination of optical data and synthetic aperture radar (SAR, Congo Basin and Malawi city, Mzimba) [8,9]. The fusion of different frequencies (L-and P-band) of SAR products has also received much attention in recent years [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Classification and Landscape Metrics for Mapping and Spatial Pattern Change Analysis of Tropical Forest Types in Thua Thien Hue Province, Vietnam

Tuong

Tani

Wang

et al. 2019

Forests

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Research Highlights: In this study, we classified natural forest into four forest types using time-series multi-source remotely sensed data through a proposed semi-supervised model developed and validated for mapping forest types and assessing forest transition in Vietnam. Background and Objectives: Data on current forest state and changes detection are always essential for forest management and planning. There is, therefore, a need for improved tools to classify and evaluate forest dynamics more accurately and effectively. Our objective is to develop such tools using a semi-supervised model and landscape metrics to classify and map changes in natural forest types by using multi-source remotely sensed data. Materials and Methods: A combination of Landsat data with PALSAR and PALSAR-2 was used for forest classification through the proposed semi-supervised model. This model turned a kernel least square into a self-learning algorithm, trained by a small number of samples with given labels, and then used this classifier to assign labels to the unlabeled data. The overall accuracy, kappa, user’s accuracy, and producer’s accuracy were used to evaluate the classification accuracy by comparing the classified image with the results of ground truth interpretation. Based on the classified images, forest transition was evaluated using certain landscape metrics at the class and landscape levels. Results: The multi-source data approach achieved improved discrimination of forest types compared to only using single data (optical or radar data). Good classification accuracies were obtained, with kappas of 0.81, 0.76, and 0.74 for the years 2007, 2010, and 2016, respectively. The analysis of landscape metrics indicated that there were different behaviors in the four forest types, as well as provided much information about the trends in spatial pattern changes. Conclusions: This study highlights the utilization of a semi-supervised model in forest classification, and the analysis of forest transition using landscape metrics. However, future research should include a comparison of different models to estimate the improvement of the proposed model. Another important study that should be conducted is to test the proposed method on larger areas.

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Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Cited by 22 publications

References 59 publications

Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series

Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

Semi-Supervised Classification and Landscape Metrics for Mapping and Spatial Pattern Change Analysis of Tropical Forest Types in Thua Thien Hue Province, Vietnam

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