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 The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments

2013 ◽  
Vol 17 (7) ◽  
pp. 2613-2635 ◽  
Author(s):  
H. E. Beck ◽  
L. A. Bruijnzeel ◽  
A. I. J. M. van Dijk ◽  
T. R. McVicar ◽  
F. N. Scatena ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Forest Regeneration ◽  
Forest Cover ◽  
Impact Analysis ◽  
Forest Area ◽  
Macro Scale ◽  
Annual Time Series ◽  
The Impact ◽  
Annual Time

Abstract. Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastures and agricultural lands, followed by forest regeneration. This paper examines the possible impacts of these secondary forests on several Q characteristics for 12 mesoscale catchments (23–346 km2; mean precipitation 1720–3422 mm yr−1) with long (33–51 yr) and simultaneous records for Q, precipitation (P), potential evaporation (PET), and land cover. A simple spatially-lumped, conceptual rainfall–runoff model that uses daily P and PET time series as inputs (HBV-light) was used to simulate Q for each catchment. Annual time series of observed and simulated values of four Q characteristics were calculated. A least-squares trend was fitted through annual time series of the residual difference between observed and simulated time series of each Q characteristic. From this the total cumulative change (Â) was calculated, representing the change in each Q characteristic after controlling for climate variability and water storage carry-over effects between years. Negative values of  were found for most catchments and Q characteristics, suggesting enhanced actual evaporation overall following forest regeneration. However, correlations between changes in urban or forest area and values of  were insignificant (p ≥ 0.389) for all Q characteristics. This suggests there is no convincing evidence that changes in the chosen Q characteristics in these Puerto Rican catchments can be ascribed to changes in urban or forest area. The present results are in line with previous studies of meso- and macro-scale (sub-)tropical catchments, which generally found no significant change in Q that can be attributed to changes in forest cover. Possible explanations for the lack of a clear signal may include errors in the land cover, climate, Q, and/or catchment boundary data; changes in forest area occurring mainly in the less rainy lowlands; and heterogeneity in catchment response. Different results were obtained for different catchments, and using a smaller subset of catchments could have led to very different conclusions. This highlights the importance of including multiple catchments in land-cover impact analysis at the mesoscale.

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 A Land Cover Changes Model Based on Carbon Stock in the Mount Patah Region of, Bengkulu-Indonesia

2020 ◽  
Vol 4 (9) ◽  
pp. 1-5
Keyword(s):  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Forest Inventory ◽  
Carbon Stock ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Land Cover Changes ◽  
Land Area ◽  
Reduced Data

Forest cover in Bengkulu is reduced. Data from WARSI shows, 1990 forest cover areas in the province are approximately 1,009,209 hectares or 50.4 % of the land area reaching 1,979,515 hectares. But now, it is only 685,762 hectares of the area of his blood. That is, the period of 25 years, there is a forest cover decline of 323,447 hectares. Forest and land cover changes are the largest contributor to greenhouse gas emissions. The purpose of this article is to see land cover changes based on carbon stock in the years 2009 and 2018. Model of land cover change based on carbon stock year 2028 and 2038. The method of this research uses the calculation of the Stock Difference Approach with spatial analysis of national land closure of Landsat imagery 2009-2018 and biomass data for forest inventory results Geographic Information System (GIS). The results of this research were the reduced forest area and the change in land use changed from 2009 and 2018. So carbon stock is also increasingly reduced.

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 GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

2021 ◽  
Vol 13 (6) ◽  
pp. 2753-2776
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
Shuai Xie ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Classification System ◽  
Landsat Imagery ◽  
Kappa Coefficient ◽  
Training Data ◽  
Land Cover Types ◽  
Global Land Cover ◽  
Global Land ◽  
Fine Classification

Abstract. Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC, and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010. They also showed that GLC_FCS30-2015 achieved the best overall accuracy of 82.5 % against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).

scholarly journals Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada

2019 ◽  
Vol 11 (19) ◽  
pp. 2286
Author(s):  
Libo Wang ◽  
Paul Bartlett ◽  
Darren Pouliot ◽  
Ed Chan ◽  
Céline Lamarche ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Land Surface ◽  
Forest Cover ◽  
Reference Sample ◽  
Tree Cover ◽  
Initial Surface ◽  
Forest Cover Change ◽  
Global Land Cover ◽  
Global Land

Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.

scholarly journals Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China

2020 ◽  
Vol 12 (1) ◽  
pp. 155 ◽  
Author(s):  
Wenjuan Shen ◽  
Xupeng Mao ◽  
Jiaying He ◽  
Jinwei Dong ◽  
Chengquan Huang ◽  
...  
Keyword(s):  
Land Cover ◽  
Markov Model ◽  
Forest Cover ◽  
Urban Forest ◽  
Detection Algorithm ◽  
Forest Area ◽  
Spatiotemporal Pattern ◽  
Forest Cover Change ◽  
Forest Interior ◽  
Land Cover Maps

Accurate acquisition of the spatiotemporal distribution of urban forests and fragmentation (e.g., interior and intact regions) is of great significance to contributing to the mitigation of climate change and the conservation of habitat biodiversity. However, the spatiotemporal pattern of urban forest cover changes related with the dynamics of interior and intact forests from the present to the future have rarely been characterized. We investigated fragmentation of urban forest cover using satellite observations and simulation models in the Nanjing Laoshan Region of Jiangbei New Area, Jiangsu, China, during 2002–2023. Object-oriented classification-based land cover maps were created to simulate land cover changes using the cellular automation-Markov chain (CA-Markov) model and the state transition simulation modeling. We then quantified the forest cover change by the morphological change detection algorithm and estimated the forest area density-based fragmentation patterns. Their relationships were built through the spatial analysis and statistical methods. Results showed that the overall accuracies of actual land cover maps were approximately 83.75–92.25% (2012–2017). The usefulness of a CA-Markov model for simulating land cover maps was demonstrated. The greatest proportion of forest with a low level of fragmentation was captured along with the decreasing percentage of fragmented area from 81.1% to 64.1% based on high spatial resolution data with the window size of 27 pixels × 27 pixels. The greatest increase in fragmentation (3% from 2016 to 2023) among the changes between intact and fragmented forest was reported. However, intact forest was modeled to have recovered in 2023 and restored to 2002 fragmentation levels. Moreover, we found 58.07 km2 and 0.35 km2 of interior and intact forests have been removed from forest area losses and added from forest area gains. The loss rate of forest interior and intact area exceeded the rate of total forest area loss. However, their approximate ratio (1) implying the loss of forest interior and intact area would have slight fragmentation effects on the remaining forests. This analysis illustrates the achievement of protecting and restoring forest interior; more importantly, excessive human activities in the surrounding area had been avoided. This study provides strategies for future forest conservation and management in large urban regions.

scholarly journals GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery

2020 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
Shuai Xie ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Classification System ◽  
Landsat Imagery ◽  
Kappa Coefficient ◽  
Training Data ◽  
Land Cover Types ◽  
Global Land Cover ◽  
Global Land ◽  
Fine Classification

Abstract. Over past decades, a lot of global land-cover products have been released, however, these is still lack of a global land-cover map with fine classification system and spatial resolution simultaneously. In this study, a novel global 30-m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5° × 5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010, and that GLC_FCS30-2015 achieved the best overall accuracy of 82.5% against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products generated in this paper is available at https://doi.org/10.5281/zenodo.3986871 (Liu et al., 2020).

scholarly journals Dinamika Cadangan Karbon akibat Perubahan Tutupan Lahan menjadi Lahan Permukiman di Kota Padang Sumatera Barat

2012 ◽  
Vol 26 (1) ◽  
pp. 45
Author(s):  
Dedi Hermon
Keyword(s):  
Land Cover ◽  
Landsat Imagery ◽  
Sampling Technique ◽  
Carbon Stocks ◽  
Stratified Sampling ◽  
Forest Area ◽  
Tree Biomass ◽  
Land Settlement ◽  
West Sumatra ◽  
Landsat 7

The purpose of this study was to analyse the dynamics of carbon stocks changes from land cover into land settlement in the Padang City, West Sumatra. Method to formulate the change of land cover into land settlement in the Padang City is the analysis of Landsat Imagery 5+TM 1988, Landsat 7+ETM Image of 1998 and Landsat 7+ETM Image of 2008. Stratified Sampling Technique was Purpose Composite plot refers to the technique, but in this study carried out modification to the size of the plot which is then converted to the extend of each hectare of land cover. Estimating tree biomass using the equation according Kattering allometric, (2001). The result of the research conducted found that the dynamics of carbon stocks always decline from 1988, 1998 and 2008. This is caused by a reduction in forest area, shrubs, gardens, and fields are consistently due to the increased amount of land used for settlement.

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