Post-classification Change Detection Technique Research Articles

Time series land use/land cover mapping and change detection to support policies on sustainable environmental and economic management

The upper Tekeze River Basin is facing challenges of widespread deforestation and natural vegetation cover degradation that could exacerbate the water scarcity, food insecurity and extreme poverty in the region. Using remote sensing and GIS, this study quantified the land use land cover change trend in the last three decades and analyzed the current land use / cover statues in the basin. A hybrid classification technique is applied to obtain better classification accuracy. Moreover, for automated cloud and cloud shadow detection the newly developed Mountainous Fmask is used. Using post classification change detection technique, seven major land use/cover classes were identified. These classes remained the dominant classes during the study period, showing marked changes in the area coverage within them. Based on the error matrix statistical indices, the classification accuracies of each class are found to be strong. The overall accuracy and the kappa coefficient for the 2021 map are 91% and 89%, respectively. The techniques used have contributed to improving the accuracy of the classification process and helped the classified images to practically match the ground truths. The analysis revealed settlement expansion by 570.31% in parallel with the expansion of farmland by 52.32% during the period 1991–2021. In contrast, the forestland decreased significantly, by 75.55%. The environmental degradation and unplanned use of land resources could have contributed to why the upper Tekeze basin is experiencing worsening poverty, water scarcity and food insecurity. Thus, land use/cover time series modeling is essential for various purposes, including land use planning and, managing natural resources. In this regards this study provides basic information for implementing sustainable environmental conservation strategies in the area. Furthermore, the applied methodologies may have practical applications in other similar areas.

Dynamics and Prediction of Land Use and Land Cover Changes Using Geospatial Techniques in Abelti Watershed, Omo Gibe River Basin, Ethiopia

Ethiopia is a growing country which is in need of scientific ground for land use planning and agricultural-based economy. Evaluation of land use/land cover (LULC) changes helps for proper scheduling and use of natural resources with safe administration in accordance with time and dynamic population growth of the country, specifically in the study area. One of the detailed and useful ways to develop land use evaluation and classification maps is the use of geospatial techniques such as remote sensing and geographic information systems (GIS). The main focus of this study is to evaluate the dynamics of land use and land cover (LULC) changes in the Abelti Watershed, Omo-Gibe River basin, Ethiopia. Maximum likelihood algorithm approach supervised classification method was used for identifying the LULC changes using satellite data to know LULC changes in the watershed. Quantifications of spatial and temporal dynamics of land use/cover changes were accomplished by using three satellite images of 2000, 2010, and 2017 and classifying them via a supervised classification algorithm by using Earth Resources and Development System (ERDAS) software and finally applying the postclassification change detection technique was performed by using ArcGIS 10.3. From the LULC analysis, the increase was observed in the agricultural area and settlement area from 2000 to 2017. On the other hand, shrub land followed a declining trend during the study period. However, forest and bare land followed variable trends during the study period in which forest declined from 2000 to 2010 but increased from 2010 to 2017 and bare land increased from 2000 to 2010 and declined from 2010 to 2017. Generally, the driving force behind this change was population growth, rapid urbanization, and deforestation which resulted in a wide range of environmental impacts, including degraded habitat quality in the watershed.

Effects of Monocropping on Land Cover Transitions in the Wet Evergreen Agro-Ecological Zone of Ghana

The wet evergreen forest ecological zone of Ghana is undergoing notable structural changes, although most maps erroneously depict the area to be an intact forest. Several hectares of plantation agriculture such as rubber, oil palm, and cocoa are replacing the natural vegetation and habitats, thereby threatening indigenous biodiversity. This study aimed to assess the effects of tree monocrop proliferation on landscape transitions between 1986 and 2020 in the Wassa East District of Ghana. The ISODATA clustering technique was used to produce land cover category maps from Landsat images of 1986, 2002, and 2020. A post-classification change detection technique resulted in transition matrices which were used for the computation of land cover transition intensities over 34 years. The results showed that the landscape was dominated by forests in 1986. Still, the forest continuously declined by 34% (1% annually) in 2020 while all other land cover types increased in both periods with cocoa covering 23% oil palm covering 14%, and rubber covering 2% of the land area. These conversions in the land area are important, especially since the closed forests usually transition to monocrops through the intermediate process: food crop farming. This information is essential for decision making on land development and biodiversity conservation.

Dynamics of Land Surface Temperature in Response to Land Use Land Cover Change in Phewa watershed, Kaski, Nepal

Geospatial tools play an important role in monitoring Land Use Land Cover Change (LULCC) and Land Surface Temperature (LST). This study assessed the extent of LULCC during 1990, 2007 and 2020 using temporal satellite imageries, and estimates LST during the study periods in relation to LULCC and explores various adaptation practices adopted in response to LST change in Phewa watershed. Landsat imageries were used for LULC classification and LST estimation. Purposive household survey (N=150), key informant survey (N=4), focus group discussion (N=4) and direct field observations were carried out to explore various adaptation practices. LULC maps was generated by using supervised classification followed by post classification change detection technique for LULCC analysis. Agricultural land and urban areas were found to have increased by 6.6% and 0.32% respectively while forest area, barren land and water bodies were found to have decreased by 1.88%, 4.41% and 0.63%, respectively, between 1990 and 2007. Forest area, urban areas and barren land had increased by 5.54%, 1.32% and 1.48%, respectively, while agricultural land and water bodies were observed to have decreased by 8.2% and 0.13%, respectively, between 2007 and 2020. The mean LST were 22.59° C, 22.81° C and 24.56° C in March-April and 14.65° C, 14.97° C and 15.27° C in January-December, respectively. Urban areas and water bodies exhibit highest and lowest mean values of LST, respectively, during study periods. LULCC due to urban growth and infrastructural developments had contributed to increase in LST. The highest rank to adaptation practices were for changes in cropping patterns and species, followed by agroforestry, use of improved seeds, shift to other income generation activities, use of more fertilizers, irrigation practices and growing vegetables in poly house which were statistically significant. This study provides scientific insights for policy makers and urban planners in improving urban planning and management.

Evaluation of spatiotemporal dynamics of land cover and land surface temperature using spectral indices and supervised classification: A case study of Jobai Beel Area, Bangladesh

This study aims to evaluate the spatiotemporal change of land cover (LC) and surface temperature of the Jobai Beel area, an exclusive agriculture zone, situated in the far-flung area of northwest Bangladesh using satellite data. Multi-temporal Landsat series of data from 1989 to 2020 and geospatial techniques have been employed to evaluate the LC change and land surface temperature (LST) variation. Different spectral indices such as NDVI, MNDWI, NDBal have been used to retrieve individual LC. Corresponding LST has also been extracted using the thermal bands. Supervised Classification and the post-classification change detection technique were employed to determine the temporal changes and validate the individual LC. The results were employed to assess the LST variation associated with LC changes. The results reveal that the area had undergone a drastic and inconsistent heterogeneous LC transformation during the study period. Water and vegetation areas have expanded at a rate of 0.24km2/year and 0.45km2/year respectively, while bare lands have shrunk at a rate of 0.70km2/year. In general, Bare land exhibits a significant positive correlation, when Vegetation areas show a significant negative correlation with LST. However, the correlation between water areas and LST was found statistically insignificant. Agriculture in the form of vegetation has been found the most dominating land cover character throughout the study period, which has been regulating the LST variation across the area.

Monitoring the Complex Dynamics of LULC Change of Tanguar Haor, a Ramsar Site in the Northeastern Part of Bangladesh

The bowl-shaped depressions between the natural levees of rivers, mostly found in the wetland of the north-eastern part of Bangladesh are locally known as Haor. This study has tried to find out the dynamics of land use/land cover (LULC) change of Tanguar Haor, a Ramsar Site of Bangladesh between 1989 and 2017. Satellite data from Landsat archive have been used to determine the LULC change and collected primary data through a questionnaire survey used to identify the factors and impacts of LULC change. Post-classification change detection techniques have been applied to evaluate the LULC change, and descriptive statistics are applied for qualitative analysis of primary data as well as other secondary data

Evaluating change detection techniques using remote sensing data: Case study New Administrative Capital Egypt

The main objective of this research is to evaluate change detection techniques to monitoring land-cover changes that occurred between 2016 and 2017 in the study area located in new administrative capital region in Cairo Governorate, Egypt. The Study area is 77.350 km2. Image data is two Satellite images covering the study area, both are by Landsat_8 OLI/TIRS satellite with a low spatial resolution 30 m * 30 m. Reference data is two Satellite images covering the study area, both are by Nanosat satellite with a high spatial resolution 3 m * 3 m. The Study area was classified into roads, sand, rocks, bare soil, routes and buildings using Maximum Likelihood Classifier. Three change detection techniques namely; post-classification, independent component analysis and principal component analysis were applied. Quantitative evaluations for the results of these techniques were performed to determine the most appropriate change detection technique which will provide the highest accuracy for identifying the nature and extent of land-cover changes in New capital. The results indicated that the post classification change detection technique provided the highest accuracy while the principal component analysis technique gave the least accuracy. And the results show that the post classification change detection technique is straightforward because it is direct and its accuracy is mainly dependent upon the accuracy of the initial classifications of the two images. In the other two change detection techniques, more processing stages are required such as transformation stage

Determining Long-Term Land Cover Dynamics in the South Baltic Coastal Zone from Historical Aerial Photographs

Coastal regions are dynamic environments that have been the main settlement destinations for human society development for centuries. Development by humans and environmental changes have resulted in intensive land cover transformation. However, detailed spatiotemporal analyses of such changes in the Polish Baltic coastal zone have not been given sufficient attention. The aim of the presented work is to fill this gap and, moreover, present a method for assessing indicators of changes in a coastal dune environment that could be an alternative for widely used morphological line indicators. To fulfill the main aim, spatial and temporal variations in the dune areas of the Pomeranian Bay coast (South Baltic Sea) were quantified using remote sensing data from the years 1938–2017, supervised classification, and a geographic information system post-classification change detection technique. Finally, a novel quantitative approach for coastal areas containing both sea and land surface sections was developed. The analysis revealed that for accumulative areas, a decrease in the land area occupied by water was typical, along with an increase in the surface area not covered by vegetation and a growth in the surface area occupied by vegetation. Furthermore, stabilized shores were subject to significant changes in tree cover area mainly at the expense of grass-covered terrains and simultaneous slight changes in the surface area occupied by water and the areas free of vegetation. The statistical analysis revealed six groups of characteristic shore evolutionary trends, of which three exhibited an erosive nature of changes. The methodology developed herein helps discover new possibilities for defining coastal zone dynamics and can be used as an alternative solution to methods only resorting to cross sections and line indicators. These results constitute an important step toward developing a predictive model of coastal land cover changes.

The Dynamics of Land Use Land Cover and its Driving Forces in Mekelle City Region, Ethiopia

The rationale of this study was to study the spatio-tempo LULC dynamics over the past 47 years and identifying the major drivers of these changes. It was conducted in Mekelle city region, northern Ethiopia which is highly susceptible to environmental degradation. This landscape-scale level study employs a combination of analysis of satellite imageries, information from field studies, document review, key informant interview and observation. Digital satellite images were processed, classified and analysed by ERDAS Imagine. Computations of the area changes in the land use categories was made using supervised classification by applying maximum likelihood classifier algorithm and finally post-classification change detection technique was undertaken using Arc GIS 10.5.1. For stastical analysis of variables spatial autocorrelation and structural equation model was used. During the study period between 1972 and 2019 about 60,705.56 hectares of the total landscape of the study area was converted from one LULC type to another. The findings show increase was observed in cultivated land, built-up area and bushes and shrubs. On the other hand, natural forest, water body and bare lands were dramatically declined. Among the driving factors; climate variability, population growth, DEM and slope were identified as the leading land use and land cover change drivers. Thus, spatial planners need to take these drivers into consideration and make sound decisions regarding changes in LULC during decision makings.

Drivers and Dynamics of Land Use Land Cover in Phewa Watershed, Kaski, Nepal


 
 
 Geospatial tools play an important role in monitoring Land Use Land Cover (LULC) dynamics. This study assessed the extent of LULC changes during 2003, 2010 and 2018 using temporal satellite imageries, computed the rate of change in area of Phewa Lake and explored the drivers of LULC change and lake area change in Phewa watershed. It used Landsat Imageries for 2003, 2010 and 2018 and carried out purposive household survey (N=60), key informant survey (N=5), focus group discussion (N=4) and direct field observation to explore the drivers of LULC change and lake area change. It generated LULC maps by using supervised classification and computed LULC change by applying post classification change detection technique. On screen digitization was done to find the area of Phewa Lake during 2010 and 2018. Agricultural land and urban areas were found to have increased by 11.63% and 1.46% respectively while forest area, barren land and water bodies were found to have decreased by 9.21%, 3.56% and 0.5% respectively between 2003 and 2010. Forest area, urban areas and barren land were found to have increased by 5.9%, 3.28% and 5.02% respectively while agricultural landand water bodies were observed to have decreased by 7.83% and 0.16% respectively between 2010 and 2018. During 2010-2018, rate of change in lake area was found to have decreased by 0.61% with periodic annual decrement by 2.59 ha. The drivers responsible for LULC change were alternative form of energy, community forestry, promotion of private forestry, migration for foreign employment, inadequate market price of agricultural products, road construction, soil erosion and population pressure. Lake area was found to have decreased due to sedimentation, encroachment and road construction. Further study is important to know the exact contributions of these drivers of LULC change and lake area change for the sustainability of Phewa watershed.
 
 

Detection of spatio-temporal changes of vegetation in coastal areas subjected to soil erosion issue

Coastal soil erosion can be recognized as the most alarming environmental issue since, causing shoreline retreat, reduces the area available for plant habitat survival, highly influencing their health status, and, consequently, limiting their ability in beach front properties protection. A deep knowledge of vegetation changes is required to identify the proper strategy to be adopted to face soil erosion problems in coastal areas. Therefore, the current paper is aimed to quantitatively examine the spatio-temporal changes suffered by the vegetation in the coastline of Siponto in Apulia Region (Southern Italy) covering a time period of about forty years. LANDSAT images from 1975, 2006, 2011 and 2018 were collected, atmospherically corrected and, finally, processed to generate binary classification maps of vegetation by applying the Composite Vegetation Index, a novel index based on the interpolation of Red, Green and Near-Infrared bands, suitable for catching both cellular and metabolic features of vegetation. Then, the generated binary classification maps were compared using the Vegetation Index Differencing technique, a post-classification change detection technique. The results showed an increase in vegetation extension cover and density overall the entire examined period. That phenomenon appeared more and more prominent between 1975 and 2006, where an increment of vegetated areas extension of about 88% were registered. Combining of the novel vegetation index, developed ad-hoc in the current research, and Vegetation Index Differencing approach shows promising results in vegetation classification and comparison over the time. Indeed, the method allows the fast vegetation extraction, great processing time saver. Nevertheless, spatial resolution of Landsat Images limits the classification of small and low-density vegetated areas. Therefore, future work should plan to test the proposed approach at a more detailed scale.

Land Use Change and Its Impacts on Land Surface Temperature in Galle City, Sri Lanka

This study investigated the spatiotemporal changes of land use land cover (LULC) and its impact on land surface temperature (LST) in the Galle Municipal Council area (GMCA), Sri Lanka. The same was achieved by employing the multi-temporal satellite data and geo-spatial techniques between 1996 and 2019. The post-classification change detection technique was employed to determine the temporal changes of LULC, and its results were utilized to assess the LST variation over the LULC changes. The results revealed that the area had undergone a drastic LULC transformation. It experienced 38% increase in the built-up area, while vegetation and non-built-up area declined by 26% and 12%, respectively. Rapid urban growth has had a significant effect on the LST, and the built-up area had the highest mean LST of 22.7 °C, 23.2 °C, and 26.3 °C for 1996, 2009, and 2019, correspondingly. The mean LST of the GMCA was 19.2 °C in 1996, 20.1 °C in 2009, and 22.4 °C in 2019. The land area with a temperature above 24 °C increased by 9% and 12% in 2009 and 2019, respectively. The highest LST variation (5.5 °C) was observed from newly added built-up area, which was also transferred from vegetation land. Meanwhile, the lowest mean LST difference was observed from newly added vegetation land. The results show that the mean annual LST increased by 3.2 °C in the last 22 years in GMCA. This study identified significant challenges for urban planners and respective administrative bodies to mitigate and control the negative effect of LST for the long livability of Galle City.

Identification of land degradation hotspots in semiarid region of Anantapur district, Southern India, using geospatial modeling approaches

Land degradation and desertification has been graded as a foremost environmental dispute for the coming eras. Thus, the surveillance and early detection of degradation is a major objectives for a number of scientific and policy organizations, with RS and GIS techniques being a candidate choice for the development of monitoring systems. This study was carried out as an attempt to develop a new modeling approach for identification of desertified hotspots based on normalized difference vegetation index (NDVI) in semiarid region of Anantapur district, Southern India. A supervised classification was carried out on the six reflective bands for the past 28 years, i.e., from 1990 to 2018 individually with the aid of ground truth data. Ground truth information collected during field data and land cover map of 2005–2006 was used to assess the accuracy of the classification results. Using ancillary data, visual interpretation and expert knowledge of the area through GIS further refined the classification results. Post-classification change detection technique was used to produce change image through cross-tabulation. Changes among different NDVI classes such as water bodies, agricultural fields, vegetation, degraded lands, desertified lands were assessed. During the study period, a very severe land degradation has taken place as a results reveals that 31.93% of the land in this region is facing the land degradation and desertification conditions; in that 17.34% of the land face the severe land degradation and 14.59% of the land have been desertified. These changes in land cover led to vegetation degradation and water logging in part of the study area.

Land Cover Mapping and Change Analysis in Tropical Humid-highlands: Case of Ndakaini Water Reservoir in Central Kenya

We successfully used optical remote sensing approach to test the skills of post-classification change detection technique as well as techniques of circumventing the challenges of cloud/cloud-shadow contamination and of working in a data-scarce environment in tropical humid highlands. The aim was to generate an accurate estimate of current land cover distribution map and analyze land-cover change around Ndakaini area in Kenya. Landsat imageries (TM and ETM + ) acquired between 1985 and 2011 and corresponding to the study area was selected. Employing bands 3 and 4 of respective Landsat images, thresholding techniques, Boolean and masking operations were implemented in detecting cloud/cloud-shadows and subsequent removal and filling of gaps. In absence of other historical ancillary data about land cover types, a total of 278 points across the study area were captured from Google Earth and used to evaluate the accuracy of each of the generated land cover maps. From the results, cloud/cloud-shadow gaps were reduced immensely (e.g. 90% for the 1985 image and 82% for the 2011 image). With regard to quality of classification outputs, the respective land cover/land-use maps of 2000, 2005 and 2010 anniversaries had fairly high level of overall accuracy (64%, 79% and 68% respectively) and Kappa statistic (0.47, 0.69 and 0.53 respectively) while classification outputs of 1985 and 1995 yielded slightly lower overall accuracy (60%) and Kappa statistic (0.42). Post-classification change involving three land cover classes, tea plantation, forest/woodlot and annual crop fields denoted as others were successfully determined and conclusions based on trend analysis drawn. The satisfactory results of this study imply the usefulness of post-classification change detection method in generating information about land cover dynamics in tropical humid highlands especially when coupled with robust techniques that adequately circumvent the cloud and cloud-shadow problem and scarcity of ancillary data often common in these areas. Keywords : Post-classification change detection, thresholding and Boolean techniques, landcover change, tropical humid-highlands DOI: 10.7176/JNSR/10-8-03 Publication date: April 30 th 2020

DYNAMICS OF SPATIO-TEMPORAL URBAN EXPANSION IN SOUTH WEST DELHI REGION: A GEO-SPATIAL APPROACH FOR URBAN DISASTER MANAGEMENT

Abstract. Constantly increasing population and up-scaling economic growth has certainly contributed to fast-paced urban expansion, but simultaneously, as a result, has developed immense pressure on our natural resources. Among other unfavorable consequences, this has led to significant changes in the land use and land cover patterns in megacities all across the globe. As the impact of uncontrolled and unplanned development continues to alter life patterns, it has become imperative to study severe problems resulting from rapid development and leading to environmental pollution, disruptions in ecological structures, ever increasing pressure on natural resources and recurring urban disasters This paper presents an approach to address these challenges using geospatial data to study the land use and land cover change and the patterns and processes of urban growth. Spatio-temporal changes in land-use/land-cover were assessed over the years using multi-date high resolution satellite data. The land use classification was conducted using visual image interpretation technique wherein, study area was categorized into five different classes based on NRSC classification system namely agricultural, built-up, urban green (forest), and fallow land and water bodies. Post-classification change detection technique was used for the assessment of land-cover change and transition matrices of urban expansion were developed to quantify the changes. The results show that the city has been expanding majorly in its borders, where land masses have been converted from agriculture based rural areas to urban structures. An increase in the built-up category was observed with the transformation of agricultural and marginal land with an approximate change of 8.62% in the peri-urban areas. Urban areas are becoming more densely populated and open barren lands are converted into urban areas due to over population and migration from the rural areas of Delhi and thus increasing threat towards urban disaster. Conservation and sustainable management of various natural resources is recommended in order to minimize the impact of potential urban disasters.

Study on land-use and land-cover change dynamics in Eastern Arunachal Pradesh, N.E. India using remote sensing and GIS

The current study describes the land use and land cover dynamics in Eastern Arunachal Pradesh from 1985 to 2005 using remote sensing and Geographical Information System (GIS). Landsat-MSS, IRS-LISS-I and LISS-III data for the period 1985, 1995 and 2005 were used to prepare the land use/land cover (LULC) map at 1:250K for different periods. Post-classification change detection technique for quantifying the changes for twelve major land use and land cover types was analyzed. The study signifies that the region experiences expansion in crop land and built-up area and decline in forest area. Crop land and built-up area has increased by 665.41 km2 and 16.72 km2, respectively, from 1985 to 2005. On the other hand, forest area has declined by 699.37 km2 during the period. The study showed that topography and increasing population play an important role in shaping the LULC pattern. The study further revealed the importance of satellite remote sensing and GIS as an effective approach for analyzing the direction, rate and spatial pattern of land use dynamics. It is believed that this type of study will help to contribute towards sustainable land-use planning and management in this part of the North East India in the near future.

Evaluation of land use/land cover changes due to gold mining activities from 1987 to 2017 using landsat imagery, East Cameroon

Gold mining activities began around 1934 at the artisanal stage and became semi-mechanized around 2004 in eastern Cameroon, with the emergence of exploration and mining companies in the region. This paper aims at monitoring Land Use and Land Cover (LULC) changes between 1987 and 2017 among Betare-Oya, Ngoura and Batouri Districts which are witnessing extensive gold mining activities, assessing the dynamics between LULC types and understanding the anthropogenic impact of gold mining activities during this period. A series of Landsat images acquired in 1987, 2000, and 2016/2017 were used to examine LULC change trajectories at per-pixel scale with the post-classification change detection techniques based on the matrix of changes. A supervised classification by the maximum likelihood algorithm composed of five classes—Bare land, Settlements, Water bodies, Vegetation and Mine activities, was designed for this study, in order to classify Landsat images into thematic maps. This research revealed spatio-temporal change patterns, various composition and rates among the three study areas. Also, it shows the strong appearance and emergence of mining activities between 2000 and 2017 are coupled with increase in settlement surfaces and major changes in environment in the study areas. The LULC change analysis over time for the study areas have provide the current change trends. This study stresses the usefulness of Landsat TM/ETM + and Landsat Data Continuity Mission (LDCM) data and highlights the data processing methods for long-term monitoring of artisanal mining activities impacts on the environment.

Assessment of land degradation and implications on agricultural land in Qalyubia Governorate, Egypt

BackgroundLand degradation considers as a phenomena or more that decrease the current and/or the potential soil capability to produce goods. It signifies a regression from a higher to lower state, owing to descend in land capability, productivity, and decline of biodiversity. This study is an attempt to address the complexity of land degradation issue, particularly in the targeted farming irrigated fields, Qalyubia Governorate, Egypt. It aims to assess and observe degradation hazard through satellite image analyze, model trends of degradation, and generate a change detection hazard map for the investigated area.ResultsThe maximum likelihood supervised classification tool and post classification change detection technique were implemented for monitoring changes in land qualities. Land degradation indicator data layers were summarized into the metrological data, ground truth, vegetation cover, and the applied land management practices. The Modified Global Assessment of Soil Degradation GLASOD model was adapted to model land degradation, specify its action in term of degradation degree, relative extent, severity level, and causative factors. Land degradation processes were evaluated in the delineated mapping units. The result indicated that the studied areas are considered as quite unstable in term of ecosystem due to active degradation resulting from aridity phenomena, soil properties, and improper farm management.ConclusionThe most active land degradation processes are assessment of salinization, waterlogging, alkalization, and soil compaction.

Using Remote Sensing Technics for Land Use Land Cover Changes Analyses from 1950s to 2000s in Somone Tropical Coastal Lagoon, Senegal

In many developing countries, some natural areas are faced with gaps in appropriate map coverage mainly on land use and land cover (LULC) changes. This situation makes it difficult to plan and implement natural environmental protection and natural resource management programs. Remote sensing and geographic information systems (GIS) are excellent tools for mapping LULC changes. This study investigated LULC changes in ‘Somone’ coastal lagoon in Senegal using multisource remote sensed data. Data sets included aerial photographs recorded in March 1954, and February 1978, as well as satellite images recorded in February 2003 and April 2016. All images were geometrically corrected and segmented. Photos and/or images interpretations were made with the aid of computer and post-classification change detection technique was applied to classify multisource data and to map changes. Stratified sampling was used to assess all classification results. The accuracies of image classifications averaged 65% (1954), 62% (1978), 79% (2003) and 88% (2016). The post-classification analysis resulted in the largest overall accuracy of 66, 72.7, 72.4 and 80.6% for the 1954–1978, 1978-2003 and 2003–2016 image pairs, respectively. Results indicated an increase in Settlements, from 0.29% in 1954 to 9.21% in 2016, the expansion of the Sabkha, from 5.29% in 1954 to 18.48% in 2016. The mangrove forest has experimented a reduction between 1954 and 1978 (from 4.07% to 0.56%) and a regeneration (linked to the protection and preservation policies within the protected area) from the year 2003 to 2016 (from 1.44% to 2.65%). However, the forest areas were greatly reduced (from 51.06% in 1954 to 10.86% in 2016) and replaced by Settlements (urbanization) as well as Croplands.

Spatial and temporal trends of forest cover as a response to policy interventions in the district Chitral, Pakistan

Forest protection policies potentially reduce deforestation and forest degradation. Using satellite imagery, a supervised classification method and a post-classification change detection technique, this study quantifies forest cover changes prior (1973–1993) and after (1993–2015) a nation-wide logging ban in 1993. Using logistic model, we also assess and compare the relative importance and patterns of drivers of deforestation and forest degradation between the two periods. We show that despite a complete ban on green felling, total forest in the study area has decreased from 89,938 ha in 1973 to 68,904 ha in 2015. Proportionally more forest (13,636 ha) disappears during the post-logging ban as compare to the pre-logging ban period (7398 ha) with annual conversion rate of 0.43% and 0.82% respectively. In particular, forest degradation has accelerated in the region after the imposition of the logging ban policy. However, sparse forest conversion appears to slightly slowdown after the imposition of the logging ban. The logistic analysis shows that most of the deforestation in the pre-ban period occurred in the high elevation range (3000 to 4000 m), while during the post-ban period more can be observed in the low elevation range (2500 and 3500 m). Opposite to deforestation, forest degradation shifted from low elevation range (2500 to 3000 m) during the pre-ban period to high elevation range (3000 to 3500 m) of mature and old growth stands during the post-ban period. Distances to political boundary and forest edge were the other significant variables that behaved differently between the two periods. We found that factors influencing deforestation and forest degradation were not only time and space specific but also varied with the policy setting.It is argued that forest depletion continued even after the ban because of high demand for firewood and timber. However, the changes in the behavior of explanatory variables over time might be attributed to the influence of policy and a general decrease in resource availability in the region.