scholarly journals A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery

2021 ◽  
Vol 4 ◽  
Author(s):  
G. N. Tanjina Hasnat
Keyword(s):  
Surface Temperature ◽  
Temperature Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Forest Cover ◽  
Thematic Mapper ◽  
Forest Cover Change ◽  
Wildlife Sanctuary ◽  
Time Period ◽  
The Impact

Forest cover change is an important criterion as it affects the environmental balance whereas land surface temperature is a significant parameter within the earth climate system. Spatio-temporal change of forest cover can be detected and land surface temperature can be retrieved by applying remote sensing technology. The present study aimed to capture the impact of forest cover change on land surface temperature in Dudpukuria-Dhopachari Wildlife Sanctuary (DDWS), Bangladesh, using multi-spectral and multi-temporal satellite data. To avoid the biasness in the calculation, leaf flash time was targeted for collecting Landsat images from United States Geological Survey (USGS) Earth Explorer and, based on availability, images were collected purposively which ones had closer time period:1990 (March 5, 1990), 2000 (February 5, 2000), 2010 (February 24, 2010) and 2020 (March 23, 2020). Unsupervised classification was applied over the images Landsat 4–5 Thematic Mapper (TM), 7 Enhanced Thematic Mapper Plus (ETM+), and 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data for detecting forest cover change. To retrieve the land surface temperature, Mono Window Algorithm (MWA) method was applied over similar images. Maximum forest degradation was observed in 2010 and the change found was 17% as compared to 1990. After 2010, the forest started to flourish. Land surface temperature dramatically changes over the time period. The highest land surface temperature in the forested area was observed in 2020 (32.2°C) and it was changed 7.7°C from that of the 1990 (24.5°C). In every 10 years, almost 2.3°C–3.0°C temperature change was detected. In the first three decades, a reverse relationship was observed between land surface temperature and forest cover; however, in the last decade, land surface temperature was found to increase with the increase of forest cover. Thus, the results of the study revealed that land surface temperature may not be relevant with the local forest cover change directly. It can be estimated from the results that local forest cover change may have limited impact on local temperature rather than global forest cover change, whereas global warming could play a vital role in changing land surface temperature locally as well as globally.

Reconciling different approaches to quantifying surface cooling induced by afforestation in China using satellite observations

2021 ◽  
Author(s):  
Huanhuan Wang ◽  
Chao Yue ◽  
Sebastiaan Luyssaert ◽  
Jie Zhao ◽  
Hongfei Zhao
Keyword(s):  
Surface Temperature ◽  
Temperature Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Forest Cover ◽  
Satellite Observations ◽  
Forest Cover Change ◽  
Surface Cooling ◽  
Surface Temperature Change ◽  
Significant Difference

<p>Forest cover change can cause strong local biophysical feedbacks on climate. Satellite observations of land surface temperature (T) and land cover distribution or forest cover change have been widely used to examine the effects of afforestation/deforestation on local surface temperature change (ΔT). However, different approaches were used by previous analyses to quantifying ΔT, and it remains unclear whether results of ΔT by these approaches are comparable. We identified three influential approaches to quantifying ΔT used by previous studies, namely the actual ΔT resulting from actual changes in forest coverage over time and accounting for changes in background climate (ΔT<sub>a</sub> proposed by Alkama and Cescatti, 2016), potential ΔT by hypothesizing potential shifts between non-forest and forest at given native spatial resolutions of satellite products (ΔT<sub>p1</sub> by Li et al., 2015), and potential ΔT, but using the singular value decomposition technique to derive ΔT by hypothesizing a shift between a 100% complete non-forest and 100% forest (ΔT<sub>p2</sub> by Duveiller et al., 2019). China realized large-scale afforestation making it a suitable test case to compare satellite-based approaches for estimating ΔT following afforestation. We hypothesize that (1) ΔT<sub>a</sub> depends on the fraction of ground area that’s been afforested (F<sub>aff</sub>). (2) The relative magnitude between different approaches should be: ΔT<sub>a</sub> < ΔT<sub>p1</sub> < ΔT<sub>p2</sub>. (3) When ΔT<sub>a</sub> is extended to a hypothetical case that F<sub>aff</sub> reaches 100%, it should be comparable to ΔT<sub>p1</sub> or ΔT<sub>p2</sub>. We used multiple satellite observation products to test these hypotheses. The results show that the magnitude of actual daytime surface cooling by afforestation (ΔT<sub>a</sub>) increases with F<sub>aff</sub>, and is significantly lower than ΔT<sub>p1</sub> and ΔT<sub>p2</sub>. But no significant difference was found between ΔT<sub>p1</sub> and ΔT<sub>p2</sub>. A linear regression model established between ΔT<sub>a</sub> and F<sub>aff</sub> extends the ΔT<sub>a</sub>, when F<sub>aff</sub> reaches 100%, to a comparable magnitude than ΔT<sub>p1</sub> and ΔT<sub>p2</sub>. Our study thus highlights the importance to consider the actual surface cooling impact by afforestation projects in contrast to the potential effects, and provides a first study to reconcile different approaches to quantify the land surface temperature change due to afforestation.</p>

scholarly journals The Influence of Deforestation on Land Surface Temperature—A Case Study of Perak and Kedah, Malaysia

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 670 ◽  
Author(s):  
Wan Shafrina Wan Mohd Jaafar ◽  
Khairul Nizam Abdul Maulud ◽  
Aisyah Marliza Muhmad Kamarulzaman ◽  
Asif Raihan ◽  
Syarina Md Sah ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Forest Cover ◽  
Environmental Changes ◽  
Rapid Change ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Forest Cover Change

Over the past few decades, there has been a rapid change in forest and land cover, especially in tropical forests due to massive deforestation. The major factor responsible for the changes is to fulfill the growing demand of increasing population through agricultural intensification, rural settlements, and urbanization. Monitoring forest cover and vegetation are essential for detecting regional and global environmental changes. The present study evaluates the influence of deforestation on land surface temperature (LST) in the states of Kedah and Perak, Malaysia, between 1988 and 2017. The trend in forest cover change over the time span of 29 years, was analyzed using Landsat 5 and Landsat 8 satellite images to map the sequence of forest cover change. With the measurement of deforestation and its relationship with LST as an end goal, the Normalized Difference Vegetation Index (NDVI) was used to determine forest health, and the spectral radiance model was used to extract the LST. The findings of the study show that nearly 16% (189,423 ha) of forest cover in Perak and more than 9% (33,391 ha) of forest cover in Kedah have disappeared within these 29 years as a result of anthropogenic activities. The correlation between the LST and NDVI is related to the distribution of forests, where LST is inversely related to NDVI. A strong correlation between LST and NDVI was observed in this study, where the average mean of LST in Kedah (25 °C) is higher than in Perak (22.6 °C). This is also reflected by the decreased NDVI value from 0.6 to 0.5 in 2017 at both states. This demonstrated that a decrease in the vegetation area leads to an increase in the surface temperature. The resultant forest change map would be helpful for forest management in terms of identifying highly vulnerable areas. Moreover, it could help the local government to formulate a land management plan.

scholarly journals Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

2021 ◽  
Vol 13 (2) ◽  
pp. 323
Author(s):  
Liang Chen ◽  
Xuelei Wang ◽  
Xiaobin Cai ◽  
Chao Yang ◽  
Xiaorong Lu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Maximum Temperature Difference ◽  
Rapid Urbanization ◽  
The Impact ◽  
The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

scholarly journals Quantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale

2019 ◽  
Vol 52 (sup4) ◽  
pp. 74-83 ◽  
Author(s):  
Elena Barbierato ◽  
Iacopo Bernetti ◽  
Irene Capecchi ◽  
Claudio Saragosa
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
The Impact

scholarly journals LAND SURFACE TEMPERATURE ANOMALIES IN RESPONSE TO CHANGES IN FOREST COVER

2019 ◽  
Author(s):  
Behnam Khorrami ◽  
Orhan Gunduz ◽  
Nilanchal Patel ◽  
Souad Ghouzlane ◽  
Mohamed Najjar
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Forest Cover ◽  
Temperature Anomalies

scholarly journals SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 401-405
Author(s):  
M. K. Firozjaei ◽  
M. Makki ◽  
J. Lentschke ◽  
M. Kiavarz ◽  
S. K. Alavipanah
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Incident Angle ◽  
Surface Characteristics ◽  
Slope Aspect ◽  
Biophysical Parameters ◽  
The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

scholarly journals Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

2021 ◽  
Vol 10 (12) ◽  
pp. 809
Author(s):  
Jing Sun ◽  
Suwit Ongsomwang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Decomposition Analysis ◽  
Lulc Change ◽  
Main Components ◽  
The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

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