Assessment Of Forest Cover Changes Research Articles

A global annual fractional tree cover dataset during 2000–2021 generated from realigned MODIS seasonal data

Fractional tree cover facilitates the depiction of forest density and its changes. However, it remains challenging to estimate tree cover from satellite data, leading to substantial uncertainties in forest cover changes analysis. This paper generated a global annual fractional tree cover dataset from 2000 to 2021 with 250 m resolution (GLOBMAP FTC). MODIS annual observations were realigned at the pixel level to a common phenology and used to extract twelve features that can differentiate between trees and herbaceous vegetation, which greatly reduced feature dimensionality. A massive training data, consisting of 465.88 million sample points from four high-resolution global forest cover products, was collected to train a feedforward neural network model to predict tree cover. Compared with the validation datasets derived from the USGS circa 2010 global land cover reference dataset, the R2 value, MAE, and RMSE were 0.73, 10.55%, and 17.98%, respectively. This dataset can be applied for assessment of forest cover changes, including both abrupt forest loss and gradual forest gain.

Assessment of forest cover change, key drivers of change and perception of locals in Birendranagar Municipality, Surkhet District, Nepal

Land use and land cover change has an immense impact on the global environment and ecosystem and is mostly connected with human activities such as conversion of forest into agricultural land and settlements. Forests are changing globally in quantity and quality, and in both increasing and decreasing trends. For the Birendra Nagar Municipality, this study evaluated LULC dynamics, the conversion of forest cover from 2000 to 2020, as well as community perceptions on forest cover change, its drivers. Landsat images of 2000 and 2020 were used for LULC dynamics and for forest cover change detection Using software GIS 10.8. Questionnaire survey, key informant survey, field observation and secondary data were acquired from journal articles, reports, District Forest office, books, and various other sources to find out drivers of forest cover change and perception of locals. It was found that 1146 ha of forest area, 280.53 ha of water bodies, 752.62 ha of barren land were lost between 2000 and 2020. However, 1147.74 ha of settlement area and 1032.40 ha of agricultural land was gained.Net forest cover decreased overall by 4.67%, however, some areas within the municipality experienced an increase in forest cover namely Ward 11 and ward 16 of Municipality shows Highest positive and Negative Forest cover change in terms of Area. Expansion of agriculture land and settlements with the encroachment of forest area, haphazard infrastructure development, illegal timber harvesting and low regeneration capacity of land responsible for the reduction of forest, along with Community forestry approach, plantations programs, promotion of agroforestry practices, fuel transition were responsible drivers for improvements of forest cover. Water cycle disruption, Increases Natural hazard, Temperature rises, shortage of wood for fuel, extinction of indigenous species were the most perceived negative impacts of the forest cover reduction whereas income generation Activities, support biodiversity, easy to collect fuel wool and fodder and others including purifying air quality, increasing aesthetic beauty were the most perceived positive impacts of the forest cover improvement in the study area. Most of the locals have a holistic understanding of forest cover change. Strengthening forest protection, improving soil and water conservation structures, awareness creation, enrichment planting, Promote Alternate Energy Resources and Equitable benefit-sharing mechanism are possible solutions perceived by locals.

Investigating Forest Cover Change Using Historical GIS Technologies: A Case Study with an Example of Jurbarkas District of the Republic of Lithuania

Effective forest management systems based on comprehensive tools need to be developed for the analysis and assessment of forest cover change. This requires a wide range of data, starting from old maps, which allow the reconstruction of spatially referenced information on past forest areas, to recently collected data. The research methodology proposed in this paper involves a combined analysis of data from different sources, using statistical, comparative, and visual analysis methods. The case study is Jurbarkas District of the Republic of Lithuania characterized by a high land productivity score. The research found that the highest forest cover areas were in the 19th century and the lowest in the 20th century. It can be seen that during the period under research, forest areas ‘migrated’ from an area favorable for agricultural activities to a less favorable area with higher slopes. Permanent forest cover areas and the age of the dominant trees have been identified. The age of the trees in the forest areas has allowed for the identification of the periods when the most intensive logging took place. The rate of forest cover change made it possible to predict the total disappearance of the forest areas provided that the factors influencing forest cover change at that time remained unchanged. If the trends in forest change in Jurbarkas District had been maintained in the period between the 19th and 20th centuries, the forest would have disappeared in 2006.

Ứng dụng GIS và tư liệu viễn thám đánh giá biến động lớp phủ rừng tại vùng đệm Vườn Quốc gia Cát Tiên thuộc địa phận huyện Tân Phú, tỉnh Đồng Nai.

The Cat Tien National Park in the South of Vietnam represents a protected ecological area, it is located in tropical rainforest zone. For assessment of forest cover change in three buffer zones of Cat Tien National Park, namely Nam Cat Tien, Nui Tuong and Phu An at Tan Phu district, satellite images (Landsat 5 TM and Landsat 8 OLI) take in 1990, 2000, 2010 and 2020 were used. Land use/land cover were classified by 5 categories using maximum likelihood classifier (MLC) method, result of constructing a key for image classification showed that images without reference data for classification have over 83.50% of map accuracies. After classification, differentiation of forest land use/landcover was analysied in each year. Deforestation and agricultural area reduction took place in 1990 - 2020 period. Besides, study has quantified changes in forest cover during periods: 1990 - 2000, 2000 - 2010 and 2010 - 2020. As a result, findings show that the extents of forest cover have decreased from 5522.22 ha to 2620.98 ha during the period of 1990 - 2020. Maps of forest status as well as changes in forests can be used for forest resource management activities under the Cat Tien National Park management.

GIS-based spatio-temporal assessment of forest cover change and carbon sequestrations of District Abbottabad, Pakistan

Abstract Forest plays an important role in the climate of a country and ecosystem's balance. During the last decade, District Abbottabad of Pakistan has gone through extensive land-use changes due to accelerated development, urbanization, and agriculture. This study examines the change in forest cover due to environmental factors with the help of satellite images (Landsat, Sentinel) and their classification through supervised classification, as well as the application of change detection technique after classification using geographical information system (GIS) over the last three decades (1986–2019). The land-use transition matrix was calculated from the year 1986 to 2019. The result shows an overall significant increase of 3.17, 17.24, and 7.24% in the forest, vegetation, and built-up areas, respectively; whereas, water bodies and others (barren land) have decreased significantly by 0.69 and 26.96%, respectively. This study also provides an assessment of the incline of carbon stock from the forest area over the period of the study by 17.19%.

Assessment of Forest Cover Changes in Vavuniya District, Sri Lanka: Implications for the Establishment of Subnational Forest Reference Emission Level

Assessment of forest cover changes is required to establish the forest reference emission level (FREL) at any scale. Due to civil conflict, such assessments have not yet been undertaken in Sri Lanka, especially in the conflict zone. Here, we assessed the forest cover changes in Vavuniya District, Sri Lanka, from 2001 to 2020, using a combination of the Google Earth Engine (GEE) platform and the phenology-based threshold classification (PBTC) method. Landsat 5 TM data for 2001, 2006, and 2010, and Landsat 8 OLI data for 2016 and 2020 were used to classify forest cover by categories, and their related changes could be assessed by four categories, namely dry monsoon forest, open forest, other lands, and water bodies. With an overall average accuracy of 87% and an average kappa coefficient of 0.83, forest cover was estimated at 57.6% of the total land area in 2020. There was an increase of 0.46% per annum for the entire district between 2001 and 2010, but a drastic loss of 0.60% per year was observed between 2010 and 2020. Specifically, the dry monsoon forest lost 0.30%, but open forest gained 3.62% annually over the same period. Loss and gain of forest cover resulted in carbon emissions and removals of 165,306.6 MgCO2 and 24,064.5 MgCO2 annually, respectively, over the same period. Our findings could be used to set the baseline trend of deforestation, based on which, a subnational forest reference emission level can be established as an emission benchmark, against which comparisons of carbon emissions following the implementation of REDD+ activities can be made, and result-based payment can be claimed under the Paris Agreement.

A three decade assessment of forest cover changes in Nijhum dwip national park using remote sensing and GIS

Forest cover loss has a crucial impact on global climate change. The Nijhum Dwip National Park (NDNP), a deltaic part of the Sundarbans mangrove forest, has changed significantly over the decades. The goal of this study was to detect the decadal changes in forest cover using freely available satellite remote sensing data and GIS tools. A maximum likelihood classification technique was used to classify the Landsat images of 1990, 2001, 2011 and 2020. The potential of NDVI and SAVI-based classification for detection of forest cover changes in comparison to supervised classification was also evaluated. More than 42% of the total forest cover area (1781 ha) has been deforested over the decades between 1990 and 2011. The study also reported that nearly 1235 ha and over 856 ha of forest area disappeared from 4217.5 ha in 1990 to 2982.5 ha and 2126.16 ha in 2001 and 2011, respectively. In contrast, an increase of 310 ha of forest area has been recorded in the last decade, from 2011 to 2020. NDVI performed better in forest cover change detection than SAVI. This research will be invaluable for this type of research continuity and proper monitoring of this protected mangrove forest area, along with providing useful information to decision makers.

An Assessment of Forest Cover Change and Its Driving Forces in the Syrian Coastal Region during a Period of Conflict, 2010 to 2020

In Syria, 76% of the forests are located in the Syrian coast region. This region is witnessing a rapid depletion of forest cover during the conflict that broke out in mid-2011. To date, there have been no studies providing accurate, reliable, and comprehensive data on the qualitative and quantitative aspects of forest change dynamics and the underlying drivers behind this change. In this study, changes in the dynamics of forest cover and its density between 2010 and 2020 were detected and analyzed using multi-temporal Landsat images. This study also analyzed the relationship between changes in forest cover and selected physical and socio-demographic variables associated with the drivers of change. The results revealed that the study area witnessed a significant decrease in the total forest area (31,116.0 ha, 24.3%) accompanied by a considerable decrease in density, as the area of dense forests decreased by 11,778.0 ha (9.2%) between 2010 and 2020. The change in forest cover was driven by a variety of different factors related to the conflict. The main drivers were changes in economic and social activities, extensive exploitation of forest resources, frequent forest fires, and weakness of state institutions in managing natural resources and environmental development. Forest loss was also linked to the expansion of cultivated area, increase in population and urban area. Fluctuating climatic conditions are not a major driver of forest cover dynamics in the study area. This decrease in forest area and density reflects sharp shifts in the natural environment during the study period. In the foreseeable future, it is not possible to determine whether the changes in forest cover and its density will be permanent or temporary. Monitoring changes in forest cover and understanding the driving forces behind this change provides quantitative and qualitative information to improve planning and decision-making. The results of this study may draw the attention of decision-makers to take immediate actions and identify areas of initial intervention to protect current the forests of the Syrian coast region from loss and degradation, as well as develop policies for the sustainable management of forest resources in the long term.

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

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.

Assessment of Forest Cover Change of Dang, an Inner Terai District of Nepal

This study analyzed the dynamics of changes of forest cover classes in the inner Terai District Dang, Nepal, based on Landsat Thematic Mapper (TM) images from two different years, viz., 1990 and 2011. Forest cover change analysis was performed through the analysis of a classified Landsat TM image using supervised classification. The overall classification accuracy for seven different land cover classes considered in this study were 80.37% and 80.56% for years 1990 and 2011, respectively. These classified images were further reclassified as forest and non-forest to analyze forest cover dynamics effectively using the post classification change detection. The results indicated that during 1990-2011, the total spatial areal coverage of forest land converted into other land cover was 20612 ha (shrub-land), 8571 ha (agriculture), and 2787 ha (others) non-forest classes. A significant portion of non-forest classes was also converted into forest (e.g., 11433 ha of shrubland, 5663 ha of agriculture, and 5581 ha of other non forest classes). Sand and water bodies remained more or less constant during this period. While forest cover was estimated to be disappearing at the rate of 0.2% per year, dense forest appears to be converting into a sparse forest at the rate of 0.1% per year. Future study to assess the causes and driving forces of forest cover change in Nepal should get guidance from this study on where to target interventions.

Development of National Database on Long-term Deforestation in Sri Lanka

Sri Lanka is one of the biodiversity hotspots of the world. This study has utilized satellite remote sensing and GIS techniques to generate a nation-wide database on forests, forest types and land use/land cover of Sri Lanka. Spatial assessment of forest cover changes was carried out for the periods 1976–1985, 1985–1994, 1994–2005 and 2005–2014. The landscape fragmentation analysis has carried out to calculate the spatial and temporal patterns of forest. Land use/land cover map was prepared representing seven classes in 2014. The plantations occupy a large area (34.2%) followed by forests (33.4%) and agriculture (26.1%) in 2014. During the period of 1976–2014, the forest has been decreased by 5.5%. From 1976 to 1985 forest recorded a loss at an annual rate of 0.49%. This annual rate decreased to 0.01% during 2005–2014 indicates declining trend of deforestation and effective conservation measures. The study found deforestation hotspots in south east and northern most parts of the Sri Lanka. Total number of patches estimated has increased from 15193 in 1976 to 16136 in 2014. The study has found that main causes of deforestation in Sri Lanka were due to expansion of agriculture and plantations. The extent of change detected in the study through geospatial techniques has significance to the forest ecology and management of natural landscapes in Sri Lanka.

Review and combination of recent remote sensing based products for forest cover change assessments in Cameroon

SUMMARY Two recent remote sensing derived products providing assessment of forest cover change in the Congo Basin for the last two decades were compared and combined to obtain estimates of forest cover change in Cameroon for the period 1990–2012. The intensity of deforestation in the whole region decreased from the 1990s to the 2000s. Forest loss is observed everywhere in Cameroon but present higher intensity nearby the Atlantic coast. The analysis of the spatial distribution of the forest loss in Cameroon reveals that an important part of the loss is happening outside the permanent and the non-permanent forest domain. The Landsat spatial resolution used in the two products allows us to capture change caused by agro-industrial exploitation as well as shifting cultivation, infrastructure and selective logging when the area affected is large enough. But it fails to monitor forest degradation and small scale deforestation events.

A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region.

Integrated Assessment of Forest Cover Change and Above-Ground Carbon Stock in Pugu and Kazimzumbwi Forest Reserves, Tanzania

A study was conducted to estimate the forest cover change, quantify and map tree above-ground carbon stock using Remote sensing and GIS techniques together with forest inventory. Landsat images of 1980, 1995 and 2010 acquired during dry season were used in the estimation of cover changes. Supervised image classification using Maximum Likeli-hood Classifier was performed in ERDAS Imagine software to analyze the images and further analysis was performed in Arc GIS 9.3 software. Stratified sampling procedure was used to select concentric inventory plots in Pugu Forest Reserve (PFR) and Kazimzumbwi Forest Reserve (KFR). Plots were laid according to NAFORMA, and the tree parameters in each sampling plot were collected. A Microsoft Excel spreadsheet was used to compute the above-ground bio- mass for each plot using an empirical equation relating wood basic density and tree height. The above-ground carbon was calculated using a conversion factor of 0.49. Geostatistical method in ArcGIS was used to analyze and map carbon. Results revealed that for the periods 1980-1995 and 1995-2010, Closed Forest in PFR decreased by 4.5% and 25.3% respectively, while for KFR, Closed Forest decreased by 11.9% and 31.3% respectively. The mean carbon density for PFR and KFR were respectively 5.72 tC/ha and 0.98 tC/ha while carbon stocks were 14 730.41 tC and 7 206.46 tC re- spectively. The revealed low carbon densities were attributable to decline in area under Closed Forest in the two Forest Reserves. The study recommends concerted efforts to enhance proper management of the forests so that the two forest reserves may contribute to REDD initiatives.

Forest cover change assessment in conflict-affected areas of northwest Pakistan: the case of swat and shangla districts

This study examines the spatial and temporal forest cover changes in Swat and Shangla districts to understand the deforestation pattern in context of the recent security conflict in these districts. We used multi-resolution satellite images to assess the long term deforestation from 2001 to2009 and also to identify episodic forest cutting areas appeared during the conflict period of Oct. 2007 – Oct. 2008. There are only 58 ha of deforestation identified during the conflict period while 1268 ha of gross annual deforestation were assessed during last eight years. Most of the deforestation patches persist around the administrative boundaries at sub-district levels (tehsils) which can be attributed to ambiguity in unclear jurisdiction between the forest official. The results highlight that the forest cutting appeared in Swat and Shangla during the conflict period is not as significant when compared with the long term deforestation pattern in the area. On the one side the results of the study are supportive to the picture that emerges from international studies which report high rate of deforestation in the country and on the other side it negates any relation between the security situation and the increasing deforestation in the north western Pakistan. The study concludes that deforestation assessments require verification by independent sources of data, such as satellite imagery to improve our understanding of deforestation processes.