Remote sensing-supported vegetation parameters for regional climate models: a brief review

Latifi, Hooman; Gálos, Borbála

doi:10.3832/ifor0543-003

Cited by 10 publications

(9 citation statements)

References 36 publications

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“…Compared with the Pathfinder AVHRR land (PAL) dataset, the GIMMS NDVI has further eliminated the effects of calibration, geometry, viewing angle and volcanic aerosols (Tucker et al 2004). One of the most significant datasets available for large-scale vegetation studies was recently demonstrated (Myneni et al 1997;Nemani et al 2003;Rikie et al 2007), not only because of its relative continuity in temporal and spatial terms but also due to its high correlations with some vegetation parameters such as net primary production (NPP), leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by the vegetation canopy (FPAR) (Goward et al 1985;Myneni and Williams 1994;Purevdorj et al 1998;Latifi and Galos 2010).…”

Section: Ndvi Datasetmentioning

confidence: 99%

Assessing vegetation dynamics and their relationships with climatic variability in Heilongjiang province, northeast China

Liu

Cai

et al. 2011

Environ Earth Sci

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In this study, the vegetation dynamics in Heilongjiang province and their relationships with climate variability were assessed using normalized difference vegetation index (NDVI) and meteorological datasets from 1981 to 2003. The conclusions from our results are as follows: (1) After 1981, vegetation cover, as indicated by the NDVI, exhibited an insignificant increasing tendency. However, the inter-annual variations of the NDVI showed apparent spatial differentiations.(2) The inter-annual changes of the NDVI were different from season to season. The spring and autumn NDVI values increased, while the summer and winter NDVI decreased. (3) The annual NDVI was significantly correlated with precipitation. Thus, as compared to temperature, precipitation was the dominant climatic factor affecting the vegetation dynamics in Heilongjiang province. (4) The trend in the NDVI showed a marked homogeneity corresponding to regional and seasonal variations in climate. Additionally, land use changes also play an important role in influencing the NDVI trends over some regions. All of these findings will enrich our knowledge of the natural forces that impact the stability of boreal ecosystems and provide a scientific basis for the environmental management in Heilongjiang province in response to climate change and human activities.

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Section: Ndvi Datasetmentioning

confidence: 99%

Assessing vegetation dynamics and their relationships with climatic variability in Heilongjiang province, northeast China

Liu

Cai

et al. 2011

Environ Earth Sci

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“…Thus, LAI is one of the key biophysical variables required by many process models describing the soil/plant/atmosphere system (Baret & Buis 2008, Latifi & Galos 2010. Because canopies and some of their characteristics can be directly observed from above, LAI is among the major variables of interest in remote sensing analyses (Lee et al 2004), and its importance has led to considerable efforts to map its distribution over a variety of spatial and temporal scales (Cohen et al 2003, Morisette et al 2006, Zhao & Popescu 2009, Latifi & Galos 2010, Tang et al 2012. The majority of studies used (semi-) empirical relationships between LAI and combinations of spectral bands, namely vegetation indexes (VI), for LAI mapping (Baret & Buis 2008, Vuolo et al 2010.…”

Section: Introductionmentioning

confidence: 99%

Mapping Leaf Area Index in subtropical upland ecosystems using RapidEye imagery and the randomForest algorithm

Beckschäfer¹,

Fehrmann²,

Harrison³

et al. 2014

iForest

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Canopy leaf area, frequently quantified by the Leaf Area Index (LAI), serves as the dominant control over primary production, energy exchange, transpiration, and other physiological attributes related to ecosystem processes. Maps depicting the spatial distribution of LAI across the landscape are of particularly high value for a better understanding of ecosystem dynamics and processes, especially over large and remote areas. Moreover, LAI maps have the potential to be used by process models describing energy and mass exchanges in the biosphere/atmosphere system. In this article we assess the applicability of the RapidEye satellite system, whose sensor is optimized towards vegetation analyses, for mapping LAI along a disturbance gradient, ranging from heavily disturbed shrub land to mature mountain rainforest. By incorporating image texture features into the analysis, we aim at assessing the potential quality improvement of LAI maps and the reduction of uncertainties associated with LAI maps compared to maps based on Vegetation Indexes (VI) solely. We identified 22 out of the 59 image features as being relevant for predicting LAI. Among these, especially VIs were ranked high. In particular, the two VIs using RapidEye’s RED-EDGE band stand out as the top two predictor variables. Nevertheless, map accuracy as quantified by the mean absolute error obtained from a 10-fold cross validation (MAE_CV) increased significantly if VIs and texture features are combined (MAE_CV = 0.56), compared to maps based on VIs only (MAE_CV = 0.62). We placed special emphasis on the uncertainties associated with the resulting map addressing that map users often treat uncertainty statements only in a pro-forma manner. Therefore, the LAI map was complemented with a map depicting the spatial distribution of the goodness-of-fit of the model, quantified by the mean absolute error (MAE), used for predictive mapping. From this an area weighted MAE (= 0.35) was calculated and compared to the unweighted MAE of 0.29. Mapping was done using randomForest, a widely used statistical modeling technique for predictive biological mapping

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“…The use of high-resolution true color and false color (VNIR) RGB composite aerial photos (also stereoscopic solutions) and multispectral satellite images, has become much easier during the last few decades. A multitude of environmental research has confirmed the possibility of using this geodata supported by GIS analyses for remote and automated environment monitoring in the area of nature protection, forestry, meteorology, climatology and hydrology (Coops, White 2003, Corona et al 2008, Drzewiecki et al 2014, Latifi, Galos 2010, Wężyk et al 2013. The role played by image 6 MARTA SzOSTAK, PIOTR WężyK, PAWEł HAWRyłO, MARTA PUCHAłA classification based on multi-source, multi-resolution and multi-temporal remote sensed data is ever increasing in the environmental studies field.…”

Section: Introductionmentioning

confidence: 93%

Monitoring the Secondary Forest Succession and Land Cover/Use Changes of the Błędów Desert (Poland) Using Geospatial Analyses

Szostak

Wężyk

Hawryło

et al. 2016

Quaestiones Geographicae

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szostAk M., wężyk P., HAwryło P., PucHAłA M., 2016. Monitoring the secondary forest succession and land cover/use changes of the Błędów Desert (Poland) using geospatial analyses. Quaestiones Geographicae 35(3), Bogucki Wydawnictwo Naukowe, Poznań, pp. 5-13, 5 figs, 1 table.ABstrAct: The role of image classification based on multi-source, multi-temporal and multi-resolution remote sensed data is on the rise in the environmental studies due to the availability of new satellite sensors, easier access to aerial orthoimages and the automation of image analysis algorithms. The remote sensing technology provides accurate information on the spatial and temporal distribution of land use and land cover (LULC) classes. The presented study focuses on LULC change dynamics (especially secondary forest succession) that occurred between 1974 and 2010 in the Błędów Desert (an area of approx. 1210 ha; a unique refuge habitat -NATURA 2000; South Poland). The methods included: photointerpretation and on-screen digitalization of KH-9 CORONA (1974), aerial orthoimages (2009) and satellite images (LANDSAT 7 ETM + , 1999 and BlackBridge -RapidEye, 2010) and GIS spatial analyses. The results of the study have confirmed the high dynamic of the overgrowth process of the Błędów Desert by secondary forest and shrub vegetation. The bare soils covered 19.3% of the desert area in 1974, the initial vegetation and bush correspondingly 23.1% and 30.5%. In the years 2009/2010 the mentioned classes contained: the bare soils approx. 1.1%, the initial vegetation -8.7% and bush -15.8%. The performed classifications and GIS analyses confirmed a continuous increase in the area covered by forests, from 11.6% (KH-9) up to 24.2%, about 25 years later (LANDSAT 7) and in the following 11 years, has shown an increase up to 35.7% (RapidEye 2010).

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Remote sensing-supported vegetation parameters for regional climate models: a brief review

Cited by 10 publications

References 36 publications

Assessing vegetation dynamics and their relationships with climatic variability in Heilongjiang province, northeast China

Assessing vegetation dynamics and their relationships with climatic variability in Heilongjiang province, northeast China

Mapping Leaf Area Index in subtropical upland ecosystems using RapidEye imagery and the randomForest algorithm

Monitoring the Secondary Forest Succession and Land Cover/Use Changes of the Błędów Desert (Poland) Using Geospatial Analyses

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