scholarly journals Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

2014 ◽  
Vol 6 (9) ◽  
pp. 8878-8903 ◽  
Author(s):  
Xiao-Peng Song ◽  
Chengquan Huang ◽  
Joseph Sexton ◽  
Saurabh Channan ◽  
John Townshend
Keyword(s):  
Time Series ◽  
Forest Cover ◽  
Tree Cover ◽  
Satellite Measurements ◽  
Forest Cover Loss

scholarly journals Causes for Deforestation in Indonesia: Corruption and Palm Tree Plantation

2015 ◽  
Vol 11 (27) ◽  
pp. 120
Author(s):  
Osama Eldeeb ◽  
Petr Prochazka ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Forest Cover ◽  
Palm Tree ◽  
Test Statistics ◽  
Crude Palm Oil ◽  
Tree Plantation ◽  
Long Run ◽  
Corruption Perception ◽  
Forest Cover Loss

<p>Indonesian biodiversity is threatened by massive deforestation. In this research paper, claims that deforestation in Indonesia is caused by corruption and supported by crude palm oil production is verified using time series analysis. Using Engel Granger cointegration test, three time series of data, specifically corruption perception index, rate of deforestation and price of crude palm oil are inspected for a long-run relationship. Test statistics suggests that there is no long-run relationship among these variables. Authors provide several explanations for this result. For example, corruption in Indonesia, as measured by CPI is still very high. This may mean that forest cover loss is possible even though there is a positive change in corruption level. According to the results, crude palm oil price has also no effect upon forest cover loss. This is likely due to very low shut-down price of crude palm oil for which production is still economical.</p>

scholarly journals Mapping the spatial distribution of stand age and aboveground biomass from Landsat time series analyses of forest cover loss in tropical dry forests

2021 ◽  
Author(s):  
Stephanie P. George‐Chacón ◽  
Jean François Mas ◽  
Juan Manuel Dupuy ◽  
Miguel Angel Castillo‐Santiago ◽  
José Luis Hernández‐Stefanoni
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Aboveground Biomass ◽  
Forest Cover ◽  
Tropical Dry Forests ◽  
Stand Age ◽  
Dry Forests ◽  
Time Series Analyses ◽  
Forest Cover Loss

scholarly journals Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 187 ◽  
Author(s):  
Viktor Myroniuk ◽  
Mykola Kutia ◽  
Arbi J. Sarkissian ◽  
Andrii Bilous ◽  
Shuguang Liu
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Forest Cover ◽  
Regional Scale ◽  
Forest Products ◽  
Google Earth ◽  
Tree Cover ◽  
Landsat 8 ◽  
Fragmented Landscapes ◽  
Forest Mapping

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.

scholarly journals Satellite-based time series land cover and change information to map forest area consistent with national and international reporting requirements

2020 ◽  
Vol 93 (3) ◽  
pp. 331-343 ◽  
Author(s):  
Michael A Wulder ◽  
Txomin Hermosilla ◽  
Graham Stinson ◽  
François A Gougeon ◽  
Joanne C White ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Forest Area ◽  
Tree Cover ◽  
Minimum Size ◽  
Spatially Explicit ◽  
Reference Source ◽  
International Reporting

Abstract Forests are dynamic ecosystems, subject to both natural and anthropogenic agents of change. Wildfire, harvesting and other human activities alter the tree-covered area present in forests. From national and international reporting perspectives, forests include areas currently treed, as well as those disturbed forest areas that are not currently treed but will be, given time for regeneration and the advancement of natural successional processes. As a consequence, forest area can be depicted at a particular point in time, informed by a retrospective temporal context. Using time series of Landsat imagery, annual land cover maps can be generated that are informed by knowledge of past disturbance history (such as wildfire and harvesting). In this research, we use over three decades of annual land cover data generated from Landsat time series to generate a spatially explicit estimate of the forest area of Canada in 2010. We demonstrate how land cover and disturbance information can be combined to map the area of ‘forest’, as defined by the Food and Agricultural Organization of the United Nations (FAO), within Canada’s 650 Mha of forested ecozones. Following this approach, we estimated Canada’s total forest area in 2010 to be 354.5 Mha. This estimate includes 324.5 Mha of current forest cover in 2010, plus an additional 33.2 Mha (or 9.4 per cent) of temporally informed forest area where tree cover had been temporarily lost due to fire or harvest, less 3.3 Mha that were removed to meet a definitional minimum size (0.5 ha) for contiguous forest area. Using Canada’s National Forest Inventory (NFI) as an independent reference source, the spatial agreement between the two estimates of forest area was ~84 per cent overall. Aspatially, the total area of the Landsat-derived estimate of 2010 forest area and the NFI baseline estimates differed by only 3 per cent, with notable regional differences in the wetland-dominated Hudson Plains Ecozone. Satellite-derived time series land cover and change information enable spatially explicit depictions of forest area (distinct from representations of forest cover) in a robust and transparent fashion, producing information of value to science, management and reporting information needs.

scholarly journals Assessing Drivers of Forest Conservation in Simancuang Village Forest, West Sumatra

2021 ◽  
Author(s):  
Adelina Chandra ◽  
Dimas Fauzi ◽  
Fadhilla Husnul Khatimah ◽  
Satrio Adi Wicaksono
Keyword(s):  
Program Implementation ◽  
Forest Cover ◽  
Secondary Data ◽  
Monitoring And Evaluation ◽  
Tree Cover ◽  
Social Forestry ◽  
Protection Forest ◽  
West Sumatra ◽  
Conservation Awareness ◽  
Forest Cover Loss

AbstractThis study empirically assessed Social Forestry program implementation in Simancuang Village Forest or locally known as Hutan Nagari (HN) Simancuang in West Sumatra, Indonesia. We performed two analyses using primary and secondary data, namely propensity score matching to estimate the effects of the enactment of HN Simancuang in 2012 on forest cover loss and ordinal logistic regression (OLR) to predict the determinants of conservation awareness. The results of the forest cover analysis showed that forest cover loss in HN Simancuang between 2012 and 2019 was 0.038 percentage point lower than the adjacent protection forest. The relatively small impact was meaningful because although HN Simancuang is located much closer to settlements which increases the pressure on the forest, it could still maintain lower tree cover losses than the adjacent protection forest. This result indicated a certain degree of conservation awareness among HN Simancuang members, which prompted us to conduct a survey to 111 individuals from different households. To do this, we used the Ecosystem Services framework to conceptualise conservation awareness in HN Simancuang. Our OLR results showed that regulating and provisioning services of forests are the strong determinants of conservation awareness among the individuals in our sample. Our study indicates the need to implement social forestry program monitoring and evaluation, improve access to facilitation, and enhance agroforestry practice as the means to increase conservation awareness among forest-dwelling communities.

scholarly journals Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor

2018 ◽  
Vol 10 (11) ◽  
pp. 1850 ◽  
Author(s):  
Michael Schultz ◽  
Aurélie Shapiro ◽  
Jan Clevers ◽  
Craig Beech ◽  
Martin Herold
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Land Cover ◽  
Forest Cover ◽  
Tree Cover ◽  
Series Data ◽  
Conservation Area ◽  
Vegetation Degradation ◽  
Transfrontier Conservation ◽  
Transfrontier Conservation Area

Forest cover and vegetation degradation was monitored across the Kavango-Zambezi Transfrontier Conservation Area (KAZA) in southern Africa and the performance of three different methods in detecting degradation was assessed using reference data. Breaks for Additive Season and Trend (BFAST) Monitor was used to identify potential forest cover and vegetation degradation using Landsat Normalized Difference Moisture Index (NDMI) time series data. Parametric probability-based magnitude thresholds, non-parametric random forest in conjunction with Soil-Adjusted Vegetation Index (SAVI) time series, and the combination of both methods were evaluated for their suitability to detect degradation for six land cover classes ranging from closed canopy forest to open grassland. The performance of degradation detection was largely dependent on tree cover and vegetation density. Satisfactory accuracies were obtained for closed woodland (user’s accuracy 87%, producer’s accuracy 71%) and closed forest (user’s accuracy 92%, producer’s accuracy 90%), with lower accuracies for open canopies. The performance of the three methods was more similar for closed canopies and differed for land cover classes with open canopies. Highest user’s accuracy was achieved when methods were combined, and the best performance for producer’s accuracy was obtained when random forest was used.

scholarly journals Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

2020 ◽  
Vol 12 (19) ◽  
pp. 3226
Author(s):  
Daniel Cunningham ◽  
Paul Cunningham ◽  
Matthew E. Fagan
Keyword(s):  
Costa Rica ◽  
Forest Cover ◽  
Agricultural Land ◽  
Forest Area ◽  
Tree Cover ◽  
Agricultural Crop ◽  
Forest Change ◽  
Alos Palsar ◽  
Tropical Regions ◽  
The Impact

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.

scholarly journals India’s Commitments to Increase Tree and Forest Cover: Consequences for Water Supply and Agriculture Production within the Central Indian Highlands

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 959
Author(s):  
Benjamin Clark ◽  
Ruth DeFries ◽  
Jagdish Krishnaswamy
Keyword(s):  
Groundwater Recharge ◽  
Forest Cover ◽  
Hydrological Modeling ◽  
Monsoon Season ◽  
Forest Policy ◽  
Central India ◽  
Field Measurements ◽  
Tree Cover ◽  
Policy Goals ◽  
The Impact

As part of its nationally determined contributions as well as national forest policy goals, India plans to boost tree cover to 33% of its land area. Land currently under other uses will require tree-plantations or reforestation to achieve this goal. This paper examines the effects of converting cropland to tree or forest cover in the Central India Highlands (CIH). The paper examines the impact of increased forest cover on groundwater infiltration and recharge, which are essential for sustainable Rabi (winter, non-monsoon) season irrigation and agricultural production. Field measurements of saturated hydraulic conductivity (Kfs) linked to hydrological modeling estimate increased forest cover impact on the CIH hydrology. Kfs tests in 118 sites demonstrate a significant land cover effect, with forest cover having a higher Kfs of 20.2 mm hr−1 than croplands (6.7mm hr−1). The spatial processes in hydrology (SPHY) model simulated forest cover from 2% to 75% and showed that each basin reacts differently, depending on the amount of agriculture under paddy. Paddy agriculture can compensate for low infiltration through increased depression storage, allowing for continuous infiltration and groundwater recharge. Expanding forest cover to 33% in the CIH would reduce groundwater recharge by 7.94 mm (−1%) when converting the average cropland and increase it by 15.38 mm (3%) if reforestation is conducted on non-paddy agriculture. Intermediate forest cover shows however shows potential for increase in net benefits.

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