Forestation does not necessarily reduce soil erosion in a karst watershed in southwestern China

Forestation as part of the Returning Farmland to Forest Project was implemented to mitigate soil erosion in southwestern China. However, whether forestation has effectively reduced soil erosion in southwestern China remains unclear, mostly because of the lack of monitoring forest cover change and soil erosion at watershed scales. We interpreted forest cover change from satellite images and simulated soil erosion changes for the period of 1986–2018 in the Chong’an River Basin with the Water and Tillage Erosion Model and Sediment Delivery Model. Our results show that the change in forest cover has the highest correlation coefficient with the sediment yield in the watershed, with an obvious inverse phase relationship between them for all the simulated years. From 2002 to 2014, large-scale forestation and frequent droughts caused the forest cover to vary, resulting in significant changes in the annual soil erosion amount. Because crevices favoring tree growth are more developed in limestone than in dolomite, the forest cover reduction on dolomite is significantly higher than that on limestone under severe droughts in karst areas. Our study implied that the function of forestation in preventing soil erosion depends on lithology in karst areas.

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Synthesizing published knowledge of boreal forest cover change for large-scale landscape dynamics modelling

The Forestry Chronicle ◽

10.5558/tfc79132-1 ◽

2003 ◽

Vol 79 (1) ◽

pp. 132-146 ◽

Cited By ~ 3

Author(s):

Dennis Yemshanov ◽

Ajith H Perera

Keyword(s):

Boreal Forest ◽

Large Scale ◽

Forest Canopy ◽

Forest Cover ◽

Forest Succession ◽

Simulation Models ◽

Limiting Factors ◽

Sources Of Information ◽

Forest Cover Change ◽

Discrete State

We reviewed the published knowledge on forest succession in the North American boreal biome for its applicability in modelling forest cover change over large extents. At broader scales, forest succession can be viewed as forest cover change over time. Quantitative case studies of forest succession in peer-reviewed literature are reliable sources of information about changes in forest canopy composition. We reviewed the following aspects of forest succession in literature: disturbances; pathways of post-disturbance forest cover change; timing of successional steps; probabilities of post-disturbance forest cover change, and effects of geographic location and ecological site conditions on forest cover change. The results from studies in the literature, which were mostly based on sample plot observations, appeared to be sufficient to describe boreal forest cover change as a generalized discrete-state transition process, with the discrete states denoted by tree species dominance. In this paper, we outline an approach for incorporating published knowledge on forest succession into stochastic simulation models of boreal forest cover change in a standardized manner. We found that the lack of details in the literature on long-term forest succession, particularly on the influence of pre-disturbance forest cover composition, may be limiting factors in parameterizing simulation models. We suggest that the simulation models based on published information can provide a good foundation as null models, which can be further calibrated as detailed quantitative information on forest cover change becomes available. Key words: probabilistic model, transition matrix, boreal biome, landscape ecology

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A method for detecting large-scale forest cover change using coarse spatial resolution imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2004.12.014 ◽

2005 ◽

Vol 95 (4) ◽

pp. 414-427 ◽

Cited By ~ 69

Author(s):

R.H. Fraser ◽

A. Abuelgasim ◽

R. Latifovic

Keyword(s):

Spatial Resolution ◽

Large Scale ◽

Forest Cover ◽

Forest Cover Change ◽

Coarse Spatial Resolution

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Economic crisis, small-scale agriculture, and forest cover change in southern Cameroon

Environmental Conservation ◽

10.1017/s0376892900000321 ◽

2000 ◽

Vol 27 (3) ◽

pp. 284-290 ◽

Cited By ~ 41

Author(s):

W.D. SUNDERLIN ◽

O. NDOYE ◽

H. BIKIÉ ◽

N. LAPORTE ◽

B. MERTENS ◽

...

Keyword(s):

Population Growth ◽

Economic Crisis ◽

Rural Population ◽

Structural Adjustment ◽

Large Scale ◽

Forest Cover ◽

Small Scale ◽

Forest Cover Change ◽

Macroeconomic Shocks ◽

Adjustment Policies

The rate of forest cover loss in the humid tropics of Cameroon is one of the highest in Central Africa. The aim of the large-scale, two-year research project described here was to understand the effect of the country's economic crisis and policy change on small-scale agricultural systems and land-clearing practices. Hypotheses were tested through surveys of more than 5000 households in 125 villages, and through time-series remote sensing analysis at two sites. The principal findings are that: (1) the rate of deforestation increased significantly in the decade after the 1986 onset of the crisis, as compared to the decade prior to the crisis; (2) the main proximate causes of this change were sudden rural population growth and a shift from production of cocoa and coffee to plantain and other food crops; and (3) the main underlying causes were macroeconomic shocks and structural adjustment policies that led to rural population growth and farming system changes. The implication of this study is that it is necessary to understand and anticipate the undesirable consequences of macroeconomic shocks and adjustment policies for forest cover. Such policies, even though they are often not formulated with natural resource consequences in mind, are often of greater relevance to the fate of forests than forest policy.

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Assessing the tropical forest cover change in northern parts of Sonitpur and Udalguri District of Assam, India

Scientific Reports ◽

10.1038/s41598-021-90595-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ranjit Mahato ◽

Gibji Nimasow ◽

Oyi Dai Nimasow ◽

Dhoni Bushi

Keyword(s):

Protected Areas ◽

National Park ◽

Large Scale ◽

Forest Cover ◽

Ground Truth ◽

Forest Cover Change ◽

Wildlife Sanctuary ◽

Remotely Sensed Data ◽

Ground Truth Data ◽

Increasing Trends

AbstractSonitpur and Udalguri district of Assam possess rich tropical forests with equally important faunal species. The Nameri National Park, Sonai-Rupai Wildlife Sanctuary, and other Reserved Forests are areas of attraction for tourists and wildlife lovers. However, these protected areas are reportedly facing the problem of encroachment and large-scale deforestation. Therefore, this study attempts to estimate the forest cover change in the area through integrating the remotely sensed data of 1990, 2000, 2010, and 2020 with the Geographic Information System. The Maximum Likelihood algorithm-based supervised classification shows acceptable agreement between the classified image and the ground truth data with an overall accuracy of about 96% and a Kappa coefficient of 0.95. The results reveal a forest cover loss of 7.47% from 1990 to 2000 and 7.11% from 2000 to 2010. However, there was a slight gain of 2.34% in forest cover from 2010 to 2020. The net change of forest to non-forest was 195.17 km2 in the last forty years. The forest transition map shows a declining trend of forest remained forest till 2010 and a slight increase after that. There was a considerable decline in the forest to non-forest (11.94% to 3.50%) from 2000–2010 to 2010–2020. Further, a perceptible gain was also observed in the non-forest to the forest during the last four decades. The overlay analysis of forest cover maps show an area of 460.76 km2 (28.89%) as forest (unchanged), 764.21 km2 (47.91%) as non-forest (unchanged), 282.67 km2 (17.72%) as deforestation and 87.50 km2 (5.48%) as afforestation. The study found hotspots of deforestation in the closest areas of National Park, Wildlife Sanctuary, and Reserved Forests due to encroachments for human habitation, agriculture, and timber/fuelwood extractions. Therefore, the study suggests an early declaration of these protected areas as Eco-Sensitive Zone to control the increasing trends of deforestation.

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Assessment of forest cover change and its environmental impacts using multi-temporal and multi-spectral satellite images

Mitigating Land Degradation and Improving Livelihoods ◽

10.4324/9781315754444-13 ◽

2015 ◽

pp. 103-116

Keyword(s):

Environmental Impacts ◽

Satellite Images ◽

Forest Cover ◽

Forest Cover Change ◽

Multi Temporal

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Forest Cover Change Detection Using Satellite Images

10.1007/978-981-16-3961-6_46 ◽

2021 ◽

pp. 565-573

Author(s):

Achal Kalwar ◽

Rohan Mathur ◽

Shubham Chavan ◽

Chhaya Narvekar

Keyword(s):

Change Detection ◽

Satellite Images ◽

Forest Cover ◽

Forest Cover Change

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Investigating the interactions between Forest Cover Change and Hydroclimatic patterns in the Bolivian Amazon basin over the last 30 years

10.5194/egusphere-egu2020-17876 ◽

2020 ◽

Author(s):

Sly Wongchuig Correa ◽

Jhan Carlo Espinoza ◽

Hans Segura ◽

Thomas Condom ◽

Clémentine Junquas

Keyword(s):

Land Cover ◽

Large Scale ◽

Amazon Basin ◽

Forest Cover ◽

Hydrological Modeling ◽

European Space Agency ◽

Forest Cover Change ◽

Wet Season ◽

Climatic Index ◽

Bolivian Amazon

Large evidences support the strong impacts on rainfall amount and the increasing of dry-season length on the Amazonian forest. All of these effects are usually attributed to large scale atmospheric circulation and to land cover changes as part of anthropogenic effects. In this research we assess statistical and modeling approaches to investigate the interaction between changes in forest cover and hydroclimate processes on a regional and local scale.Henceforth, the deforestation areas and climatic indexes for the southern Amazon basin (south of 14&#176;S) were evaluated. The deforestation map was estimated for the 1992-2018 period, based on global land cover maps at 300 m of spatial resolution produced by the European Space Agency (ESA) Climate Change Initiative (CCI) by using several remote sensing datasets. The CHIRPS rainfall dataset (P) for the 1981-2018 period was used to estimate the dry day frequency (DDF, P<1mm) and the wet day frequency (WDF, P>10mm). In addition, the mean actual seasonal evapotranspiration (AET) was GLEAM and ET-Amazon evapotranspiration datasets for the 1980-2018 and 2003-2013 periods respectively. In order to determine the local and the regional climatic effect for each pixel of the climatic index (DDF, WDF and AET), the deforestation fraction was estimated considering different spatial radii of influence (20 to 50 km).The first results indicate a particular pattern in the southern Bolivian Amazon where two groups of areas were identified, considering the common period of analysis (1992-2018). One of them shows a significant relationship between increasing trend of DDF and decreasing trend of WDF while deforestation fraction is high, what mainly occurs during the wet season. In addition, this region is clearly placed in areas with values of deforestation fraction above ~30%, a closest value to the usually estimated Amazon Tipping Point (~40%). Below this value, the second group is also located in regions with positive trends of DDF and negative trends of WDF. This region has probably a strongest link with the large-scale climate.Considering these preliminary results, the statistical approaches developed in this research could give some insights about the interactions between forest change and the regional hydro climatology, which might improve the understanding of this interaction based on large-scale hydrological modeling.

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