Landsat TM Images Research Articles

FOREST COVER CHANGE DETECTION IN PAKTIA PROVINCE OF AFGHANISTAN USING REMOTE SENSING AND GIS: 1998-2018

Monitoring changes in forest cover is important to address issues like biodiversity conservation, sustainable management, and climate change. The study has been conducted in the Paktia province of Afghanistan to assess the changes in different forest classes which occurred together with political changes by using Remote Sensing and Geographic Information System (GIS). The change was analyzed for a period of two decades, i.e., 1998 to 2018. Landsat TM and OLI satellite images of 30m resolution for the years 1998 and 2018 were used respectively. The overall classification accuracy of the mapping was estimated as 82.67% and the kappa coefficient was estimated as 0.8081. The study area was delineated via visual image interpretation technique into 11 LULC classes’ viz., closed forest, open forest, forest scrub, grassland other classes (Agriculture, Agroforestry, horticulture, habitation, waterbody, wasteland, and snow). The comparison of maps 1998 and 2018 revealed that the total area under closed forest, open forest, showed an increase of 0.43%, 0.73%, respectively. While the areas under forest scrub, showed a decline of 0.30%, during the study period (1998-2018).

Is aquaculture development responsible for mangrove conversion in India? - A geospatial study to assess the influence of natural and anthropogenic factors on mangroves in the last three decades

Aquaculture was cited as the primary factor for the reduction of mangrove area worldwide. But, there is no data available on the extent of conversion of mangroves for aquaculture in India, the second largest country in global aquaculture production. The present study assessed the factors of changes in mangroves area between the pre aquaculture period and recent years. Landsat TM images of 1988, Sentinel 2 A images of 2018, geospatial analysis, ground truth verification, post-classification approach, and accuracy assessment were used for the assessment. Comparing the extent of mangroves area in 1988 with 2018, the gain and loss was 278% and 30% in Kerala, 859% and 19% in Karnataka, 204% and 18% in Maharashtra, and 4100 and 20% in Daman and Diu respectively. Mangrove extent has increased due to restoration activities, while lost due to coastal erosion and other developmental activities. Combining mangrove statistics drawn from this study with available data from earlier study, indicate that the aquaculture has converted 2052 ha of mangroves, which represents 0.66% of India's mangrove area. The extent of mangroves increased by 20.72% in India between 1988 and 2018. There is a possibility that part of the mangrove - converted lands are privately owned. Besides, Coastal Aquaculture Authority Act of 2005 prohibits the use of mangrove lands to aquaculture. The present study proves that shrimp aquaculture is not accountable for the large-scale changes in the mangrove lands of India, except in Andhra Pradesh State. However, it demands the land use policy to protect the mangrove lands in private owned lands by allocating alternate resource space, and monitoring periodically.

Impact of Al-Ahdeb Oil Field on the Surrounding Environment Using Remote Sensing and GIS Techniques, Wasit Governorate, Iraq

Remote sensing and Geographical Information System (GIS) are used in this study to detect environmental changes in the vicinity of Al-Ahdeb oil field, southwest of Kut City, Wasit Governorate. Different image indices, such as Normalized Differences Vegetation Index (NDVI), Normalized Differences Soil Index (NDSoI), and Normalized Differences Salinity Index (NDSI) are used. Two LANDSAT images with acquired data of September 2007 and 2016 are used to detect the environmental changes and to detect the effect of Al-Ahdeb oil field before and after construction and industrial operation. The results of change detection show there is a high decrease in the vegetation cover during the year 2016 compared with 2007, where the area of vegetation cover has decreased from (165.85 Km 2 ) in 2007 to (119.62 Km 2 ) in 2016. The change detection results from NDSI show that the saline soil in (2016) is higher than those in (2007). The NDSI derived from Landsat TM (2007) image confirms that there is significant increase of salinity in the study area, where the calculated area of the salinity in 2007 is (5.65 Km 2 ) while in 2016 it is (21.951 Km 2 ). Change detection, using NDSoI, show that the land in the study area is going toward desertification and soil degradation. The decrease in the vegetation cover, which in turn led to soil erosion in addition to water shortage and the pollution by the waste of the oil field, could be the main reasons of the desertification in the area. Keywords: Environmental change detection; Image Indices; NDVI; NDSI; NDSoI; Al Ahdeb oil field. DOI: 10.7176/JEES/12-8-03 Publication date: August 31 st 2022

Changing nature of Land surface temperature and transformation of vegetation cover and water bodies in the 2nd largest urban agglomeration of West Bengal, Eastern India

Land surface temperature (LST), vegetation and water are very crucial for maintaining a healthy urban ecosystem. Asansol is one of the fast growing urban industrial agglomerations of West Bengal in Eastern India. In the last few decades the city has experienced a rapid urban industrial expansion along with the population growth which is associated with the continuous increase of LST. Considering this the present study has been designed to explain the nature of LST changes and transformation of land cover over the last thirty years utilizing the Landsat imageries in GIS platforms. In this study changing nature of land surface temperature, vegetation cover and surface water bodies and their interrelationship over the last thirty years have been analysed. Landsat 5TM images of May and October for the year 1990, 2000 and 2010 and Landsat 8 OLI images of May and October for 2020 have been utilized in GIS platform. LST and different indices NDVI, MNDWI and NDBI have been extracted for analyzing the nature of change along with image classification. The results of the study depict that LST is increasing continuously. In the first decade (1990–2000) LST increased at 0.24 °C/year followed by an increasing rate of 0.33 °C/year in the second decade (2000–2010) and 0.37 °C/year in the last decade (2010–2020). Same trend is also perceived for built up area, where it increased at 0.96 km2/year, 1.71 km2/year, and 2.49 km2/year in corresponding decades. On the other hand, the vegetation cover is continuously decreasing (1.59 km2/year, 0.75 km2/year and 0.49 km2/year in consecutive decades) as well as the surface water bodies (0.62 km2/year in, 0.41 km2/year, and 0.25 km2/year in corresponding three decades) over the last thirty years. Correlation and regression analysis of land surface temperature and various indices shows positive relation between LST and NDBI, negative relation between LST and MNDWI, and also between LST and NDVI. Urban-industrial expansion is the important factor for increasing temperature and transforming vegetation and surface water bodies.

Quantification of the effect of host patch configuration on the abundance of Bemisia tabaci in central Argentina: a multimodel inference approach.

Bemisia tabaci is a complex of species, which is considered the most common and important pest of a wide range of crops belonging to many different botanical families. In Argentina, this species is recognized as a vector of geminiviruses, and Middle East-Asia Minor 1, Mediterranean, New World and New World 2 have been found to coexist in the same area. Landscape elements, like habitat patch area and isolation, define the habitat configuration and have a direct effect on insect populations between and within host patches. In this paper, we analyse the effect of potato patch configuration on the distribution and abundance of B. tabaci. Potato patches were identified using Landsat TM5 and TM7 images, and a supervised classification was performed to quantify the spatial distribution of the patches in the whole study area. Potato patch metrics were estimated using Fragstats 4.4. Generalized linear mixed models were employed to analyse the relationship between whiteflies and landscape configuration, through a multimodel inference approach, finding that B. tabaci abundance and landscape metrics were very variable. After a multimodel selection process, we found that perimeter-to-area ratio and Euclidean distance between patches were the variables that best explained whitefly abundance in potato patches. Implications of these findings are discussed.

Vegetation Coverage Monitoring Model Design Based on Deep Learning

Based on the medium-resolution Landsat TM and OLI satellite images in the study area, the deep learning ENVINet-5 model is adopted for vegetation coverage monitoring. By referring to the fusion image and Google Earth high-resolution satellite image, the training samples and verification samples are manually labeled, and the labels of four types of ground objects (desert, water body, cultivated land, and construction land) are made. Through the ENVI deep learning binary classification model, the labeled training samples are trained, and a large number of samples of desert, water, and cultivated land are extracted and transformed into corresponding label images. Then, a large number of training sample labels extracted from the model are combined with the manually made construction land sample labels and both of them are used as the training samples of the ENVI deep learning multiclassification model. According to the classification process of the deep learning model (creating label image, initializing training model, and training model and model classification), through the adjustment of various parameters, the four types of ground objects in the study area are finally classified. Finally, the classification results that meet the accuracy requirements are statistically analyzed. It is proved that the model classification results can meet the use requirements.

Study of Fluctuations in Surface Area of Lake Haramaya using NDWI and MNDWI Methods

Lakes are of great value to human beings and important for various reasons like regulating the flow of river water, to maintain the eco-system and storage of water during the dry seasons. Lake Haramaya, which is situated at 14 km Northwest of Harer town (UNESCO Site) is one of the famous and beautiful lakes of Ethiopia. It acts as a source of life for human beings and animals. The over exploitation of the lake haramaya for water supply and agricultural purposes has led to its extinction in the last two decades. This study attempts to identify the fluctuations in surface area of Lake Haramaya between 1995 to 2020 by using multi-temporal satellite data. The Landsat 5TM images of 1995, 2000 and 2010, Landsat 7ETM+ image of 2005 and Landsat 8 OLI TIRS images of 2015 and 2020 are analyzed using the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) methods. These methods are used to quantify the changes in surface area and compared to each other for identifying the suitable method for detecting water bodies. The present study shows that, between 1995 to 2010 the lake lost up to 2.3238 sq.km and almost dried. But, during 2011 to 2020 the lake surface area increased by 2.6946 sq.km. The study states that the lake surface area is fluctuating and MNDWI method is highly reliable in extracting water bodies.

GIS-based analysis of green space in urban Himalayas and modelling the effects on destination business performance

Purpose This paper aims to analyse the changes in the extent of the green space in the city of Srinagar, a unique urban Himalayan destination, and examine whether the difference in the green space has a significant effect on the destination business performance measured in terms of loss of ecological attractiveness. Design/methodology/approach This study was carried out in two phases in the study area. In phase I, the changes in the extent of the green space area were analysed using Landsat TM and Sentinel Images for classification. The study has used the period from 2001 to 2018 to understand changes in the green space. The Post-Classification Comparison technique was used to investigate the variation in the green space zones in the city of Srinagar. In phase II, the paper evaluated the impact of change in the green space on the destination business performance. The data was collected from the tour operation companies through a questionnaire survey, and the impact path was examined using structural equation modelling. Findings Results reveal that the green space in the city of Srinagar has decreased over the past 18 years, and the decreasing green space has a significant effect on the destination business performance. Research limitations/implications Identifying the impact of decreasing green space on the destination business performance of the study area under investigation is essential for tourism development both in terms of new product development and resource preservation. Developing a measurement scale showing the impact of decreasing green space on destination business performance could offer destination managers a means of identifying the essence of the green space in the destination regions. These findings add to the growing literature on the attributes of tourism destinations, providing scholars with new insights into the role of green space in destination performance. The current study offers evidence of the impact of decreasing green space on the destination's performance. This provides a new perspective for future studies on visitor satisfaction as a potential mediator of the relationship between reducing greenspace and destination business performance. The main limitation of this study is that the researchers have only analysed the impact of decreasing green space on the destination business performance in terms of its ecological competitiveness. Other destinations business performance verticals, such as hotels, restaurants and grocery stores were not considered by this study and can be taken up for future investigation. Practical implications This study provides empirical insights that can have significant implications for researchers, policymakers, destination management organizations, academia and practitioners and further enrich the existing literature by establishing an empirical argument in the context of urban destinations positioned with a fragile Himalayan ecosystem. Originality/value This study aims to assist the urban administrators in improving the green space ecosystem in the region, which can help attain the sustainability of the city environment and assist in economic regeneration in urban settings.

Local Scale Assessment of Forest-Cover in the Tropics – An Implication to Habitat Conservation

Deforestation is one of the greatest environmental concerns that the world is facing at present. As a result of human-mediated deterioration or destruction, tropical forests have altered at an unparalleled rate during the last century. Further tropical forest transition does not occur evenly throughout a region or country; rather, it is localized in a very limited area. As such, the study aimed to measure and document the deforestation and degradation on a small forested habitat in the tropics, i.e. Kaki Reserve Forest under Marat Longri Wildlife Sanctuary, northeast India using remote sensing technology. The forest conditions were observed using Landsat TM and OLI satellite images between 1991 and 2015. The current study used the Forest Canopy Density Mapping and Monitoring Model to track deforestation or degradation in the test region. Results show a significant decline in forest cover in the area. It was observed that between 1991 and 2015, the 53.3% of the total area is under pressure of deforestation and degradation. Rate of forest cover transformation under different classes ranges from 0.37 to -8.15 which are exceptionally high in comparison to other parts of the country. The study also indicated that increased human activities such as illegal-felling, agricultural development, encroachment, and collections pressure have caused huge disruptions in this forested habitat throughout the study period. Thus, it requires rapid attention in order to ensure effective forest planning and management. The study also demonstrates how integration of remote sensing data and biophysical models can be used to examine spatial forest state, which may be used for long-term forest management at the local and regional levels.

EXTENT OF URBANIZATION IMPACT ON FOREST RESOURCES IN OJU LOCAL GOVERNMENT AREA, BENUE STATE, NIGERIA

Several researches have been carried out on urbanization especially urban areas in Nigeria and globally, but were mostly centred on major cities and little has been done on the growth of smaller towns. This study was carry out to determine the impact of urbanization on forest resources of Oju local Government Areas of Benue State, Nigeria.. Specifically the study determined the extent and growth rate of urban areas and their impacts on forest resources in Oju local Government Area of Benue State, Nigeria. Remotely sensed images of Landsat TM and ETM+ were acquired for 1998 and 2008 while Operational Landsat Imager (OLI) was used for 2018 respectively. The images were subjected to various image processing techniques and a supervised classification using Maximum Likelihood Classifier was performed in Integrated Land and Water Information System (ILWIS) software. The classification resulted in five classes of Land use and Land cover (LULC) of forested areas, farmland, vegetation, built-up areas and water. The results shows that between 1998 and 2018 forest decreased by -231.5.km2 (63.24%) representing an annual rate of -3.16%. The built-up area increased by 150.45km2 (108.99%) at an annual rate of change of 5.45% Farmland area decreased by -30.56 km2 (-6.5%) at an annual growth rate of 0.32%. The study revealed that, loss of naturally vegetated areas was mainly as a results of urban growth and expansion, farming and logging activities with commercial activities. The study recommends awareness creation against deforestation and encouragement of community and urban forestry.

Assessment of the Effectiveness of Sand-Control and Desertification in the Mu Us Desert, China

The first successful sand-control was achieved in the Mu Us Desert by local people in the 1950–1960s, and their experience and approach have been extended to the whole Ordos and Northern China since then. The objective of this paper is to assess comprehensively the effectiveness of sand-control in 15 counties in and around Mu Us using multitemporal satellite images and socioeconomic data. After atmospheric correction, Landsat TM and OLI images were harnessed for land cover classification based on the ground-truth data and for derivation of the GDVI (generalized difference vegetation index) to extract the biophysical changes of the managed desert and desertification. Climatic, socioeconomic, environmental and spatial factors were selected for coupling analysis by multiple linear and logistic regression models to reveal the driving forces of desertification and their spatial determinants. The results show that from 1991 to 2020, 8712 km2 or 63% of the desert has been converted into pastures and shrublands with a greenness increase of 0.3509 in GDVI; the effectiveness of sand-control is favored by the rational agropastoral activities and policies; though desertification occurs locally, it is associated with both climatic and socioeconomic factors, such as wind speed, precipitation, water availability, distance to roads and animal husbandry.

Effects of Climate Change and Human Activities on Aeolian Desertification Reversal in Mu Us Sandy Land, China

The aeolian desertification in Mu Us Sandy Land (MUSL) in northern China have been paid much attention, but the relative contributions of climate change and human activities to desertification dynamics are still not clear. Based on the Landsat MSS, TM, ETM+ and OLI images in 1975, 1990, 1995, 2000, 2005, 2010 and 2015, we developed a database of aeolian desertification land distribution, discussed the spatial and temporal variation of aeolian desertification, and discovered the relative contributions of climate change and human activities to desertification reversal, using the trends of the potential net primary productivity (NPP) and the human-influenced NPP with meteorological data and MODIS NPP products. The results indicated that aeolian desertification developed firstly from 1975 to 2000, with serious and severe aeolian desertification land continually increasing, and then changed into a reversal state from 2000 to 2015, as the serious aeolian desertification land decreased, although the severe, moderate and light aeolian desertification land lightly increased. Human activities were the dominant factor in desertification dynamics in MUSL and had different contributions to aeolian desertification reversal in different periods. This study will improve our understanding of the processes of aeolian desertification.

English

The northern part of the Southern rivers, which is the subject of this study, lies between 18° 54' and 14° 48' north latitude and 11° 30' and 12° 54' west longitude. It is characterized by a dense hydrographic network dominated by maritime marshes and expanses of salty lands and mangrove mudflats that have developed on a vast coastal plain. The objective of this study is to analyze spatio-temporal changes of the geomorphological units and to simulate their spatial behavior in 2035. The methodology is based on the processing of Landsat TM and Landsat OLI satellite images acquired in 1986 and 2018, respectively. Change detection is performed by post-classification comparison of the two previously validated land cover states with overall accuracies of 91% for the 1986 image and 97% for the 2018 image. The prediction made using the CA-Markov model yielded a result with 88% agreement. This made it possible to understand the spatio-temporal evolution of land use and changes in the near future (2035). The results of the land use prediction in 2035 show that bare land will have the highest gain with 93% probability while tans will decrease by 60% to other land use categories. Vegetation and mangroves will lose 47 and 44% of their areas respectively to bare soil and mudflats. A strong degradation of vegetation cover is predicted by the Markov chain by 2035. The probability of change in the area covered by water is the lowest during this period.   Key words: CA-Markov, landsat, land use change, predictive modeling, Southern rivers.

Investigation of Long and Short-Term Water Surface Area Changes in Coastal Ramsar Sites in Turkey with Google Earth Engine

Deltas and lagoons, which contain many flora and fauna, have rich coastal ecological and biological environments, and are wetlands of vital importance for humans. In this study, the current problems in all coastal Ramsar sites in Turkey are summarized, and changes in water surface areas are investigated using Landsat and Sentinel 1/2 satellite images on the Google Earth Engine (GEE) cloud computing platform. Landsat TM and OLI images were used in the long-term analysis, and time series were created by taking annual and July to September averages between 1985 and 2020. In the short-term analysis, monthly averages were determined using Sentinel 2 images between 2016 and 2020. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in the months when optical data were not suitable for use in monthly analysis. The Normalized Difference Water Index (NDWI) was used to extract water surface areas from the optical images. Afterwards, a thresholding process was used for both optical and radar images to determine the changes. The changes were analyzed together with the meteorological data and the information obtained from the management plans and related studies in the literature. Changes in the water surface areas of all coastal Ramsar sites in Turkey were determined from 1985 to 2020 at different rates. There was a decreasing trend in the Goksu and Kızılırmak Deltas, which also have inland wetlands. The decreasing rates from 1985 to 2020 were −24.52% and −2.86%, for annual average water surfaces for the Goksu and Kızılırmak Deltas, respectively, and −21.64% and −6.34% for the dry season averages, respectively. However, Akyatan Lagoon, which also has inland wetlands, showed an increasing trend. Observing the annual average surface area from 1985 to 2020, an increase of 438 ha was seen, corresponding to 7.65%. Every year, there was an increasing trend in the Gediz Delta and Yumurtalık Lagoons, that do not have inland wetlands. The increasing rates from 1985 to 2020 were 46.01% and 17.31% for the annual average surface area, for the Gediz Delta and Yumurtalık Lagoons, respectively, and 38.34% and 21.04% for the dry season average, respectively. The obtained results reveal the importance of using remote sensing methods in formulating strategies for the sustainable management of wetlands.

Simple Method to Extract Lake Ice Condition From Landsat Images

Ice plays key roles in regulating hydrological, ecological, biogeochemical, and socioeconomic functions of lakes. Long-term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> lake ice phenological records indicate that lake ice is trending toward later freeze-up, earlier breakup, and a shorter ice duration. Parallel to study of lake ice using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> records and process-based models, satellite remote sensing can expand our understanding of lake ice change over large spatial scales. However, most remote sensing studies have focused on large lakes or short periods of time, which may not robustly represent changes over multidecadal time periods or in the much more numerous small lakes. Here, we present a random forest model, Sensitive Lake Ice Detection (SLIDE), to accurately extract ice conditions from Landsat TM, ETM+, and OLI images. We trained the model using a manually labeled lake ice dataset (1089 labeled areas over 995 lakes globally). Our results show that our model achieves accurate classification between ice/snow and water (accuracy: 97.8%, kappa coefficient: 95.5%). Comparing Landsat-derived ice cover with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> ice conditions, we show that our model produces less bias, lower RMSE, and higher kappa than does the Landsat snow/ice flag from the quality assessment band. This is especially true during the transitional period surrounding the ice on and off dates reported from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> (mean bias −7.3% from our model, −17.3% from the Landsat quality band). Our results demonstrate the feasibility of mining the rich Landsat archive to study lake ice dynamics and of better flagging ice-affected lake observations.

西双版纳地区森林变化碳效应与生态效益评估

减少发展中国家因森林砍伐与森林退化导致的碳排放和保持碳储量（REDD+），不仅能减少因森林砍伐和森林退化造成的碳排放，而且还可以带来其它生态效益，如减缓森林破碎化、保护生物多样性和增强水土保持功能等。以中国的西双版纳地区为研究区域，以毁林最严重的1976-2007年为REDD+基线，基于卫星影像，并结合植被指数，提取了研究区的土地利用变化信息。基于IPCC温室气体清单方法，计算了研究区的森林碳储量变化。在此基础上，对REDD+的碳汇效益和生态效益进行了系统综合评估。结果显示：（1）1976-2007年间天然林碳储量从占总碳储量的78.24%减少至50.52%，这是造成西双版纳地区碳储量减少的主要原因。（2）1976-2007年，天然林的斑块数量和平均最近邻距离分别增加了120.00%和25.21%，平均斑块面积下降了71.98%，说明天然林的破碎化程度加剧。从研究区整体景观格局来看，斑块数量、Shannon多样性指数和Shannon均一性指数分别增加了8.16%、51.39%和34.07%；与此同时，平均斑块面积和景观内聚力指数分别下降了26.26%和2.13%，表明研究区整体景观格局朝破碎化方向发展，这和碳排放的增加相一致。（3）该区的土壤侵蚀程度逐年加剧，并与碳储量的减少密切相关。分析表明，土壤侵蚀剧烈的区域，其平均碳排放也相对较高。1999年到2007年间，剧烈侵蚀区域的平均碳排放是轻度侵蚀区域的6倍多。这些结果说明：如果REDD+政策得以实施，不仅能减少因毁林和森林退化导致的碳排放，而且能带来高的生态效益。从这个角度出发，REDD+的本质可以认为是以"增汇"或"减排"为主导功能，同时提升生态效益的一种管理方式。;The REDD+ partnership works to promote the reduction of greenhouse gas (GHG) emissions by reducing emissions from deforestation and forest degradation in the developing countries through positive incentives and conversation of forest carbon stocks. It is regarded as an essential component of the post-2012 climate regime to stabilize GHG emissions and engage the developing countries in worldwide mitigation endeavors. If cost-efficient carbon benefits can be achieved through REDD+, increases in atmospheric CO₂ concentrations could be slowed, effectively buying much needed time for countries to move to lower emissions technologies. We chose tropical forests in Xishuangbanna of China as our study area and took the most severe deforestation from 1976 to 2007 as the baseline of REDD+. Land use information was obtained through the combination of Landsat TM images and Normalized Difference Vegetation Index masks. Forest carbon storage change in the study area was firstly calculated based on IPCC methodology. Secondly ecosystem-based benefits from REDD+ were assessed systematically. Our results showed that from 1976 to 2007, the natural forest carbon storage decreased from 78.24% to 50.52% of total carbon storage, which was the main reason for the reduction of carbon storage in Xishuangbanna. At the meantime, significant degradation of ecosystem services emerged. This was demonstrated by two important indicators:forest fragmentation and soil erosion. For example, from 1976 to 2007 the percentage change in the carbon stocks was 1.4%, accompanying with increasing 8.16% in number of patches, 51.39% in Shannon's diversity index and 34.07% in shannon's evenness index. Meanwhile, mean path area and patch cohesion index decreased by 26.26% and 2.13%, respectively. It suggested that the forest ecosystem was changing in both carbon storage and forest landscape structure. The number of patches and average nearest neighbor distance of natural forest increased by 120.00% and 25.21%, respectively, and the average patch area decreased by 71.98%, indicating the degree of exacerbated fragmentation in natural forest. In this sense, carbon emission and forest fragmentation were well integrated. Furthermore, we investigated relationship between carbon emission and soil erosion and high correlation was found. For example, carbon emissions which accounted for 45% of the total located in the high erosion area, which is 18% of the whole study area. As a result, when forests that would have been lost or degraded are retained or restored through REDD+, they deliver ‘multiple benefits’ in addition to protecting or enhancing carbon stocks. These ecosystem-based benefits may include conservation of forest biodiversity, water regulation, soil conservation, timber, forest foods and other non-timber forest products, most of which have been demonstrated or proved in this study. By securing benefits beyond carbon, REDD+ has the potential to encourage countries to realize a broader range of values.

Potential of medium-resolution satellite imagery for monitoring sand transport in pre-Saharan urban areas

One of the most spectacular manifestations of desertification is silting up. On a sandy windy day in the Saharan environment, the atmosphere can be so loaded with solid matter that it feels like twilight. It thus presents a danger and a health risk for the population at two levels: i) at the respiratory level, firstly because of the inhalation of solid matter and ii) at the level of sight; solids entering the eye can cause irritation and over time serious infections. One of the most spectacular manifestations of desertification is silting up. On a sandy windy day in the Saharan environment, the atmosphere can be so loaded with solid matter that it feels like twilight. Wind erosion is the main cause of all silting up, the wind playing the double role of erosive agent and transport agent. However, in pre-Saharan environments, sand can also be transported by bedload in wadis; in some cases, the sand can also be of very local origin (erosion of soft sandstone for example); some popular cultures then speak of a "source of sand" which they describe as "inexhaustible". The silting up of a territory can be accelerated (climatic conditions, existence of favorable transport corridors) or slowed down by natural screens (vegetation or relief, for example). Studying the silting up and trying to understand its causes and evolution requires having a multi-date synoptic view of the territory; satellite imagery then appears to be the most appropriate means to provide adequate basic information. The present study has three main objectives: i) delimit areas covered with sand or likely to be; ii) delimit the potential foci (or sources) of supply for the silting up phenomenon and finally iii) hypothesize on the progression of the silting up phenomenon for a pre-Saharan area where we are witnessing a significant and rapid extension to the both urban space and cultures on the outskirts. It mainly concerns the city of Laghouat, a former oasis in the southern foothills of the Saharan Atlas, and its periphery. The population of Laghouat increased from 42,800 inhabitants in 1977 to 119,043 inhabitants in the year 2003 (estimate), i.e. a variation of 178% (i.e. a multiplication by a factor of 2.8) in 26 years resulting in increasingly important consumption needs of all kinds and a profound change in space. It is estimated at 170,693 in 2021. Data from the National Statistics Office are only available until 2019 for overall statistics. For the present work we used several satellite images at different spatial resolutions: a Landsat MSS image from April 1972 at 80 meters resolution, two Landsat TM images at 30 meters from April 1987 and April 2000 and finally an image extract. ASTER from 2004 to 15 meters. We applied a specific index combining some of the channels to accentuate the perception and the distinction of the zones of transfer and accumulation of sand. The comparative analysis of the images makes it possible to specify, firstly, the preferred directions of sand transport and, secondly, the directions of transport and then the areas likely to be most affected.

Presenting the spatial-temporal model for assessing and predicting qualitative changes of the groundwater resources in Shahriar, Tehran, Iran.

The quality of surface and groundwater are fundamental concerns. The World Health Organization (WHO) reports that 80% of diseases are waterborne. This percentage mostly lies in developing nations and underdeveloped countries. Determining the relationship between land-use patterns and groundwater quality changes is one of the methods to evaluate the impact of land-use on the hydrological condition of each region which ultimately assists in managing natural resources. In the present study, the spatial-temporal model via satellite images of Landsat TM, ETM, and OLI for2002, 2007, 2012, and 2017 are used to investigate all these modalities in Shahriar Plain, Tehran, Iran. Following image processing and analysis, the land-uses of the study area are classified into urban, farming, and bare lands. In order to assess the water quality of the study area, the periodic sampling data of piezometric wells in the plain were used from 2002 to 2017. Based on the US Salinity Laboratory (USSL) diagram, water qualities for agricultural uses are classified. Results demonstrated that land area has changed over time indicating an increase in urban land area and declines in agricultural and bare lands. Additionally, the results of the groundwater quality indicated C3S1 class in the second and third periods have increased to 3.58% (1025.93ha) and 12.82% (9367.85ha) while C2S1 class has been decreased in second and third periods to 3.58 and 12.82%. C1S1 and C4S1 classes during the three periods have been 0.

Urban Heat Island Assessment using Remote Sensing Data in West Java, Indonesia: From Literature Review to Experiments and Analyses

Urban heat island (UHI) is a significantly increasing temperature that occurs in the urban region due to urbanization and anthropogenic activities. The UHI represents environmental quality decrease and able to change a microclimate in the long term. It phenomenon can be estimated using multi-temporal remote sensing imagery data. This study aims to analyze the spatial dynamics of UHI in the urban region of West Java from 1998 to 2018. We only used remote sensing data from different datasets. Information of land-surface temperature is extracted from Landsat-5 TM and Landsat-8 OLI images using radiative transfer equation which validated using MODIS data in the same period. This study showed that UHI intensity in the urban region of West Java reach 5.11oC in 2018. For 20 years, the land-surface temperature increased to 4.44oC. The UHI distribution is concentrated in the central business district, industrial area, harbor, terminal, airport, and traffic jam zone. The UHI significantly increased in Depok and Cimahi, which are known as satellite cities for the surrounding megapolitan (Jakarta and Bandung Raya). This model has high validity result with a correlation value of 0.74. The UHI management is important to strengthen urban resilience in the environmental field through green open space, green belts, roof gardens, land use and land cover formal direction, and the use of high albedo materials to build construction.

“Human elephant conflict in changing land-use land-cover scenario in and adjoining region of Buxa tiger reserve, India”

The land use land cover changes (LULCC) in and around wildlife habitat are one of the major driving forces behind frequent human elephant conflict (HEC). The present study aims to determine the spatiotemporal LULC changes and its impact on Human – Elephant conflict in and adjoining Buxa Tiger Reserve (BTR). Landsat TM and OLI images of 1990, 2000, 2010, and 2019 were used and supervised classification using maximum likelihood classification algorithm applied to classify the LULC attributes. Each sign of elephant presence was recorded using GPS used for corridor mapping. The recorded sign of conflict was collected, used for settlement based human elephant conflict probability zone identification mapping by areal interpolation method. We found that (1990 - 2019) dense forest, surface water bodies, scrub land and agricultural fallow land decreased by 184.04 sq km, 21.94 sq km, 142.48 sq km, and 130.21 sq km respectively. On the other hand crop land, sparse vegetation, tea garden, open forest, degraded forest and built up areas increased by 119.74 sq km, 90.96 sq km, 87.05 sq km, 57.02 sq km, 19.07 sq km and 32.21 sq km respectively. The visual overlay method was performed between LULC map, and settlement based HEC probability map, therefore impact of spatiotemporal LULC change on HEC was determined. It finds out moderate to very high HE conflict settlement zone lies where nearby forest tract either degraded or large areas of open forest, scrub land replaced into crop fields and settlements. In addition, elephant sign along the broad gauge rail line were collected on foot, recorded using GPS used to prepared accident prone susceptibility map by Inverse Distance Weighting (IDW) interpolation technique to estimate where accidents were more likely to occur due to the concentration of elephant activity. It finds out high to very high accident prone susceptible zone lies in between Rajabhatkhawa station (st) – Damanpur st, Gorapara st- Rajabhatkhawa st, and railway line within Hamiltonganj range. The study wants to draw attention to the concerned authority and policy makers on that there is a need of long term land use planning to save BTR forest and its biodiversity as well as need for proper mitigate method for HE conflict for peaceful coexistences of both the species.