Tree cover and forest cover dynamics in the Mekong Basin from 2001 to 2011

Leinenkugel, Patrick; Wolters, Michel L.; Oppelt, Natascha; Kuenzer, Claudia

doi:10.1016/j.rse.2014.10.021

Cited by 41 publications

(24 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the regions with high daytime LST variability in the Lower Mekong Basin strongly agree with areas which underwent permanent tree cover loss between 2000 and 2011 (delineated by Leinenkugel et al 2014). These areas are mainly in Cambodia.…”

Section: Analysis Of the Variabilitysupporting

confidence: 52%

“…A layer with land use classes from Leinenkugel et al (2013b) For a detailed analysis of LST_8day change in deforested areas, a forest change layer from Leinenkugel et al (2014) was used. The layer was regridded from lat/lon to sinusoidal projection.…”

Section: Methodsmentioning

confidence: 99%

“…There are also other small spots of high variability in Cambodia which might point to additional deforestation activities. The Khorat Plateau in Thailand also shows high daytime LST variability, yet, according to Leinenkugel et al (2014) only few areas are affected there by deforestation. The effect of deforestation on LST_8day is discussed further in Sect.…”

Section: Analysis Of the Variabilitymentioning

confidence: 99%

See 2 more Smart Citations

Analysing a 13 Years MODIS Land Surface Temperature Time Series in the Mekong Basin

Frey

Kuenzer

2015

Remote Sensing Time Series

Self Cite

View full text Add to dashboard Cite

Land surface temperature (LST) is an important parameter in the climate system, impacting vegetation development, snow cover, runoff, and human livelihoods. Knowledge of LST dynamics can furthermore be used as an indicator for climate variability and change. LST is regularly measured from satellite sensors on a broad spatial scale and with a high temporal resolution. In this research, MODIS (Moderate Resolution Imaging Spectroradiometer) sensor data are used to assess the spatial and temporal patterns of LST in the Mekong Basin (MB) including its temporal variability. The dataset contains 13 years of MODIS LST data, a unique measurement time series in terms of resolution, accuracy, and homogeneity. In the analysis a temporal granularity of 8-days was used. The MB was divided into six physiographically homogenous regions. The height and magnitude of annual LST curves differ between the regions and prove to be strongly dependent on the topography. Large intra-annual magnitudes and low temperatures (daytime/nighttime annual regional means are 14 C/À7 C) are found in the northern areas, mainly in the high-lying Tibetan Plateau. The more southern areas are characterized by low LST seasonality and high temperatures (daytime/nighttime annual regional means of these regions range from 25 C to 30 C/19 C to 25 C). The year-to-year variability of LST is similar in all regions (regional weekly daytime/nighttime deviations lower than 4 C/6 C, except for the Tibetan Plateau, where regional weekly daytime/nighttime deviations reach 6 C/18 C). In summer, 42 % of daytime LST could be explained by topographic height. In winter and in nighttime scenes, topography explained 89-97 % of the LST distribution. Land use and land use change further influence the LST pattern, mainly in the daytime. An example of rising LST due to deforestation is given. This study allows for an improved understanding of temperature dynamics in one of the world's largest river basins.

show abstract

Section: Analysis Of the Variabilitysupporting

confidence: 52%

Section: Methodsmentioning

confidence: 99%

Section: Analysis Of the Variabilitymentioning

confidence: 99%

See 1 more Smart Citation

Analysing a 13 Years MODIS Land Surface Temperature Time Series in the Mekong Basin

Frey

Kuenzer

2015

Remote Sensing Time Series

Self Cite

View full text Add to dashboard Cite

show abstract

“…In tropical regions, deforestation has been the predominant mode of land use changes [1,2]. It was estimated that deforestation in Southeast Asia resulted in a forest area loss of 43 × 10 6 ha between 1880 and 1980, equivalent to 28% of the initial area in 1880 [3].…”

Section: Introductionmentioning

confidence: 99%

Variations of Energy Fluxes and Ecosystem Evapotranspiration in a Young Secondary Dry Dipterocarp Forest in Western Thailand

2017

View full text Add to dashboard Cite

Deforestation, followed by abandonment and forest regeneration, has become one of the dominant types of land cover changes in the tropics. This study applied the eddy covariance (EC) technique to quantify the energy budget and evapotranspiration in a regenerated secondary dry dipterocarp forest in Western Thailand. The mean annual net radiation was 126.69, 129.61, and 125.65 W m −2 day −1 in 2009, 2010, and 2011, respectively. On average, fluxes of this energy were disaggregated into latent heat (61%), sensible heat (27%), and soil heat flux (1%). While the number of energy exchanges was not significantly different between these years, there were distinct seasonal patterns within a year. In the wet season, more than 79% of energy fluxes were in the form of latent heat, while during the dry season, this was in the form of sensible heat. The energy closure in this forest ecosystem was 86% and 85% in 2010 and 2011, respectively, and varied between 84-87% in the dry season and 83-84% in the wet season. The seasonality of these energy fluxes and energy closure can be explained by rainfall, soil moisture, and water vapor deficit. The rates of evapotranspiration also significantly varied between the wet (average 6.40 mm day −1 ) and dry seasons (3.26 mm day −1 ).

show abstract

“…Socio-economic transformation leads -among other consequences -to the intensification of agriculture and forestry, the cultivation of formerly non-arable land, and the expansion of infrastructure, all leading to a decline of natural ecosystems. Deforestation of the planet's last primary and secondary forests aims at the provision of space for monoculture plantations or pastures (Leinenkugel et al 2014;Broich et al 2011;Kuemmerle et al 2009), wetlands are drained to create space for cultivating livestock and crops (Rebelo et al 2009), rivers and lakes are harnessed (Grumbine and Pandit 2013), and irrigation schemes extend the boundaries of cultivatable land at the cost of groundwater depletion and surface subsidence (Fielding et al 1998;Amelung et al 1999;Higgins et al 2013). Urbanization leads to increasing pressure on natural ecosystems (Taubenb€ ock et al 2012;Haas and Ban 2014) and brings with it soil, water and air pollution (Duh et al 2006;Ren et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Time Series Revealing Land Surface Dynamics: Status Quo and the Pathway Ahead

Kuenzer

Dech

Wagner

2015

Remote Sensing Time Series

Self Cite

View full text Add to dashboard Cite

The face of our planet is changing at an unprecedented rate. Forest ecosystems diminish at alarming speed, urban and agricultural areas expand into the surrounding natural space, aquaculture is spreading, sea level rise leads to changes in coastal ecosystems, and even without obvious land cover change, land use intensity may change and complex ecosystems may undergo transient changes in composition. Satellite based earth observation is a powerful means to monitor these changes, and especially time series analysis holds the potential to reveal long term land surface dynamics. Whereas in past decades time series analysis was an elaborate undertaking mostly performed by a limited number of experts using coarse resolution data, attention shifts nowadays to open source tools and novel techniques for analyzing time series and the utilization of the same for numerous environmental applications. The reasons are the pressing call for climate-relevant, long term data analyses and value added products revealing past land surface dynamics and trends, the growing demand for global data sets, and the opening up of multidecadal remote sensing data archives, all at a time of considerablyimproved hardware power, computer literacy, and a general trend towards cloud solutions and available open source algorithms and programming languages. This chapter presents a comprehensive overview of time series analysis. We introduce currently orbiting optical, radar, and thermal infrared sensors and elucidate which of them are suitable for long term monitoring tasks based on remote sensing time

show abstract

Tree cover and forest cover dynamics in the Mekong Basin from 2001 to 2011

Cited by 41 publications

References 60 publications

Analysing a 13 Years MODIS Land Surface Temperature Time Series in the Mekong Basin

Analysing a 13 Years MODIS Land Surface Temperature Time Series in the Mekong Basin

Variations of Energy Fluxes and Ecosystem Evapotranspiration in a Young Secondary Dry Dipterocarp Forest in Western Thailand

Remote Sensing Time Series Revealing Land Surface Dynamics: Status Quo and the Pathway Ahead

Contact Info

Product

Resources

About