Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring

Hüttich, Christian; Herold, Martin; Wegmann, Martin; Cord, Anna F.; Strohbach, Ben J.; Schmullius, Christiane; Dech, Stefan

doi:10.1016/j.rse.2011.05.005

Cited by 56 publications

(33 citation statements)

References 52 publications

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“…The characteristic biome is subtropical savanna, where the cover is not discrete but rather characterized by a mixture of growth forms, primarily grasses, shrubs, and trees. Small patches and gradual transition between open and closed vegetation cover are typical features of these landscapes [44][45][46][47][48][49][50][51][52]. Such characterization has caused problems in not only differentiating between the grass, shrub, and tree vegetation cover, but also in making the use of spectral information from passive remote sensing a difficult challenge given the inherent complexities in its structure.…”

Section: Study Areamentioning

confidence: 99%

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo

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Berger

et al. 2016

Forests

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Abstract:The use of optical remote sensing data for savanna vegetation structure mapping is hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud cover and unpredictable weather conditions. Longwave microwave data, with its low sensitivity to clouds addresses some of these problems, but many space borne studies are still limited by low quality structural reference data. Terrestrial laser scanning (TLS) derived canopy cover and height metrics can improve aboveground biomass (AGB) prediction at both plot and landscape level. To date, few studies have explored the strength of TLS for vegetation structural mapping, and particularly few focusing on savannas. In this study, we evaluate the potential of high resolution TLS-derived canopy cover and height metrics to estimate plot-level aboveground biomass, and to extrapolate to a landscape-wide biomass estimation using multi-temporal L-band Synthetic Aperture Radar (SAR) within a 9 km 2 area savanna in Kruger National Park (KNP). We inventoried 42 field plots in the wet season and computed AGB for each plot using site-specific allometry. Canopy cover, canopy height, and their product were regressed with plot-level AGB over the TLS-footprint, while SAR backscatter was used to model dry season biomass for the years 2007, 2008, 2009, and 2010 for the study area. The results from model validation showed a significant linear relationship between TLS-derived predictors with field biomass, p < 0.05 and adjusted R 2 ranging between 0.56 for SAR to 0.93 for the TLS-derived canopy cover and height. Log-transformed AGB yielded lower errors with TLS metrics compared with non-transformed AGB. An assessment of the backscatter based on root mean square error (RMSE) showed better AGB prediction with cross-polarized (RMSE = 6.6 t/ha) as opposed to co-polarized data (RMSE = 6.7 t/ha), attributed to volume scattering of woody vegetation along river valleys and streams. The AGB change analysis showed 32 ha (3.5%) of the 900 ha experienced AGB loses above an average of 5 t/ha per annum, which can mainly be attributed to the falling of trees by mega herbivores such as elephants. The study concludes that SAR data, especially L-band SAR, can be used in the detection of small changes in savanna vegetation over time.

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Section: Study Areamentioning

confidence: 99%

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo

Nickless

Berger

et al. 2016

Forests

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“…These temporal signatures can be useful to discriminate vegetation and land cover types that are spectrally distinct only during short periods of time throughout the year. Time series of MODIS Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) have been successfully used to characterize vegetation types in different environmental settings [12][13][14]. For example, MODIS Vegetation Index (VI) time series have improved the classification of abandoned farmland, different crop types, and semi-arid vegetation by capturing the specific phenological pattern of each land cover type.…”

Section: Introductionmentioning

confidence: 99%

Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series

et al. 2013

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Abstract:We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia's biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and short-wave infrared (SWIR) reflectance data to develop phenological metrics. These phenological metrics were used to classify rubber plantations and forests with the Random Forest classification algorithm. We evaluated which key phenological characteristics were important to discriminate rubber plantations and natural forests by estimating the influence of each metric on the classification accuracy. As a benchmark, we compared the best classification with a classification based on the full, fitted time series data. Overall classification accuracies derived from EVI and SWIR time series alone were 64.4% and 67.9%, respectively. Combining the phenological metrics from EVI and SWIR time series improved the accuracy to 73.5%. Using the full, smoothed time series data instead of metrics derived from the time series improved the overall accuracy only slightly (1.3%), indicating that the phenological metrics were sufficient to explain the seasonal changes captured by the MODIS time series. The results demonstrate a promising utility of phenological metrics for mapping and monitoring rubber expansion with MODIS.

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“…Semi-arid ecosystems with typically heterogeneous landscapes consisting of mixed vegetation (mosaics of trees, shrubs, and grassland) and small scale land use patterns pose challenges (e.g., mixed pixels) for remote sensing (RS) based medium resolution land cover (LC) mapping [1,2]. This is reflected by relatively low map accuracies in medium resolution (250-500 m) global LC datasets for those areas [3,4].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Potential of PROBA-V Satellite Image Time Series for Improving LC Classification in Semi-Arid African Landscapes

et al. 2016

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Satellite based land cover classification for Africa's semi-arid ecosystems is hampered commonly by heterogeneous landscapes with mixed vegetation and small scale land use. Higher spatial resolution remote sensing time series data can improve classification results under these difficult conditions. While most large scale land cover mapping attempts rely on moderate resolution data, PROBA-V provides five-daily time series at 100 m spatial resolution. This improves spatial detail and resilience against high cloud cover, but increases the data load. Cloud-based processing platforms can leverage large scale land cover monitoring based on such finer time series. We demonstrate this with PROBA-V 100 m time series data from 2014-2015, using temporal metrics and cloud filtering in combination with in-situ training data and machine learning, implemented on the ESA (European Space Agency) Cloud Toolbox infrastructure. We apply our approach to two use cases for a large study area over West Africa: land-and forest cover classification. Our land cover classification reaches a 7% to 21% higher overall accuracy when compared to four global land cover maps (i.e., Globcover-2009, Cover-CCI-2010, MODIS-2010. Our forest cover classification shows 89% correspondence with the Tropical Ecosystem Environment Observation System (TREES)-3 forest cover data which is based on spatially finer Landsat data. This paper illustrates a proof of concept for cloud-based "big-data" driven land cover monitoring. Furthermore, we show that a wide range of temporal metrics can be extracted from detailed PROBA-V 100 m time series data to continuously optimize land cover monitoring.

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Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring

Cited by 56 publications

References 52 publications

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series

Evaluating the Potential of PROBA-V Satellite Image Time Series for Improving LC Classification in Semi-Arid African Landscapes

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