Soil chemical changes after tropical forest disturbance and conversion: The hydrological perspective

1998 ◽  
pp. 45-61 ◽  
Author(s):  
L. A. Sampurno Bruijnzeel
Keyword(s):  
Tropical Forest ◽  
Forest Disturbance ◽  
Chemical Changes

scholarly journals Effect of disturbance on plant species abundance and density distribution in tropical forest of Sunsari district, Eastern Nepal

2016 ◽  
Vol 6 (1) ◽  
pp. 1-12
Author(s):  
Tilak Prasad Gautam ◽  
Tej Narayan Mandal
Keyword(s):  
Carbon Sequestration ◽  
Species Composition ◽  
Tropical Forest ◽  
Tropical Forests ◽  
Tree Species ◽  
Forest Disturbance ◽  
Species Abundance ◽  
Forest Degradation ◽  
Tree Density ◽  
Shrub Species

The disappearance of global tropical forests due to deforestation and forest degradation has reduced the biodiversity and carbon sequestration capacity. In these contexts, present study was carried out to understand the species composition and density in the undisturbed and disturbed stands of moist tropical forest located in Sunsari district of eastern Nepal. Study revealed that the forest disturbance has reduced the number of tree species by 33% and tree density by 50%. In contrary, both number and density of herb and shrub species have increased with forest disturbance.

scholarly journals SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery

2021 ◽  
Vol 252 ◽  
pp. 112159
Author(s):  
Marie Ballère ◽  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
Thuy Le Toan ◽  
Thierry Koleck ◽  
...  
Keyword(s):  
Tropical Forest ◽  
French Guiana ◽  
Forest Disturbance ◽  
Optical Imagery ◽  
Sar Data

Can VIIRS continue the legacy of MODIS for near real-time monitoring of tropical forest disturbance?

2020 ◽  
Vol 249 ◽  
pp. 112024 ◽  
Author(s):  
Xiaojing Tang ◽  
Eric L. Bullock ◽  
Pontus Olofsson ◽  
Curtis E. Woodcock
Keyword(s):  
Tropical Forest ◽  
Real Time ◽  
Forest Disturbance ◽  
Real Time Monitoring

Changes in dung beetle communities along a gradient of tropical forest disturbance in South-East Asia

2009 ◽  
Vol 25 (6) ◽  
pp. 677-680 ◽  
Author(s):  
Janice Ser Huay Lee ◽  
Ian Qian Wei Lee ◽  
Susan Lee-Hong Lim ◽  
Johannes Huijbregts ◽  
Navjot S. Sodhi
Keyword(s):  
Species Richness ◽  
Ecosystem Services ◽  
Tropical Forest ◽  
Forest Disturbance ◽  
Dung Beetle ◽  
Peninsular Malaysia ◽  
Dung Beetles ◽  
Ecosystem Functions ◽  
Natural Habitats ◽  
Biodiversity Changes

With increasing conversion of South-East Asian forests to human-dominated landscapes, dramatic changes in biodiversity are likely to have ramifications on ecosystem processes (Sodhi & Brook 2006). Dung beetles (Coleoptera: Scarabaeidae) have been used to investigate how biodiversity changes affect ecosystem functions (Larsen et al. 2005, Slade et al. 2007). Dung beetles provide important ecosystem services such as dung removal and secondary seed dispersal (Nichols et al. 2008) and have been shown to be reliable indicators of tropical forest disturbance (Gardner et al. 2008, Klein 1989). Here, we determine the effects of forest disturbance on the species richness of dung beetles and ecosystem functions they perform in Peninsular Malaysia and Singapore. As far as we know, there has been no known study published on dung beetle ecology on the Malay Peninsula. In this study, we test the hypothesis that old-growth forests contain dung beetle communities of higher species richness, abundance, biomass and larger body size. Previous studies have shown that changes in dung beetle communities have the potential to disrupt ecosystem services in natural habitats (Larsen et al. 2005, Mittal 1993). We also investigate whether dung removal is affected by forest disturbance and test the hypothesis that dung removal is reduced in more disturbed forests compared with less-disturbed forests.

Using biomass distributions to determine probability and intensity of tropical forest disturbance

2012 ◽  
Vol 6 (1) ◽  
pp. 87-99 ◽  
Author(s):  
Mathew Williams ◽  
Timothy C. Hill ◽  
Casey M. Ryan
Keyword(s):  
Tropical Forest ◽  
Forest Disturbance

Corrigendum to “Quantifying the factors affecting wood decomposition across a tropical forest disturbance gradient” [For. Ecol. Manage. 468 (2020) 118166]

2021 ◽  
Vol 491 ◽  
pp. 119170
Author(s):  
Gbadamassi G.O. Dossa ◽  
Ekananda Paudel ◽  
Douglas Schaefer ◽  
Jiao-Lin Zhang ◽  
Kun-Fang Cao ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Disturbance ◽  
Wood Decomposition ◽  
Factors Affecting ◽  
Disturbance Gradient

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

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