scholarly journals Mapping Mangrove Forest Change in Nijhum Dwip Island

2019 ◽  
Vol 11 (1-2) ◽  
pp. 217-225
Author(s):  
MM Rahman ◽  
MAT Pramanik ◽  
MI Islam ◽  
S Razia
Keyword(s):  
Mangrove Forest ◽  
Forest Cover ◽  
Storm Surges ◽  
Forest Loss ◽  
Forest Conversion ◽  
Forest Cover Change ◽  
Forest Change ◽  
Change Analysis ◽  
Coastal Belt ◽  
Land Covers

Mangroves have been planting in the coastal belt of Bangladesh to protect the inhabitants of the coastal areas from cyclones and storm surges. Nijhum Dwip is located at the southern part of Hatiya Island. Most part of the island has been planted with the mangroves in the 1970s and 1980s; while parts of the mangroves have been deforested during the past few decades. The objectives of this research were to delineate and quantify the changes in the extent of mangroves in the island. The Landsat data of 1989, 2001, 2010 and 2018 have been utilized in the study. Three major land covers, namely forest, water and other land have been interpreted and delineated by using on-screen digitizing. The quantity of mangrove forest loss in the island is estimated as 1,024 ha, while 395 ha were afforested during 1989-2018. In the decadal change analysis, it was revealed that net forest cover change was higher in 2000s compared to other two decades and it was -425 ha. The result of the study is helpful to understand the extent and pattern of forest conversion in the island and to halt further forest loss and conserve the remaining forest. J. Environ. Sci. & Natural Resources, 11(1-2): 217-225 2018

scholarly journals Object-Based Classification of Forest Disturbance Types in the Conterminous United States

2019 ◽  
Vol 11 (5) ◽  
pp. 477 ◽  
Author(s):  
Lian-Zhi Huo ◽  
Luigi Boschetti ◽  
Aaron Sparks
Keyword(s):  
United States ◽  
Forest Cover ◽  
Forest Disturbance ◽  
Global Scale ◽  
Management Policy ◽  
Forest Loss ◽  
Forest Cover Change ◽  
Forest Change ◽  
Goods And Services ◽  
Moderate Resolution Imaging Spectroradiometer

Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes.

scholarly journals Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia

2015 ◽  
Vol XL-7/W3 ◽  
pp. 557-562 ◽  
Author(s):  
A. Wijaya ◽  
R. A. Sugardiman Budiharto ◽  
A. Tosiani ◽  
D. Murdiyarso ◽  
L.V. Verchot
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Spatial Information ◽  
Brazilian Amazon ◽  
Congo Basin ◽  
Reliable Estimate ◽  
Forest Conversion ◽  
Forest Cover Change ◽  
Forest Change

Indonesia possesses the third largest tropical forests coverage following Brazilian Amazon and Congo Basin regions. This country, however, suffered from the highest deforestation rate surpassing deforestation in the Brazilian Amazon in 2012. National capacity for forest change assessment and monitoring has been well-established in Indonesia and the availability of national forest inventory data could largely assist the country to report their forest carbon stocks and change over more than two decades. This work focuses for refining forest cover change mapping and deforestation estimate at national scale applying over 10,000 scenes of Landsat scenes, acquired in 1990, 1996, 2000, 2003, 2006, 2009, 2011 and 2012. Pre-processing of the data includes, geometric corrections and image mosaicking. The classification of mosaic Landsat data used multi-stage visual observation approaches, verified using ground observations and comparison with other published materials. There are 23 land cover classes identified from land cover data, presenting spatial information of forests, agriculture, plantations, non-vegetated lands and other land use categories. We estimated the magnitude of forest cover change and assessed drivers of forest cover change over time. Forest change trajectories analysis was also conducted to observe dynamics of forest cover across time. This study found that careful interpretations of satellite data can provide reliable information on forest cover and change. Deforestation trend in Indonesia was lower in 2000-2012 compared to 1990-2000 periods. We also found that over 50% of forests loss in 1990 remains unproductive in 2012. Major drivers of forest conversion in Indonesia range from shrubs/open land, subsistence agriculture, oil palm expansion, plantation forest and mining. The results were compared with other available datasets and we obtained that the MOF data yields reliable estimate of deforestation.

scholarly journals FOREST COVER CHANGE ANALYSIS IN INNER MONGOLIA USING REMOTE SENSING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1951-1954
Author(s):  
S. Xie ◽  
J. Gong ◽  
X. Huang
Keyword(s):  
Protected Area ◽  
Inner Mongolia ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Shape Index ◽  
Forest Area ◽  
Forest Cover Change ◽  
Forest Change ◽  
Change Analysis ◽  
Forest Patches

Forest is the lung of the earth, and it has important effect on maintaining the ecological balance of the whole earth. This study was conducted in Inner Mongolia during the year 1990–2015. Land use and land cover data were used to obtain forest cover change of Inner Mongolia. In addition, protected area data, road data, ASTER GDEM data were combined with forest cover change data to analyze the relationship between them. Moreover, patch density and landscape shape index were calculated to analyze forest change in perspective of landscape aspect. The results indicated that forest area increased overall during the study period. However, a few cities still had a phenomenon of reduced forest area. Results also demonstrated that the construction of protected area had positive effect on protecting forest while roads may disturbed forest due to human activities. In addition, forest patches in most of cities of Inner Mongolia tended to be larger and less fragmented. This paper reflected forest change in Inner Mongolia objectively, which is helpful for policy making by government.

scholarly journals Using Geospatial Analysis to Map Forest Change in New Hampshire: 1996–Present

2020 ◽  
Vol 118 (6) ◽  
pp. 598-612
Author(s):  
Heather Grybas ◽  
Russell G Congalton ◽  
Andrew F Howard
Keyword(s):  
Land Cover ◽  
New Hampshire ◽  
Forest Cover ◽  
Landsat Imagery ◽  
The State ◽  
Forest Cover Change ◽  
Forest Change ◽  
Change Analysis ◽  
Land Cover Map ◽  
Forest Use

Abstract New Hampshire’s forests are vitally important to the state’s economy; however, there are indications that the state is experiencing a continuous loss in forest cover. We sought to investigate forest cover trends in New Hampshire. A baseline trend in forest cover between 1996 and 2010 was established using National Oceanic and Atmospheric Administration Coastal Change Analysis Program land cover data. A land cover map was then generated from Landsat imagery to extend the baseline trend to 2018. Results show that the state has experienced a continual decline in forest cover with the annual net loss steadily increasing from 0.14% between 1996 and 2001 to 0.27% between 2010 and 2018. Additionally, the more urbanized counties in southern New Hampshire are experiencing some of the greatest rates of net forest loss, most likely because of urbanization and agricultural expansion. This study demonstrated an effective methodology for tracking forest cover change and will hopefully inform future forest use policies.

scholarly journals Quantifying the Actual Impacts of Forest Cover Change on Surface Temperature in Guangdong, China

2020 ◽  
Vol 12 (15) ◽  
pp. 2354
Author(s):  
Wenjuan Shen ◽  
Jiaying He ◽  
Chengquan Huang ◽  
Mingshi Li
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Forest Cover ◽  
Temperature Response ◽  
Forest Loss ◽  
Forest Cover Change ◽  
Local Surface ◽  
Forest Change ◽  
Gain And Loss

Forest cover change is critical in the regulation of global and regional climate change through the alteration of biophysical features across the Earth’s surface. The accurate assessment of forest cover change can improve our understanding of its roles in the regulation processes of surface temperature. In spite of this, few researchers have attempted to discern the varying effects of multiple satellite-derived forest changes on local surface temperatures. In this study, we quantified the actual contributions of forest loss and gain associated with evapotranspiration (ET) and albedo to local surface temperature in Guangdong Province, China using an improved spatiotemporal change pattern analysis method, and explored the interrelationships between surface temperature and air temperature change. We specifically developed three forest change products for Guangdong, combining satellite observations from Landsat, PALSAR, and MODIS for comparison. Our results revealed that the adjusted simple change detection (SCD)-based Landsat/PALSAR forest cover data performed relatively well. We found that forest loss and gain between 2000 and 2010 had opposite effects on land surface temperature (LST), ET, and albedo. Forest gain led to a cooling of −0.12 ± 0.01 °C, while forest loss led to a warming of 0.07 ± 0.01 °C, which were opposite to the anomalous change of air temperature. A reduced warming to a considerable cooling was estimated due to the forest gain and loss across latitudes. Specifically, mid-subtropical forest gains increased LST by 0.25 ± 0.01 °C, while tropical forest loss decreased LST by −0.16 ± 0.05 °C, which can demonstrate the local differences in an overall cooling. ET induced cooling and warming effects were appropriate for most forest gain and loss. Meanwhile, the nearby temperature changes caused by no-change land cover types more or less canceled out some of the warming and cooling. Albedo exhibited negligible and complex impacts. The other two products (i.e., the GlobeLand30 and MCD12Q1) affect the magnitude of temperature response due to the discrepancies in forest definition, methodology, and data resolution. This study highlights the non-negligible contributions of high-resolution maps and a robust temperature response model in the quantification of the extent to which forest gain reverses the climate effects of forest loss under global warming.

scholarly journals Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize

2019 ◽  
Vol 11 (7) ◽  
pp. 823 ◽  
Author(s):  
Carly Voight ◽  
Karla Hernandez-Aguilar ◽  
Christina Garcia ◽  
Said Gutierrez
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Agricultural Practices ◽  
Google Earth ◽  
Training Data ◽  
Forest Loss ◽  
Landsat 8 ◽  
Forest Cover Change ◽  
Change Analysis

Tropical forests and the biodiversity they contain are declining at an alarming rate throughout the world. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened by unsustainable agricultural practices. Deforestation data allow forest managers to efficiently allocate resources and inform decisions for proper conservation and management. This study utilized satellite imagery to analyze recent forest cover and deforestation in southern Belize to model vulnerability and identify the areas that are the most susceptible to future forest loss. A forest cover change analysis was conducted in Google Earth Engine using a supervised classification of Landsat 8 imagery with ground-truthed land cover points as training data. A multi-layer perceptron neural network model was performed to predict the potential spatial patterns and magnitude of forest loss based on the regional drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests, predicting a decrease from 75.0% mature forest cover in 2016 to 71.9% in 2026. This study represents the most up-to-date assessment of forest cover and the first vulnerability and prediction assessment in southern Belize with immediate applications in conservation planning, monitoring, and management.

scholarly journals Thirty-Year Spatiotemporal Change Record of Sundarban Mangrove Forest in Bangladesh

2021 ◽  
Vol 24 (2) ◽  
pp. 15-32
Author(s):  
KMM Uzzaman ◽  
MG Miah ◽  
HM Abdullah ◽  
MR Islam ◽  
MSI Afrad ◽  
...  
Keyword(s):  
Mangrove Forest ◽  
Forest Cover ◽  
Water Bodies ◽  
Forest Monitoring ◽  
Continuous Change ◽  
Forest Cover Change ◽  
Subsequent Increase ◽  
Stable Pattern ◽  
Sundarban Mangrove ◽  
Conservation And Management

Accurate and realistic forest cover change assessment is essential for the conservation and management of the Sundarban mangrove forest of Bangladesh. With these views, an integrated way of the vegetation cover assessment was conducted using time-series Landsat satellite imageries of 1991, 2001, 2011, and 2021. During the last 30-year (1991-2021), variations in four land cover classes viz. healthy vegetation, unhealthy vegetation, water body, and sandbar were recorded. It showed a decreasing trend of forest vegetation and a subsequent increase of water bodies during the study period. The healthy vegetation and unhealthy vegetation decreased at 1.33 and 1.66%, respectively, whereas water bodies increased 2.55% at the same time. The healthy vegetation consistently decreased over the decades, but unhealthy vegetation decreased during the 2001-2011 period. Conversion from healthy vegetation to unhealthy vegetation and unhealthy vegetation to healthy vegetation during 1991-2001 was similar. Such transform was much higher from unhealthy to healthy vegetation during 2001-2011. Transformation of healthy vegetation to unhealthy vegetation was remarkably higher during the 2011-2021 period. Further continuous change detection and classification algorithm (CCDC) showed a stable pattern over the study period without significant breakpoints. This study reveals the need for regular mangrove forest monitoring. The findings of this study can be used as a reference in the formulation and implementation of sustainable mangrove forest conservation and management. Ann. Bangladesh Agric. (2020) 24(2): 15-32

Opportunities for Reaping the Benefits of REDD+ in Sikkim Himalaya for Conservation and Enhancement of Carbon Stock

2017 ◽  
Vol 40 (3) ◽  
pp. 209-215
Author(s):  
Mohommad Shahid ◽  
◽  
L.K. Rai ◽  
Keyword(s):  
Climate Change ◽  
Forest Cover ◽  
Carbon Sink ◽  
Forest Degradation ◽  
Forest Cover Change ◽  
Change Analysis ◽  
Carbon Sequestration Potential ◽  
Sequestration Potential ◽  
Article 5

Paris Agreement recognized the role of forests as carbon sink for mitigation of climate change, under Article 5 as REDD+, i.e., reducing emissions from deforestation and forest degradation and role of conservation, sustainable management of forests and enhancement of forest carbon stocks. Forest cover change analysis was done between two time periods 2005 and 2015 to assess the forest degradation. Carbon sequestration potential of the forests of Sikkim for mitigating climate change is also estimated. Benefits of implementing of REDD+ in Sikkim involving local communities as stakeholder to conserve and sustainably manage the forest is assessed. Gaps and challenges faced by the stakeholder in implementing REDD+ at project level are also highlighted.

scholarly journals Uncertainty in Historical Land-Use Reconstructions with Topographic Maps

2014 ◽  
Vol 33 (3) ◽  
pp. 55-63 ◽  
Author(s):  
Dominik Kaim ◽  
Jacek Kozak ◽  
Krzysztof Ostafin ◽  
Monika Dobosz ◽  
Katarzyna Ostapowicz ◽  
...  
Keyword(s):  
Land Use ◽  
Forest Cover ◽  
Reliability Assessment ◽  
Swiss Alps ◽  
Topographic Maps ◽  
Forest Cover Change ◽  
Change Analysis ◽  
Polish Carpathians ◽  
Historical Land Use

Abstract The paper presents the outcomes of the uncertainty investigation of a long-term forest cover change analysis in the Polish Carpathians (nearly 20,000 km2) and Swiss Alps (nearly 10,000 km2) based on topographic maps. Following Leyk et al. (2005) all possible uncertainties are grouped into three domains - production-oriented, transformation- oriented and application-oriented. We show typical examples for each uncertainty domain, encountered during the forest cover change analysis and discuss consequences for change detection. Finally, a proposal for reliability assessment is presented.

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