Assessment and monitoring of long-term forest cover changes (1920–2013) in Western Ghats biodiversity hotspot

Western Ghats are considered as one of the global biodiversity hotspots. There is an information gap on conservation status of the biodiversity hotspots. This study has quantified estimates of deforestation in the Western Ghats over a period of past nine decades. The classified forest cover maps for 1920, 1975, 1985, 1995, 2005 and 2013 indicates 95,446 (73.1%), 63,123 (48.4%), 62,286 (47.7%), 61,551 (47.2%), 61,511 (47.1%) and 61,511 km² (47.1%) of the forest area, respectively. The rates of deforestation have been analyzed in different time phases, i.e., 1920–1975, 1975–1985, 1985–1995, 1995–2005 and 2005–2013. The grid cells of 1 km² have been generated for time series analysis and describing spatial changes in forests. The net rate of deforestation was found to be 0.75 during 1920–1975, 0.13 during 1975–1985, 0.12 during 1985–1995 and 0.01 during 1995–2005. Overall forest loss in Western Ghats was estimated as 33,579 km² (35.3% of the total forest) from 1920’s to 2013. Land use change analysis indicates highest transformation of forest to plantations, followed by agriculture and degradation to scrub. The dominant forest type is tropical semi-evergreen which comprises 21,678 km² (35.2%) of the total forest area of Western Ghats, followed by wet evergreen forest (30.6%), moist deciduous forest (24.8%) and dry deciduous forest (8.1%) in 2013. Even though it has the highest population density among the hotspots, there is no quantifiable net rate of deforestation from 2005 to 2013 which indicates increased measures of conservation.     

Predictive modelling of the spatial pattern of past and future forest cover changes in India

This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km², which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880–2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multi-layer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km² of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.     

Assessing the impact of master plan on the dynamicity and resilience of forest cover change: a study on Adina Deer Park (Forest), West Bengal,India

Assessing land use and land cover change as well as monitoring dynamic nature of forest cover plays an important role for the management of biodiversity and its native ecosystem. The present paper mainly focuses on the impact of master plan on dynamicity and resilience of Adina Deer Park since 1990–2017 using multi-temporal LANDSAT data. Supervised pixel and object oriented techniques and change detection method have been used for both pre (1990–2015) and post (2015–2017) master plan. Landscape metrics have also been analyzed to detect forest fragmentation applying fragstats software. Overall accuracies for both classified images of 2010 and 2017 were 81.25% and 84.12%. The forest cover changes map exhibits that dense forest and water bodies increased from 15.68 to 39.64 ha and 8.12 to 9.17 ha whereas sparse forest and bare land declined from 31.02 to 22.08 ha and 22.59 to 9.11 ha respectively. The result showed a significant increase in dense forestand sparse forest with annual growth rate of 20% and 15.97% after the implementation of master plan in 2015. This study may assist the planners and policy makers to adopt the environmental and ecological oriented strategies for sustainable management of ecosystems and biodiversity.

     

Forecasting areas vulnerable to forest conversion using artificial neural network and GIS (case study: northern Ilam forests,Ilam province,Iran)

Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the Zagros region. Yet, areas vulnerable to forest conversion are unknown. This study aims to predict the spatial distribution of deforestation in western Iran. Landsat images dated 1988, 2001, and 2007 are classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. Meanwhile, in order to examine deforestation factors’ investigation, deforestation maps with physiographic and human spatial variables are entered into the model. Areas vulnerable to forest changes in the Zagros forest region are predicted by a multilayer perceptron neural network (MLPNN) with a Markov chain model. The results show that about 19,294 ha forest areas are deforested in the last 19 years. The predictive performance of the model appears successful, which is validated using the actual land cover map of the same year from Landsat data. The validated map is found to be 94 % accurate. The validation is also tested using the relative operating characteristic approach which yielded a value of 0.96. The model is then further extended to predict forest cover losses for 2020. The MLPNN approach was found to have a great potential to predict land use/land cover changes because it permits developing complex, nonlinear models.     

Gully development in eastern Romania: a case study from Falciu Hills

The gullied systems from the Falciu Hills within the Chioara catchment (2997 ha) consist of both main types of gullies, discontinuous and large continuous ones along valley bottoms, and lots of ephemeral gullies. Several methods have been used to measure and estimate gully characteristics. Then, the gully development stages, the effect of the natural conditions, and especially the impact of land management on gullying in the Falciu Hills over the last two centuries have been defined. In addition, the role of gully erosion in triggering landslides has also been studied. Two main periods have been distinguished (until 1960 and 1961–2012) for assessing major characteristics of land degradation. The results show that total gully area in the Chioara catchment is 66.4 ha excepting for the ephemeral gullies, and areas occupied by gullies from the five study sub-catchments (2334 ha) account for two-thirds. Total length of the main gully network in the entire catchment is 33.2 km from which the five sub-catchments account for 71 %. The mean gully density of 1.11 km km⁻² supports the evidence that here gullying is the major environmental threat. Half of the gully areal growth and three-quarters of the new landslide area occurred over the 1961–2012 period. Delayed deforestation peaking during 1830–1930 and land conversion to arable use resulted in severe soil erosion, high aggradation along the non-gullied valley bottoms, and severe gullying. The average gully head retreat rate over the last two centuries from four trunk continuous gullies is 14 m year⁻¹, and the sediment yield from gullying only accounted for 54–69 % of the sediment mass produced by water erosion. The evolution of gullies is linked to major land-use changes in the study area. Despite a decreasing tendency of gullying and catchment area over the last half century, gullying still remains problematically high in East Romania.

     

Spatiotemporal dynamics of land cover and their impacts on potential dust source regions in the Tarim Basin,NW China

Human-driven dynamics of land cover types in the Tarim Basin are able to affect potential dust source regions and provide particles for dust storms. Analyses about dynamics of potential dust source regions are useful for understanding the effects of human activities on the fragile ecosystem in the extremely arid zone and also provide scientific evidence for the rational land development in the future. This paper therefore selected the Tarim Basin, NW China, as a representative study area to reveal spatiotemporal dynamics of land cover and their impacts on potential dust source regions. The results showed that farmland, desert and forest increased by 28.63, 0.64 and 29.27%, while grassland decreased by 10.29% during 1990–2010. The largest reclamation, grassland loss and desertification were 639.17 × 10³, 2350.42 × 10³ and 1605.86 × 10³ ha during 1995–2000. The relationship between reclamation and grassland loss was a positive correlation, while a highly positive correlation was 0.993 between the desertification and grassland loss at different stages. The most serious dust source region was the desertification during 1990–2010 (1614.58 thousand ha), and the serious region was stable desert (40,631.21 thousand ha). The area of the medium and low dust source region was 499.08 × 10³ and 2667.27 × 10³ ha. Dramatic reclamation resulted in the desertification by destroying natural vegetation and breaking the balance of water allocation in various regions.     

Investigating possible links between the North Atlantic Oscillation and rainfall variability in Marrakech (Morocco)

Eighty-two-year rainfall time series have been studied together with climatic patterns of NAO using classical statistical methods. Then, the wavelet approach has been applied to show annual (1 year (1y)) and inter-annual (2–4 years (2-4y), 5–8 years (5-8y), and 8–16 years (8-16y)) modes distributed following four major discontinuities: 1945, 1960, 1975, and 1995. The 1y, 2-4y, and 5-8y powers show high energy during the wet period 1922–1930 and a low one in 1928–1938. After 1945, the annual mode highlights a high energy while the inter-annual modes present low energy. Between 1975 and 1995, powerful modes of 1 and 2–4 years are identified with low power of 5-8y and 8-16y modes. Since 1995, the low power of 5–8y decreases, while the 8-16y mode emphasizes a high variability. The coherence between NAO and Marrakech precipitation is strongly defined for low frequencies with a total contribution of 75 %. This coherence is in phase in the beginning and presents out phase signs since 1945. The change of phase can be associated to a decreasing of coherence especially around 1990. This finding is useful to understand the relationship between the hydrological variability and NAO climate patterns in the southern side of the Mediterranean Sea.     

Land cover change dynamics mapping and predictions using EO data and a GIS-cellular automata model: the case of Al-Baha region,Kingdom of Saudi Arabia

The present study designed to monitor and predict land cover change (LCC) in addition to characterizing LCC and its dynamics over Al-Baha region, Kingdom of Saudi Arabia, by utilizing remote sensing and GIS-cellular automata model (Markov-CA). Moreover, to determine the effect of rainwater storage reservoirs as a driver to the expansion of irrigated cropland. Eight Landsat 5/7 TM/ETM images from 1975 to 2010 were analyzed and ultimately utilized in categorizing LC. The LC maps classified into four main classes: bare soil, sparsely vegetated, forest and shrub land, and irrigated cropland. The quantification of LCC for the analyzed categories showed that bare soil and sparsely vegetated was the largest classes throughout the study period, followed by forest, shrubland, and irrigated cropland. The processes of LCC in the study area were not constant, and varied from one class to another. There were two stages in bare soil change, an increase stage (1975–1995) and decline stage (1995–2010), and the construction of 25 rainwater-harvesting dams in the region was the turning point in bare soil change. The greatest increase was observed in irrigated cropland after 1995 in the expense of the other three categories as an effect of extensive rainwater harvesting practices. Losses were evident in forest and shrubland and sparsely vegetated land during the first stage (1975–1995) with 5.4 and 25.6 % of total area in 1995, while in 1975, they covered more than 13.8 and 32.7 % of total area. During the second stage (1995–2010), forest and shrubland witnessed a significant increase from 1569.17 km² in 1975 to 1840.87 km² in 2010. Irrigated cropland underwent the greatest growth (from 422.766 km² in 1975 to 1819.931 km² in 2010) during the entire study period, and this agriculture expansion reached its zenith in the 2000s. Markov-CA simulation in 2050 predicts a continuing upward trend in irrigated cropland and forest and shrubland areas, as well as a downward trend in bare soil and sparsely vegetated areas; the spatial distribution prediction indicates that irrigated cropland will expand around reservoirs and the mountain areas. The validation result showed that the model successfully identified the state of land cover in 2010 with 97 % agreement between the actual and projected cover. The output of this study would be useful for decision makers and LC/land use planners in Saudi Arabia and similar arid regions.     

Analysing land and vegetation cover dynamics during last three decades in Katerniaghat wildlife sanctuary,India

The change in the tropical forests could be clearly linked to the expansion of the human population and economies. An understanding of the anthropogenic forcing plays an important role in analyzing the impacts of climate change and the fate of tropical forests in the present and future scenario. In the present study, we analyze the impact of natural and anthropogenic factors in forest dynamics in Katerniaghat wildlife sanctuary situated along the Indo-Nepal border in Uttar Pradesh state, India. The study site is under tremendous pressure due to anthropogenic factors from surrounding areas since last three decades. The vegetation cover of the sanctuary primarily comprised of Shorea robusta forests, Tectona grandis plantation, and mixed deciduous forest; while the land cover comprised of agriculture, barren land, and water bodies. The classification accuracy was 83.5%, 91.5%, and 95.2% with MSS, IKONOS, and Quickbird datasets, respectively. Shorea robusta forests showed an increase of 16 km²; while Tectona grandis increased by 63.01 km² during 1975–2010. The spatial heterogeneity in these tropical vegetation classes surrounded by the human dominated agricultural lands could not be addressed using Landsat MSS data due to coarse spatial resolution; whereas the IKONOS and Quickbird satellite datasets proved to advantageous, thus being able to precisely address the variations within the vegetation classes as well as in the land cover classes and along the edge areas. Massive deforestation during 1970s along the adjoining international boundary with Nepal has led to destruction of the wildlife corridor and has exposed the wildlife sanctuary to human interference like grazing and poaching. Higher rates of forest dynamics during the 25-year period indicate the vulnerability of the ecosystem to the natural and anthropogenic disturbances in the proximity of the sanctuary.     

Assessment of deforestation,biodiversity loss and the associated factors: case study of Ijesa-Ekiti region of Southwestern Nigeria

Deforestation is driven by a variety of factors, and has resulted in land use changes that threaten biodiversity, water and energy resources. However, lack of reliable data and survey information in Nigeria has made the estimation of the effect of deforestation difficult to establish. Consequently, the extent and rate of deforestation are less well known. The study therefore, examined and analyzed the spatial and temporal patterns of deforestation over the period of 25 years (1978–2003); measured the rates, trends and explained the factors that determined deforestation in Ijesa-Ekiti region of southwestern Nigeria. The major sources of data for the study were satellites images. These were Landsat MSS 1978, with spatial resolution of 80 m, SPOT XS 1986, SPOT XS 1994, with 20 m spatial resolution and NigeriaSat_1 2003, with 32 m spatial resolution. To make them comparable, they were georeferenced to the same coordinates system, filtered, resampled and enhanced for visualization in a GIS environment. Furthermore, Ilesa, Ijebu-Ijesa, Efon-Alaaye, Iloko-Ijesa, Erin-Oke and Erin-Ijesa were identified and selected for ground truthing to validate the tonal values recorded in the images with the features on the ground. The result of ground truthing was combined with visual image interpretation as training sites for supervised classification. Focus Group Discussions were held with people who had lived in the area for over 20 years as a means of eliciting factors of deforestation and the effects on forest biodiversity. The results indicated forest loss of 53,469.23 ha over the period of 25 years at an annual deforestation rate of 7.21, 2.47, and 5.40% per year for 1978–1986, 1986–1994 and 1994–2003, respectively. FGDs with various categories of people in the bigger towns confirmed deforestation in the area and were due to illegal lumbering, intensive agricultural practices and growth of settlements resulted from increase in human population. FGDs also revealed extinction of many forest species in their communities. In conclusion, the study advanced our understanding on techniques of analyzing deforestation using geo-spatial technology. It also generated a synthesis of information on the rates of deforestation and its driving forces, which are a complex mix of anthropogenic factors, the chief of which has been the conversion of forest resources to agricultural land use.     

Deforestation in the Buea-Limbe and Bertoua regions in southern Cameroon (1984–2000): modernization,world-systems,and neo-Malthusian outlook

This paper characterizes changes in land use and forest cover in southern Cameroon. In doing so, we use remotely sensed data to define the rates and area of forest loss and cover change in two reference areas over a period of 16 years (from 1984 to 2000)—the Buea-Limbe area in the southwest province and the Bertoua area in the east province. We relate socio-economic data of these study areas with results from the empirical spatial analysis to explain causes of deforestation. A second set of explanations, which we call the theoretical perspective, attributes deforestation in southern Cameroon to the intricacies of modernization, world-systems, and neo-Malthusian theories.     

Land-use change versus natural controls on stream water chemistry in the Subandean Amazon,Peru

This study evaluates the effects of deforestation and land-use change, as compared to natural controls, on stream water chemistry in the Subandean Amazon. Dissolved major and trace elements were determined near the stream outlet of 48 independent watersheds with varying morphology, bed rock composition and intactness of forest cover (pristine to highly exploited). Geomorphological characteristics were derived from a digital elevation model, geological formations from digitalized maps and forest cover from digital classification of SPOT satellite images. Partial least square regression and multiple linear regression showed that watershed average elevation, which ranged between 396 and 1649 m, was the strongest control on stream water chemistry, explaining >70% of the variation in K and a considerable part also for Mn, U, Mg and HCO₃ with near exponential concentration increases down the altitude gradient. Forest cover, which ranged between 7% and 99%, correlated strongly with average elevation (Spearman correlation coefficient, r_s = 0.8), but had no statistically significant impact on stream solute concentrations. Thus, in the studied Subandean region, watershed scale deforestation has not resulted in measurable impacts on stream water chemistry which is dominated by the spatial variation in natural controls.     

Application of remote sensing in monitoring environmental change in ore area-Take Panzhihua V-Ti magnetite as an example

Remote sensing is an effective method to monitor environmental change. To study the influence of mining and mine dump on the environment of mine, remote sensing is used to monitor the change of land use and vegetation in the ore area, and to forecast the ecological environment by considering the mining speed. Remote sensing images at different periods were collected in the study, which include MSS images in 1975, TM images in 1990, 1995, 2000 and 2005, and SPOT-5 images in 2002. At the same time, relief map and geological map of the ore area were also collected as assistant data for remote sensing interpretation. After color synthesis, enhancing treatment and geometrical revision, the interpreted dynamic information on remote sensing images was edited with ARC/INFO software, to build a dynamic monitoring database for land use in the ore area from 1975 to 2005 and a database for land use in 1975, 1990, 1995, 2000, 2002 and 2005. Through interpretation, it is found that, after 30 years of mining activities, the vegetation area in the Panzhihua mining area was reduced from 9352.6 ha in 1975 to 8195.6 ha in 2005, and its grassland area from 1053 ha in 1975 to 795.5 ha in 2002. After that, the grassland area increased to 869.1 ha in 2005 because of re-cultivation of some mine dumps.     

Large variations of δ13C values in stalagmites from southeastern China during historical times: implications for anthropogenic deforestation

Variations in speleothem δ¹³C values can reflect changes in overlying surface vegetation, which, over historical time scales, may represent the influence of human activities. Here, we examined δ¹³C variations in two stalagmites growing for the last 2200 years in Shennong Cave, Jiangxi Province, SE China. The two δ¹³C records corroborate well one another and show a prominent 6‰ enrichment of the δ¹³C values from AD 700 to 1100. The isotopic equilibrium for modern calcite and negative correlation between δ¹⁸O and δ¹³C values along the growth axis suggest that the influences of kinetic fractionation are negligible. Varied correlations between Mg/Ca and Sr/Ca ratios and divergent changes between δ¹³C values and Mg/Ca and Sr/Ca ratios from AD 700 to 1100 reveal that the prior calcite precipitation (PCP) and water–rock interaction did not dominate the increase of δ¹³C values. It is plausible that the obvious δ¹³C variation was largely influenced by the changes in vegetation cover overlying the cave. Our δ¹³C results, together with the records of climate and human activity from historical documentary records, suggest that: (i) prior to AD 700, small fluctuations in relatively light δ¹³C values reflect the presence of lush forest coverage above the cave, which was minimally disturbed by human activities; (ii) during AD 700–1100, the drastic increase in δ¹³C values indicates persistent and massive deforestation associated with large‐scale immigration into northern Jiangxi after the Rebellion of An & Shi (AD 755–763) in the Tang Dynasty and the subsequent development of agriculture and economic activity; and (iii) since AD 1100, fluctuations in relatively high δ¹³C values suggest that local vegetation during the last millennium has been sparse. Since the Rebellion of An & Shi, southeastern China was progressively developed, coincident with deforestation and vegetation deterioration caused by human disturbance in the form of deforestation and cultivation.     

Dynamics of land use/cover changes and the analysis of landscape fragmentation in Dhaka Metropolitan, Bangladesh

Rapid urban expansion due to large scale land use/cover change, particularly in developing countries becomes a matter of concern since urbanization drives environmental change at multiple scales. Dhaka, the capital of Bangladesh, has been experienced break-neck urban growth in the last few decades that resulted many adverse impacts on the environment. This paper was an attempt to document spatio-temporal pattern of land use/cover changes, and to quantify the landscape structures in Dhaka Metropolitan of Bangladesh. Using multi-temporal remotely sensed data with GIS, dynamics of land use/cover changes was evaluated and a transition matrix was computed to understand the rate and pattern of land use/cover change. Derived land use statistics subsequently integrated with landscape metrics to determine the impact of land use change on landscape fragmentation. Significant changes in land use/cover were noticed in Dhaka over the study period, 1975–2005. Rapid urbanization was manifested by a large reduction of agricultural land since urban built-up area increased from 5,500?ha in 1975 to 20,549?ha in 2005. At the same time, cultivated land decreased from 12,040 to 6,236?ha in the same period. Likewise, wetland and vegetation cover reduced to about 6,027 and 2,812?ha, respectively. Consequently, sharp changes in landscape pattern and composition were observed. The landscape became highly fragmented as a result of rapid increase in the built-up areas. The analysis revealed that mean patch size decreased while the number of patches increased. Landscape diversity declined, urban dominance amplified, and the overall landscape mosaics became more continuous, homogenous and clumped. In order to devise sustainable land use planning and to determine future landscape changes for sound resource management strategies, the present study is expected to have significant implications in rapidly urbanizing cities of the world in delivering baseline information about long term land use change and its impact on landscape structure.     

Forest biomass carbon dynamics (1980–2009) in western Himalaya in the context of REDD+ policy

Carbon emissions from forests have decreased in the past decade due to conservation efforts, however majority of carbon losses suffered in the past went unnoticed until the role of forests in mitigating climate change was realized. Forestry sector in developing countries is recognized as one of the largest and low cost mitigation options to address climate change. The present study was conducted to assess the multi-temporal biomass carbon mitigation in the temperate forests of western Himalaya using satellite (Landsat MSS, TM, ETM+) and forest inventory data. Forest type density mapping was done through on-screen visual interpretation of satellite data. After conducting preliminary survey in 2009, 45 quadrats (0.1 ha) were laid in six forest types for collecting field inventory data viz., diameter at breast height, tree height, slope and aspect. Biomass carbon (t ha^?1) was estimated for different forest types with different crown densities (open with 10–40% crown density and closed with >40%) using recommended regression equations, ratios and factors. A decreasing trend of carbon (145.13–134.87 mt) was observed over the period of time. Temporal biomass carbon dynamics was analyzed for REDD+ opportunities. The temporal variation of carbon observed was found to be more useful for claiming benefits under negative options (deforestation and forest degradation) of REDD+. The study doesn’t take actual conversions to CO₂ into account. However, the findings are useful in establishing baseline emissions through temporal carbon losses. Further, the study helps in identification of location specific socio-economic drivers of losses that can be used for appropriate mitigation interventions.     

Poverty and environmental nexus in rural Pakistan: a multidimensional approach

The study examines the relationship between poverty and forest cover degradation in rural areas of Pakistan. The area selected for the study District Upper Dir is a rural and relatively backward region located in northwestern Pakistan, in Khyber-Pakhtunkhwa province. The study area is undergoing severe deforestation and natural disasters in the recent past. The study consists of two stages, in first stage the traditional Geographical information system image was used to analyze the spatial–temporal situation of the surroundings. In the second stage, well-designed questionnaire was used to collect the primary information from 420 randomly selected households of research areas. A multidimensional poverty index has been used to measure the poverty profile of the population. It has been found that 55% households were below the poverty line. Almost, 95% households are using wood for cooking purposes. High dependence on natural resources causes forest cover degradation while burning off too much wood causes CO₂ emission and leads to environmental degradation. A major portion of population is living on steeply sloped areas with certain risks. It is found that frequency of flash flood is 53% and agricultural land (54%) is at high risk and often flows with flash floods. It is concluded that there is strong correlation between multidimensional poverty and forest cover degradation which leads to climate and environmental risks.

     

Impact assessment of land cover changes on the runoff changes on the extreme flood events in the Kelantan River basin

In the current years, changing the land cover/land use had serious hydrological impacts affecting the flood events in the Kelantan River basin. The flood events at the east coast of the peninsular Malaysia got highly affected in the recent decades due to several factors like urbanisation, rapid changes in the utilisation of land and lack of meteorological (i.e. change in climate) and developmental monitoring and planning. The Kelantan River basin has been highly influenced due to a rapid change in land use during 1984 to 2013, which occurred in the form of transformation of agricultural area and deforestation (logging activities). In order to evaluate the influence of the modifications in land cover on the flood events, two hydrological regional models of rainfall-induced runoff event, the Hydrologic Engineering Center (HEC)-Hydrologic Modeling System (HMS) model and improved transient rainfall infiltration and grid-based regional model (Improved TRIGRS), were employed in this study. The responses of land cover changes on the peak flow and runoff volume were investigated using 10 days of hourly rainfall events from 20 December to the end of December 2014 at the study area. The usage of two hydrological models defined that the changes in land use/land cover caused momentous changes in hydrological response towards water flow. The outcomes also revealed that the increase of severe water flow at the study area is a function of urbanisation and deforestation, particularly in the conversion of the forest area to the less canopy coverage, for example, oil palm, mixed agriculture and rubber. The monsoon season floods and runoff escalate in the cleared land or low-density vegetation area, while the normal flow gets the contribution from interflow generated from secondary jungle and forested areas.     

Assessment of changes in the area of the water conservation forest in the Qilian Mountains of China’s Gansu province, and the effects on water conservation

The Qilian Mountains water conservation forest in Gansu province is an important ecological barrier surrounding the oasis in China’s Hexi Corridor. The water they provide is the basis for the existence and sustainable socioeconomic development of those oases. As a result of unsustainable use of the water conservation forest until the 1980s, the oasis ecosystems of the Hexi region were seriously damaged, and the oasis areas experienced deterioration of their ecological environment. In this paper, Landsat images were used to monitor the temporal and spatial changes in area of water conservation in Qilian Mountains of China’s Gansu province and to assess the effect on water conservation by analyzed relationship between water conservation forest area, climatic data and hydrological data. The results showed that the forest covered 15.1% of the study area in 2007 and has followed different trends during the study period. From 1978 to 1990, the forest area decreased; however, from 1990 to 2007, the forest area increased, with a faster rate of increase from 1990 to 2000, and the rate of increase averaged 2,733.89 ha per year since 1990. The water conservation forest appears to play an important role in flood control, runoff regulation, the prevention of soil erosion, and water conservation; and these benefits increase with an increasing area of forest.     

Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass

Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R² = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R² = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R² = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.