scholarly journals Assessment of the land surface temperature dynamics in the city of Sofia using Landsat satellite data

2018 ◽  
Vol 30 ◽  
pp. 42-62
Author(s):  
Petar Dimitrov ◽  
Pontus Olofsson ◽  
Georgi Jelev ◽  
Ilina Kamenova
Keyword(s):  
Change Detection ◽  
Land Surface ◽  
Forest Cover ◽  
Forest Disturbance ◽  
Image Data ◽  
Forest Cover Change ◽  
Forest Change ◽  
Image Stack ◽  
Sample Data

The paper presents the results of forest cover change mapping in two study areas in Bulgaria (in mountainous and plain-hilly terrain) for period of about 20 years. A comparison was made of two approaches for classification of multitemporal SPOT HRV/HRVIR data with 20 m spatial resolution. The first approach was the post-classification comparison, i.e. pixel-by-pixel comparison of forest/non forest maps produced by separate classifications of the images from the two ends of the time period. The second approach was a direct multitemporal classification of an image stack comprised of the two-date image data. Following international guidance, instead of counting pixels in the map to obtain the area of forest loss and gain, the areas were estimated by applying an unbiased estimator to sample data collected by stratified random sampling. The map was used to stratify the study areas. Producer’s, user’s and overall accuracy were also estimated using the sample data. A comparison of accuracy and area estimates, and confidence intervals of estimates, showed that the map produced by direct multitemporal classification was more accurate. It yielded consistently higher class-specific accuracies than the map made by post-classification comparison. As expected, the accuracies of the change classes – forest disturbance and reforestation – were significantly lower than that of the stable classes regardless of the change detection approach. Finally, practical issues and guidelines for future forest change detection studies were discussed.

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 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 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 Accuracy and Spatial Pattern Assessment of Forest Cover Change Datasets in Central Kalimantan

2018 ◽  
Vol 50 (2) ◽  
pp. 222 ◽  
Author(s):  
Sanjiwana Arjasakusuma ◽  
Uji Astrono Pribadi ◽  
Gilang Aria Seta
Keyword(s):  
Spatial Pattern ◽  
Forest Cover ◽  
Accuracy Assessment ◽  
Accurate Information ◽  
Forest Cover Change ◽  
Forest Change ◽  
Consistency Analysis ◽  
Error Matrix ◽  
Central Kalimantan ◽  
The Road

The accurate information of forest cover change is important to measure the amount of carbon release and sink. The newly-available remote sensing based products and method such as Daichi Forest/Non-Forest (FNF), Global Forest Change (GFC) datasets and Semi-automatic Claslite systems offers the benefit to derive these information in a quick and simple manner. We measured the accuracy by constructing area-proportion error matrix from 388 random sample points and assessed the consistency analysis by looking at the spatial pattern of deforestation and regrowth from built-up area, roads, and rivers from 2010 – 2015 in Katingan district, Central Kalimantan. Accuracy assessment showed that those 3 datasets indicate low to medium accuracy level in which the highest accuracy was achieved by Claslite who produced 71 % ± 5 % of overall accuracy. The consistency analysis provides a similar spatial pattern of deforestation and regrowth measured from the road, river, and built-up area though their distance sensitivity are different one to another. 

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