Multi-temporal Satellite Images Research Articles

Deep learning-based framework for spatiotemporal data fusion: an instance of Landsat 8 and Sentinel 2 NDVI

Multi-source, multi-temporal, and multi-resolution satellite images have been increasing over a couple of years. The spatiotemporal data fusion technique could be a promising solution to achieve the advantage of temporal and spatial details of multi-platform remote sensing images. Due to the unavailability of required temporal resolution of normalized difference vegetation index (NDVI) of high-spatial-resolution dataset (e.g., Sentinel 2 A/B), the remote sensing-based crop monitoring is limiting in several parts of the world, primarily in the monsoon Asia and tropical region. We propose a deep learning-based framework to fill the gap of high-resolution NDVI value using corresponding low-spatial-resolution NDVI images. Time-series NDVI images from Sentinel 2 and Landsat 8 are used to implement the proposed framework. We evaluate our method on two study areas with different climatic conditions: subtropical and temperate. The results demonstrate that spatiotemporal data fusion with our proposed framework is very effective to synthesize and generate new datasets which represents the unique features of both input sources, namely, spatial and temporal details of each input image regardless of the climatic and temporal variations.

Finding a suitable sensing time period for crop identification using heuristic techniques with multi-temporal satellite images

ABSTRACT Satellite crop identification processes are increasingly being used on a large scale, both to verify the crop and to improve production. As it is necessary to study phenological data over a period of time across a large territory, a lot of storage space is needed to save the satellite images and a lot of calculation time to analyse all this information. Sensing periods are usually established based on subjective expert criteria or previous experience. However, this decision may cause several differences when discriminating crop patterns, besides not guaranteeing good precision. These processes would greatly improve if the appropriate time periods could be found systematically using the minimum number of satellite images in the shortest possible time. In this paper, we propose a new methodology to determine a suitable sensing period for crop identification using Sentinel-2 images, applying hill climbing algorithms to the training sets of neural network models. We have used the method successfully in the 2020 Common Agricultural Policy campaign in the Extremadura region, Spain. The article also describes the use of the method in a case on tobacco detection in this region.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh

In recent years, the world has shown considerable concerns about environmental degradation accompanied by urban expansion. In terms of size, Bogura is equivalent to most of the major cities in Bangladesh, yet no thermal and ecological assessment has ever been conducted here. This study uses multitemporal Landsat satellite images between 2001 and 2020 to investigate the thermal and ecological conditions of Bogura Sadar (sub-district). Land surface temperature (LST) is obtained from Landsat images using the widely used radiative transfer equation. The thermal and ecological conditions are evaluated by computing urban heat island (UHI) and urban thermal field variance index (UTFVI) from LST data. The influence of vegetation, built-area, water-body, and bare soil on LST are examined using land cover indices through pixel-level multivariate linear regression analysis. According to the findings of this sub-district-scale (urban and rural areas) study, the mean LST has increased by 0.62 °C in the last 20 years. As per local administrative-wise findings, LST has increased in most areas, regardless of their urban or rural function. The difference between the urban area and the rest of the surroundings was significant (1.74 °C) in 2020. In 2001, UHI affected area was 5.65 km2, which expanded to 8.84 km2 in 2020. Thermal and ecological conditions are worse in urban areas than its surrounding areas. The regression models of the LST and land cover indices could explain more than half (R2: 0.66 to 0.73) of LST variation over the years. Land cover could explain the LST in 2020 to the least extent implying that anthropogenic activities have greater influence than earlier. Land cover could explain less than half of the LST variation in the urban area.

MORPHODYNAMICS OF CHILIKA MOUTH- STUDY ON SHIFTING OF TIDAL INLETS AND ASSESSMENT OF SHORELINE CHANGE ALONG EASTERN COASTAL INDIA, USING GEOSPATIAL TECHNIQUES AND DSAS EXTENSION

This study is focused on the spatio-temporal change in shoreline along Chilika coast and migration of tidal inlet of Chilika lagoon using multi temporal satellite images from the year 1972-2019. This study is carried out over decadal analysis with utilization of EPR and LRR system of DSAS model. For this change analysis studies the Landsat satellite images and IRS-P6 LISS-III images has been used. From the study it reveals that there is one new mouth opening in 2019 after occurrence of cyclone Fani. Based on calculations, the mouth of Chilika mostly has shifted in northward direction. It may due to frequently occurrences of cyclonic storm surges, which usually associated with winds and tidal actions over the coast. This study shows the high erosion trend along 8.3 km in northern part of shoreline. The accretion found along 6.5km in central part of shoreline. The coastal region is now more vulnerable to natural disaster as well as manmade disaster. Most of the areas are prone to floods. This problem may become very serious due to rapid increase in population. Every year the eastern coastal region is destroyed by cyclone and floods.

Assessment of Soil Erosion and Its Impact on Agricultural Productivity by Using the RMMF Model and Local Perception: A Case Study of Rangun Watershed of Mid-Hills, Nepal

Soil erosion is a major concern for the environment and natural resources leading to a serious threat to agricultural productivity and one of the major causes of land degradation in the mid-hills region of Nepal. An accurate assessment of soil erosion is needed to reduce the problem of soil loss in highly fragile mountainous areas. The present study aimed to assess spatial soil loss rate and identified risk areas and their perceived impact on agricultural productivity by using the Revised Morgan–Morgan–Finney (RMMF) model and social survey in the Rangun watershed of Dadeldhura district, Nepal. Soil erosion was assessed by using data on soil, digital elevation model, rainfall, land use, and land cover visually interpreted from multitemporal satellite images, and ILWIS 3.3 academic software was used to perform the model. A household questionnaire survey (n = 120) and focus group discussion (n = 2) in identified risk areas were carried out to understand the people’s perception towards soil erosion and its impact on agricultural productivity. The predicted average soil erosions from the forest, agriculture, and barren land were 2.7 t ha−1 yr−1, 53.73 t ha−1 yr−1, and 462.59 t ha−1 yr−1, respectively. The erosion risk area under very low to low, moderate to moderately high, and high to very high covers 92.32%, 4.96%, and 2.73%, respectively. It indicates that the rate of soil erosion was lower in forest areas, whereas it was higher in the barren land. The cropped area of the watershed has been reduced by 2.96 ha−1 yr−1, and productivity has been decreased by 0.238 t ha−1 yr−1. The impacts such as removal of topsoil (weighted mean = 4.19) and gully formation (weighted mean = 3.56) were the highest perceived factors causing productivity decline due to erosion. People perceived the impact of erosion in agricultural productivity differently ( ∗ significant at P ≤ 0.05 ). The study concluded that, comparatively, barren and agricultural lands seem more susceptible to erosion, so the long-term conservation and management investment in susceptible areas for restoration, protection, and socioeconomic support contribute significantly to land rehabilitation in the Rangun watershed.

Analysis of shoreline changes along the coast of Tiruvallur district, Tamil Nadu, India

Shore line change is considered as one of the most dynamic processes, which were mapped along the coast of Tiruvallur district by using topographic maps of 1976 and multi-temporal satellite images. The satellite images pertaining to 1988, 1991, 2006, 2010, 2013 and 2016 were used to extract the shorelines. It is important to map and monitor the HTL (High Tide Line) at frequent time intervals as the shoreline was demarcated by using visual interpretation technique from satellite images and topographic maps. Followed by this, an overlay analysis was performed to calculate areas of erosion and accretion in the study area. The results revealed that the coast of Tiruvallur district lost 603 ha and gained 630 ha due to erosion and accretion respectively. It was confirmed after the ground truth survey carried out in the study area. The high accretion of 178 ha was found nearby Pulicat Lake and low accretion of 19 ha was seen between Pulicat Lake and Kattupali Port. The high erosion area was found along the Pulicat Lake, Kattupali and Ennore ports, and Ennore creek mouth and southern Ennore such as Periya Kuppam, Chinna Kuppam, Kasi Koil Kuppam, and Thyagarajapuram. It may be concluded that the coastal erosion and accretion in the study area were mainly caused by anthropogenic and natural factors, which altered the coastal environment.

Land Cover and Coastline Change Assessment of Nijhum Dwip, Bangladesh, using Geospatial Analysis

Nijhum Dwip is a southern island of Bangladesh isolated from the mainland, in the convergence of the Meghna River and the Bay of Bengal. This island has studied through overlay analysis and supervised classification by geospatial and remote sensing technique, over 38 years (1980-2018) using multitemporal Landsat MSS, TM, OLI, and TIRS satellite images with identification of historical changes. This landform is facing frequent shifting of its coastline and leading to sequential changes on the land surface. Analysis revealed substantial growth of settlement and agricultural land whereas significant lessening on vegetation cover and open space. In 1990 agricultural land was 4.47 km2 (13.29%) and improved to 9.16 km2 (19.17%) in 2018. Similarly, settlement also increased from 1.92 km2 (4.79%) in 1999 to 5.72 km2 (11.97%) in 2018. Conversely, vegetation was primarily 8.02 km2 (27.71%), 18.70 km2 (55.61%), 20.97 km2 (52.29%), 18.47 km2 (36.28%) and 15.28 km2 (31.98%) in 1980,1990,1999, 2010 and 2018, indicating declination. As well, water bodies and open space also fluctuated through the period because of geomorphological processes and human intervention. Besides, the least and highest unstable char land was 1.15 km2 (3.42%) and 1.68 km2 (5.80%) in 1990 and 1980.

Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites

For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m).

The dynamics of landscape changes surrounding a firefly ecotourism area

Land use land cover (LULC) patterns have been reported to significantly affect ecotourism areas. Understanding the dynamic patterns of LULC changes is important for the efficient management of these areas. This research determined the influence of LULC changes on mangrove areas and identified the status of mangrove vegetation cover surrounding a firefly ecotourism area using multi-temporal Landsat satellite images from 2002 through 2017. A maximum likelihood classifier and normalised vegetation index differencing were used to determine LULC changes. Mangrove vegetation cover was examined using fraction vegetation cover analysis. Classification accuracy for LULC type was 86.1% and 87.2% for 2002 and 2017, respectively. The results showed a 22.06% loss (122.52 ha) and 4.47% increase (24.99 ha) in mangrove areas. Mangrove areas were converted to oil palm plantations (32.7%), forests (32.3%), settlements (9.25%), barren land (8.99%), rivers (6.8%), swampland (6.11%), urban development (1.67%), rubber plantations (1.25%), bodies of water (0.77%) and roads (0.09%). Increases in mangrove vegetation cover were observed along riverbanks away from development activities. Decreases in mangrove vegetation cover were affected by land-use activities. Proper management is required to avoid further loss of mangrove areas, and appropriate LULC management planning is needed to conserve mangrove areas and sustain firefly ecotourism.

The impact of land use and land cover changes on socioeconomic factors and livelihood in the Atwima Nwabiagya district of the Ashanti region, Ghana

Land use and land cover change (LULCC) is a general term for the human modification of Earth's terrestrial surface. These modifications come by direct and indirect activities of human actions when securing essential resources. Therefore, it is necessary to analyze these land changes for the best management of natural resources and to ensure better livelihood of the inhabitants.The influx of population from the Kumasi metropolis and peripheral areas coupled with urbanization is causing rapid changes to LULC in the Atwima Nwabiagya District. This has put pressure on the land cover especially in the forest cover areas and has affected the socioeconomic activities in the district. In view of this problem, this research sought to map and analyze the spatio-temporal LULCC patterns using multi-temporal satellite images from 1986-2019 and to identify the socioeconomic factors and their impact of LULC on the study area. Seven LULC classes were identified including; bare land, closed forest, cropland, open forest, savannah, settlement, and water. The results showed that during the period under review (1986-2019) there have been losses of 4178.5 hectares (7%) in closed and open forest and bare land whilst crop land and settlement have seen an increase of 21685.3 hectares (37%). In all, settlement establishment as a result of population increase, commercial activities, and porous land tenure system was identified as the main factors for LULCC in the Atwima Nwabiagya District.

Modeling the Environmental Hazards of El-Kharga Oasis Sand Dunes, Western Desert of Egypt, using Remote Sensing and GIS Techniques

In this study, an integrated suite of Remote Sensing (RS) data and Geographic Information System (GIS) techniques supported by fieldwork is used to assess the sand dunes movement hazards at El-Kharga Oasis. Digital Elevation Model (DEM) data obtained from the Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection (ASTER) were integrated with GIS techniques to model the vulnerable locations and to study the terrain characteristics (slope angles and aspects) in the studied area. The risk assessment model output was verified with the field investigations using multi-temporal satellite images recorded between 1990 and 2019. Monthly wind roses showed that the sand drifts in the southeastern direction differed widely from one direction to another depending on the wind direction and velocity. The most important output of the spatial model’s, results was a geo-hazard map that classified the sand dunes hazard zones into low, slight, moderate, and high-risk zones. It is concluded that, the sand dunes pose a serious hazard because of their fast movement and accumulation near the monumental sites, over roads and invading the agricultural fields. The obtained results can serve as a basis for planners and decision-makers to take the necessary precautions and measures to minimize the sand dune hazard’s impact on the monumental sites (e.g., Hibis, El-Nadura, El-Ghueita and El-Zayyan), roads, and the agricultural fields at El-Kharga Oasis and lead to a sustainable development plan. Keywords Environmental Hazards; Sand Dunes Movement; Remote Sensing; Risk Assessment, Egypt

Integrating farmers’ decisions on the assessment of forest regeneration drivers in a rural landscape of Southeastern Brazil

Forest regeneration at large-scales is one of the main paths to achieving the ongoing ambitious restoration commitments. Thus, the identification of the main drivers of this process in agricultural landscapes is critical to understand the drivers determining restoration success. A growing number of studies have explored the biophysical and, less often, the socioeconomic drivers of forest regeneration using remote sensing approaches, but have not directly considered the influence of farmers’ decisions in spatial prediction models of forest regeneration. We explored the influence of biophysical and socioeconomic drivers on forest regeneration in a rural landscape of Southeastern Brazil, where native forest cover increased by 7.7%. We evaluated forest regeneration through the analysis of time series of multi-temporal satellite images and socioeconomic information obtained through a Social Survey with 47 landowners, using each farm as the study unit. Natural forest regeneration was mainly favored by lower land suitability for agriculture and higher proximity (lower distances) to forest remnants, as well by higher numbers of land-uses types in the farm and lower economic dependence of landowners from the farm income. Our results emphasize the importance of considering farmers’ decisions on predictive models of natural forest regeneration, which are critically needed to guide the implementation of large-scale forest restoration initiatives in agricultural landscapes.

Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer

A timely and detailed crop-specific land cover map can support many agricultural applications and decision makings. However, in-season crop mapping over a large area is still challenging due to the insufficiency of ground truth in the early stage of a growing season. To address this issue, this paper presents an efficient machine-learning workflow for the rapid in-season mapping of corn and soybeans fields without ground truth data for the current year. We use trusted pixels, a set of pixels that are predicted from the historical Cropland Data Layer (CDL) data with high confidence in the current year’s crop type, to label training samples on multi-temporal satellite images for crop type classification. The entire mapping process only involves a limited number of satellite images acquired within the growing season (normally 3–4 images per scene) and no field data needs to be collected. According to the investigation on 12 states of the U.S. Corn Belt, it is found that a considerable number of trusted pixels can be identified from the historical CDL data by the trusted pixel prediction model based on artificial neural network. According to the experiment on 49 Landsat-8 scenes and 31 Sentinel-2 tiles, the in-season maps of corn and soybeans are expected to reach 85%–95% agreement with CDL as well as field data by mid-July. Once the in-season satellite imagery becomes available, the crop cover map can be rapidly created even with limited computational resources. This study provides a new perspective and detailed guidance for rapid in-season mapping of corn and soybeans, which can be potentially applied to identify more diverse crop types and scaled up to the entire United States.

Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh

Cities are developing rapidly and subsequently influences the land use/land cover (LU/LC) changes. Rajshahi city in Bangladesh facing rapid urban expansion by replacing LU/LC such as vegetation cover, water bodies (natural resources) and bare land to urban settlements. This study aims to identify the LU/LC changing patterns of Rajshahi city for 2000, 2010 and 2020 and simulate future LU/LC changes for 2040. Multi-temporal cloud-free Landsat satellite images were used to estimate the LU/LC and urban settlement changing pattern using remote sensing and geographic information system techniques. A support vector machine algorithm was used to classify the LU/LC classes. All the classified LU/LC maps demonstrate excellent accuracy by showing more than 85% overall accuracy. The multi-layer perception Markov chain (MLP-MC) model was used to predict the future LU/LC scenario for 2040. Validation of the predicted LU/LC map demonstrated more than 90% overall accuracy. The study found that an average of 3.6 km2 urban settlements increased at 10 years interval by replacing the natural resources. The future prediction shows that more than 60% of the total city area will be transformed into urban settlements by 2040. The dramatic increase in urban settlements and reduction in green cover and water bodies will damage the environmental sustainability of Rajshahi city by increasing temperature and heatwaves. This study will help the urban planners and policymakers to make proper sustainable development decisions by understanding the LU/LC changing pattern.

Quantitative appraisal of naturalistic/anthropic shoreline shifts for hurghada: Egypt

This investigation aims to establish a system to test the identification of shoreline changes along the coast of Hurghada. Besides, the Digital Shoreline Analysis System (DSAS) studies the shoreline shift rate analysis. Multi-temporal satellite images have revealed accretion and erosion patterns in the areas of concern. Landsat images with different periods were geometrically and radiometrically corrected, then classified through assigning 8 different classes for the quantitative coastline delineation analysis from 1987 to 2019: (Landsat 5–TM, Thematic Mapper (TM)), Landsat7 ETM+, worldview 2, and Sentinel 2). To detect both land use and land cover change (LULCC) and shoreline growth, a maximum controlled classification tool is used. The DSAS was used to measure the coastal changing rate as a powerful statistical analysis tool, and thus to predict the behavior of the coastal shift over a long time. DSAS used two separate methods: EPR and LRR. The results concluded that, primarily as a consequence of unregulated landfill practices before tourist facilities, Hurghada's coastal region experienced an average accretion rate of 1.38 m/y. Moreover, the El-Gouna study revealed drastic modifications because of the development of its coastal zone's closed inland lagoons. Finally, the creation of the decision matrix reveals the evolution of the shorelines in the three last decades and the statistical projection for the three next decades. This study highlights the problem of extreme erosion in the Southern part (which is still not completely developed).

Cause and Process Mechanism of Rockslide Triggered Flood Event in Rishiganga and Dhauliganga River Valleys, Chamoli, Uttarakhand, India Using Satellite Remote Sensing and in situ Observations

The catchments of Rishiganga and then Dhauliganga valleys in the Chamoli district of Uttarakhand were impacted by a catastrophic flood triggered due to a massive rockslide, caused by wedge failure on 7th February, 2021. It is estimated that the massive rockslide of ~ 23 million cubic meter volume containing base rock, deposited ice, and snow got detached from the northern slopes of the Trishul mountain range near Ronti Glacier and created a vertical fall of almost 1700 m before severely impacting the Ronti Gad valley located at 1.5 km downstream of Ronti Glacier snout. The huge detached mass of rock and ice (GLIMS ID: G079733E30381N) swiftly moved downstream through the glaciated valley entraining snow, debris, mud on its way, caused rapid fluidization, created massive water/slush waves, and washed away partially or completely the hydel power projects and bridges in its route. It is estimated that ~ 0.93 Peta Joules of potential energy led to the generation of a significant amount of kinetic and thermal energy, good enough to trigger above-mentioned processes. Post-event analysis of high-resolution satellite data shows flood water marks in the valley and on the rock outcrops reaching up to ~ 80–150 m height on the way to Raini Village. The mud and the slush produced through this process led to the formation of a dammed lake and temporarily blocked one of the tributaries of the Rishiganga joining from the northeast. This study provides an insight into the sequence of events as they unfolded, through multi-temporal satellite image analysis, aerial survey, seismological data in conjunction with various other geo-spatial and geo-visualization tools for unraveling the flood event that has happened on February 7, 2021. We also discuss the potential cause of rockslide and the process mechanism of this unique event, causing loss of lives and property besides widespread devastation.

Soil property maps with satellite images at multiple scales and its impact on management and classification

Soil maps at appropriate scales can aid decision-making in agriculture and the environment. In this sense, remote sensing products have shown their power to investigate soil properties, but the assessment of its spatial information can be hampered by the presence of other objects than soil. To overcome this issue, soil scientists have been studying spatial patterns from multi-temporal satellite images aiming at at improving soil property maps. In this work, we applied the cubist algorithm to predict topsoil properties (clay, sand, organic matter and iron contents, and soil color components) in south-eastern Brazil using multi-temporal (Landsat8-OLI, and Sentinel2-MSI) and single-date images (PlanetScope, Landsat8-OLI, and Sentinel2-MSI). We aimed to evaluate the influence of satellite’s spatial, spectral and temporal resolutions on soil mapping. Predictive models were constructed with 120 soil samples and using four (vis-NIR) and six (vis-NIR-SWIR) spectral bands as predictors in a 10-fold cross-validation procedure. The multi-temporal image obtained from the Sentinel2-MSI satellite (with 10 m pixel size and six spectral bands), showed the best model performances in cross-validation (R2 between 0.48 and 0.78). The PlanetScope image, which has only four bands in the vis-NIR region and spatial resolution of 3 m did not improve model performances, although the R2 values were higher for soil color components (R2 > 0.5). Satellite images in different spatial, spectral and temporal resolution provides slightly different soil property maps which may promote different strategies regarding soil classification and management. However, satellite images should be used with caution, as they provide only surficial information about the soil variability and confirmation with field surveys is required.

Dynamics of land‐use changes and their future trends using spatial analysis and the CA‐Markov model—A case‐study with a special emphasis on aquaculture development in India

AbstractLand resources utilized for aquaculture development have raised many environmental issues worldwide. But no detailed information is available about the land‐use changes due to aquaculture in comparison with other land use modifications. Hence, the land‐use changes and predictions have been explored in India's leading aquaculture region using multitemporal satellite images (1986–2016), spatial techniques, and land change modeling. Agriculture was the largest proportionate land use in the study area and consistently used for aquaculture and buildings. There were no aquaculture farms in 1986; however, their area had expanded to 12,575 ha by 1996 and further increased to 38,564 ha (207%) in 2006. It then reduced in area by 36% in 2016. Agricultural lands have been used for 74%, 66%, and 15% of aquaculture development during 1986–1996, 1996–2006, and 2006–2016, respectively. Scrublands and mudflats have been used for aquaculture growth between 6 and 22% and 1–2% at different periods of our study. Salt pans and mangroves were occasionally used (less than 0.5%) for aquaculture. Dynamic land‐use changes have been observed between agriculture and scrublands. The reconversion to agriculture occurred from the aquaculture area by 20 and 51% during 1996–2006 and 2006–2016, respectively. It is predicted that the aquaculture area will further reduce, but agriculture is likely to be used for aquaculture and for buildings, despite the existing ban. Land availability is a major limiting factor; thus, it will pose a significant challenge and lead to conflicts in the future if not planned for now.

ĐÁNH GIÁ BIẾN ĐỘNG RỪNG NGẬP MẶN TỈNH CÀ MAU TRÊN CƠ SỞ ẢNH VỆ TINH GIAI ĐOẠN 1988 - 2018

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.