TM Satellite Data Research Articles

Exploring the performance of spectral and textural information for leaf area index estimation with homogeneous and heterogeneous surfaces

AbstractLeaf area index (LAI) is one of the key parameters of vegetation structure, which can be applied in monitoring vegetation growth status. Currently, abundant spatial information (e.g., textural information), provided by the developing remote sensing satellite techniques, could boost the accuracy of LAI estimation. Thus, the performance of spectral and textural information must be evaluated for different vegetation types of LAI estimation in different surface types. In this study, different spectral vegetation indices (SVIs) and grey‐level co‐occurrence matrix‐based textural variables under different moving window sizes were extracted from Landsat TM satellite data. First, the ability of different types of SVIs for LAI estimation in different surface types was analysed. Subsequently, the effect of different texture variables with different moving window sizes towards LAI estimation accuracy in different vegetation types was explored. Lastly, the performance of SVIs combined with textural information for the LAI estimation in different vegetation types was evaluated. Results indicated that SVIs performed better for LAI estimation in the homogeneous region than that in the heterogeneous region, and difference vegetation index was more remarkable for LAI estimation in different vegetation types than other SVIs. In addition, variations in texture variables and moving window sizes had a large influence on LAI estimation of natural vegetation with high canopy heterogeneity. SVI combined with textural information can efficiently improve the accuracy of LAI estimation in different vegetation types (R2 = 0.672, 0.455 and 0.523 for meadow, shrub and cantaloupe, respectively.) compared with SVI alone (R2 = 0.189, 0.064 and 0.431 for meadow, shrub and cantaloupe, respectively.). Especially for natural vegetation (meadow, shrub), the addition of textural information can greatly improve the accuracy of LAI estimation.

Changes in River Channel Dynamics of the Satluj River, Punjab: Using a Geospatial Approach

Abstract River channel change is a dynamic process which involves erosion, accretion, lateral migration and changes in its width through the geomorphological process. Shifting of a river causes a direct impact on natural and manmade constructions which are located near the floodplain with consistent damage and devastation of the area. The objective of this study is to analyze the spatiotemporal variation along the Satluj river during the period 1990 to 2019 using Landsat TM, ETM+ and OLI satellite data. Spatiotemporal analysis of the river channel has been carried out to analyze the river channel change morphology, mid-line channel shifting and changes in surface water area of the Satluj river using twenty randomly distributed cross-sections (X1-X20). For surface water area change analysis, the satellite derived normalized difference water index (NDWI) has been used. The obtained results indicates that the river width increased by 0.033 km in overall cross-section (X1-X20) in the year 2019 as compared to the year 1990. Mid-line shifting of the river is mostly towards the left bank of the river. The maximum channel shifting of 1.364 km took place from Chak Bandala village in Jalandhar district to Madarpur village in Moga district between the years 1990 and 2019. However, the NDWI results indicate that the river surface area has been reduced by -90.33 km2 between the years 1990 and 2019. Additionally, the average shifting rate of the Satluj river during the period 1990 to 2019 is by 0.778 km.

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

Urban Sprawl Assessment and Its Effect on Land Use Transformation in Varanasi City using Remote Sensing and GIS

This paper seeks to examine the effect of urbanization on changes in land use in the peri-urban areas of Varanasi city in India. The area of study is divided into six different classes of land use: built-up area, agriculture, vegetation, water bodies, sand and other land use. Using the maximum likelihood technique, Landsat 5 TM satellite data were used to identify land use and land cover changes from 1996 to 2017. The findings indicate a substantial increase in the built-up area, associated with reduced water and other land use cover. The urban sprawl is observed in almost all directions from the city boundaries, and along highways. Shannon’s entropy analysis reveals dispersed distribution of built-up area. The approach based on GIS and remote sensing data, together with statistical analysis, has proved instrumental in the analysis of urban expansion. It also helps to identify priority areas that require adequate planning for sustainable development.

Regional heat capacity changes on oil palm plantation development in 1994-2010 based on Landsat-5 TM satellite data

Regional heat capacity change is calculated from the ratio between the addition or subtraction of heat (ΔQ) with the increase or decrease in temperature (ΔT) region. The purpose of this study is to calculate the regional heat capacity change due to the changes of land cover composition with forest, shrubs, oil palm plantation and bare soil using Landsat-5 TM satellite data on 1994, 2000 and 2010. The total area used in this study is 12971 ha. In 1994-2000, 4 % of forest area and 2% shrubs were increased, followed by addition biomass forest 4.01 tons/ha and 2.83 tons/ha for shrubs. The increased of forest area and biomass (tons/ha) caused by forest and shrubs growth processing towards a climax that added the canopy volume. So that, the regional heat capacity in 1994 amounted 19384 MJ °C−1 increased to 19929 MJ°C−1 in 2000. Data observation for 2000-2010 showed that forest area decreased by 66% due to forest’s clearing into oil palm plantations (47%), shrubs (8%) and bare soil (11%). However, plant’s biomass continues to increased, i.e. 1.48 ton/ha for the forest, 2.73 tons/ha for shrubs and 4.63 tons/ha for bare soil. Before 2000, there was no land cover by oil palm plantations, so the increasing rate from this land was the biggest than the three other lands, amounting to 122.29 tons/ha. Decreasing in the percentage of forest area does not cause a decrease in the heat capacity of the region. Intensive maintenance on oil palm plantation such as water management, fertilizer, and planting space made it biomass productivity, and ability to save the heat is greater than the forest. As a result, in 2010 regional heat capacity increased to 22508 MJ °C−1.

Spatiotemporal influences of land use/cover changes on the heat island effect in rapid urbanization area

Rapid urban sprawl and growth led to substantial urban thermal environment changes and influenced the local climate, environment, and quality of life of residents. Taking the Chang-Zhu-Tan urban agglomeration in China as a case, this study firstly identified the spatiotemporal patterns of surface urban heat island intensity (SUHII) and the land use/cover changes (LUCC) based on multi-temporal Landsat TM satellite data over 21 years, and then investigated the relationship between LUCC and SUHII by methods of logistic regression model and centroid shift analysis. The results showed that green spaces (e.g., cropland, forestland) of 899.13 km2 had been converted to built-up land during the 1994–2015 period, which caused significant urban expansion. The SUHII was the highest for built-up land, high for unused land, low for cropland and grassland, and the lowest for forestland and open water. Many areas experienced extensive rapid urbanization because of the emergence of the urban agglomeration, which resulted in the loss of green spaces and increased SUHI effects over the 21-year study period. In addition, the results of centroid shift analysis found that the growth of SUHII and the expansion of high SUHII areas are closely related to the expansion of an existing urban area in Xiangtan, while the increases of building density and height in Changsha resulted in the decrease of SUHII and spatiotemporal change of high SUHII areas. The analysis of the effects of land use/cover types on the SUHII in this study will contribute to future urban land use allocation for the mitigation of SUHI formation.

Impacts of Urban Expansion on Urban Heat Island - A Geospatial Approach

Growing urbanisation has led to increase in the built up area, reduction in open and green spaces with in the cities and the periphery. The urban saturation and the enlargement of the built space have determined environmental changes, increasing the already precarious condition of the natural systems in these spaces of high saturation. It results in to the formation of Urban Heat Island (UHI). Over the last few years, Ahmedabad has experienced rapid urbanization and associated Urban Heat Island (UHI) effects. This study aims at analysing spatially and temporally, the impact of urban form expansion on UHI in Ahmedabad using Landsat thermal images. The Mono Window Algorithm has been used to retrieve Land Surface Temperature (LST) from the thermal bands of LANDSAT-5, 8 TM satellite data. Various spatial analysis techniques were used to explore the relationships between area, compactness ratio, the gravity centers of urban land, and UHI. Under the similar urban land area condition, UHI and compactness ratio of urban land were in positive correlation. The moving direction of the UHI gravity center was basically in agreement with urban land sprawl. The encroachment of urban land on suburban land is the leading source of UHI effect. The results suggest that urban design based on urban form would be effective for regulating the thermal environment.

Mapping Mountain Permafrost Landscapes in Siberia Using Landsat Thermal Imagery

Intensive development of South Yakutia, a mountainous area in the Russian sporadic permafrost zone, must be founded on knowledge about regional permafrost conditions. New permafrost maps for mountainous areas in South Yakutia (the Elkon Mountains and the Olekma-Chara Upland) are presented that provide a more detailed and updated description of permafrost distribution in the area than those that were hitherto available. These maps are based on the previously-developed and tested method of detecting permafrost and unfrozen ground using Landsat-5/TM satellite data with relatively high resolution. The method represents a scheme for permafrost identification based on a set of landscape indicators: terrain elevation, slope angle and exposition, vegetation, snow cover, and land surface temperature (LST). A correlation analysis of satellite data to full-scale field data has been carried out for the two areas under consideration. Indicator properties of LST obtained by Landsat-5/TM Band 6 Infrared have been characterized in detail for detection and regional mapping of permafrost. The effect of landscape factors (landscape cryo-indicators) on ground temperature and condition, frozen or unfrozen reflected in LST intensity, is demonstrated.

Simulating the post-fire net primary production restoration and its affecting factors by using MTCLIM and 3PGS model in Genhe forest region, Northeast China

Fire is a major disturbance factor in Daxing'anling region, with important impacts on carbon balance of forest ecosystems. Fire severity and the distinction of microclimates induced by different topography are the primary factors driving the restoration of post-fire net primary productivity (NPP). In this study, we examined the influence of fire severity and topographic factors on the restoration of forest NPP in the Genhe forest region. The spatial and temporal restoration process of post-fire NPP were simulated by combining with MTCLIM and 3PGS model based on multiyear Landsat TM satellite (2008-2012) and climate (1980-2010) data. The results showed that the 3PGS-MTCLIM model could precisely estimate the spatial distribution of NPP at small scales, with a good correlation between simulated and observed values (R2=0.828). The percentage of declined NPP in the year following the fire ranged 43%-80%, and the average NPP recovery period for this region was about 10 years by comparing pre- and post-fire NPP. Fire severity had significant impacts on post-fire recovery. The stronger the fire intensity, the longer the recovery period was needed. The NPP recovered relatively slower after a period of fast-speed recovery. Among the three topographic factors, elevation was the strongest one affecting forest NPP restoration, followed by slope and aspect.

Analysis of rain fall and the temperature of Coimbatore District using land use and land cover change detection by image segmentation

Image segmentation is a process has done for the classification of high resolution remote sensing images in the present research work. The segmentation results are capable of influencing the subsequent process effects. An image can be partitioned into a number of disjoint segments which is used to represent the image structures. It is found that it is more compact to represent an image and the low level and high structures can be combined. There are different types of methods to segment an image namely, threshold-based, edge-based and region-based. Region growing approach is image segmentation methods in which the neighboring pixels are examined and merged with the class region in case of no edges are detected. The iteration is done for every pixel boundary. Unlike gradient and Laplacian methods, the edges of the region are found by the region growing and it is perfectly their region. The images are determined by the LANDSAT TM satellite data. The remote sensing technique is used for collecting information about the Coimbatore district. The sensed data is a key to many diverse applications. The contribution of this work for Coimbatore district is to find the change of the Land used and Land covered in the entire region and also to find the changes in the green lands, vegetation and Land surface utilized for urban area. The neighboring regions are taken into account and the similarities are checked in the growing process. No single region is allowed to dominate the entire proceedings. A certain number of regions are allowed to grow at a time. Comparable regions will gradually combine into expanding regions. The Control of these methods may be quite complicated but efficient methods have been developed. The directions of growing pixels are easy and efficient to implement on parallel computers. The threshold-based segmentation is completely depending on the gray level images which regards the reflectivity of the featured images. It determines a threshold based on brightness of the ground objects. It is purely from the image background. But it is rapid and its uncertainty is significant. It is not convenient to process multi-spectral images.

Spatiotemporal impact of land use/land cover changes on urban heat islands: A case study of Paço do Lumiar, Brazil

The rapid urbanization in Paço do Lumiar County has had a remarkable effect on the urban thermal environment and influenced the local climate, environment, and quality of life of residents. The intensity and accelerated growth of urban heat islands (UHI) were investigated, and changes in land use/land cover (LULC) were detected over 16 years based on multi-temporal Landsat TM satellite data. TM/Landsat images and OLI/Landsat images were used to assess the thermal characteristics of urban areas and the relationship between LULC changes and land surface temperature. The results of the multi-temporal analysis of the land surface and acquired monitoring data were used to evaluate the influences of vegetation cover and fragmentation on the urban environment. The results indicate that the city experienced extensive urban growth during the study period. UHIs fluctuated in the city, with the largest observed in the 2014 image. The UHI intensity and spatial distribution were higher and more dominant in urban zones compared to those in other areas of the city. Many areas experienced extensive urbanization over the 16-year study period, which resulted in the loss of green spaces and increased UHI effects.

Change Detection of Land Surface Temperature (LST) and some Related Parameters Using Landsat Image: a Case Study of the Ebinur Lake Watershed, Xinjiang, China

This study assesses and detects land use/cover (LUC) and land surface temperature (LST) change using multi-temporal Landsat TM satellite data. NDVI, albedo and MNDWI were used to analyze the LST qualitatively. The results revealed that the accuracy of LST measurements in watershed is within 1.5 °C. Then, temperature changes between 1998 and 2011 were analyzed. The classifications of land surface temperatures lie in five categories as follows: lower (1.9–8.9 °C), low (8.9–15.9 °C), middle (15.9–22.9 °C), high (22.9–29.9 °C), higher (29.9–36.9 °C), and highest (36.9–43.9 °C). Second, east-west profiles of the characteristics of the distribution of LUC types were made based on 1998 and 2011 images. By comparing LSTs in these two years, one can conclude woodland-grassland has a very strong influence on temperature. Third, LST increased with the increases in the density of salinized and desert lands, but decreased with the increase in vegetation cover. The relationship between MNDWI and LST was significantly negatively correlated. Multiple regression analyses between LST and each index as well as elevation were created to evaluate the watershed thermal environment. This regression showed that NDVI, albedo, MNDWI and a digital elevation model were effective indicators for quantifying the effects of land use/cover change (LUCC) on LST, and the correlation coefficient R was 0.806. Finally, natural and human factors were important factors affecting temperature change. Generally, the temperature of the oasis was lower than the surroundings, which results in a ‘cold island effect’.

Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa

The advancement, availability and high level of accuracy of satellite data provide a unique opportunity to conduct environmental and epidemiological studies using remotely sensed measurements. In this study, information derived from remote sensing data is used to determine breeding habitats for Anopheles arabiensis which is the prevalent mosquito species over Nkomazi municipality. In particular, we have utilized the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) coupled with land surface temperature (LST) derived from Landsat 5 TM satellite data. NDVI, NDWI and LST are considered as key environmental factors that influence the mosquito habitation. The breeding habitat was derived using multi-criteria evaluation (MCE) within ArcGIS using the derived environmental metric with appropriate weight assigned to them. Additionally, notified malaria cases were analysed and spatial data layers of water bodies, including rivers and dams, were buffered to further illustrate areas at risk of malaria. The output map from the MCE was then classified into three classes which are low, medium and high areas. The resulting malaria risk map depicts that areas of Komatieport, Malelane, Madadeni and Tonga of the district are subjected to high malaria incidence. The time series analysis of environmental metrics and malaria cases can help to provide an adequate mechanism for monitoring, control and early warning for malaria incidence.

Soil Salinity Mapping Using Satellite TM and Field Data in Southeastern Isfahan

This study aimed to evaluate the performance of TM satellite data acquired in June 2009 to map soil salinity in southeast of Isfahan province. Ground salinity data (EC) was collected within 9 pixels, covering an area of approximately 8100 m using stratified random sampling technique at 53 sample sites. Spectral indices including TM bands, BI, SI1, SI2 and SI3, PC1, PC2, PC3 and also multiple linear regression modeling and maximum likelihood classification techniques were applied to the geometrically corrected image. Results of regression analysis showed that the TM band 4 had the strongest relationship with EC data (R=0.48) and also the relationship of the modeling image using TM 3, TM 4, TM5 and PC3 was significant at the 99% confidence level. The accuracy assessment of the stratified TM4 and modeling image into five classes including 0-4, 4-20, 20-60, 60-100 and EC>100 ds/m indicated that there was more than 86% agreement with the field measurements of EC data. Therefore, it can be concluded that the discretely classified salinity maps have higher accuracy than regression methods for identifying broad areas of saline soils, and can be used as appropriate tools to manage and combat soil salinization.

Change detection of urban heat islands and some related parameters using multi-temporal Landsat images; a case study for Cairo city, Egypt

Cairo has a unique geographic location and is one of the highest built-up densities worldwide. This study attempts to map and detect changes in land-cover and heat islands over Cairo through three decades using multi-temporal Landsat TM satellite data. Five sub-scenes (1984, 1990, 2001, 2006 and 2013) were selected for summer daytime. A supervised classification was used to map land use/landcover and changes. Landsat bands were used to derive the albedo and Normalized Difference Vegetation Index. The thermal band was used to retrieve the land surface temperature (LST) and urban heat island (UHI). Results show that during the observed period, the city experienced a massive urban growth. The UHI was fluctuating, the highest value recorded was in 2013 image. NDVI mean values decreased probably due to weak management policies for green areas. Based on the observed images, the LST values were higher over eastern zones. The highest albedo values were recorded in the eastern desert zones. It was also noticed that the UHI intensity and spatial distribution were higher and more dominant in such zones compared the UHI in the dense built-up areas of the city.

A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique

Conservation and management of Sundarban mangrove forest is difficult chiefly due to inaccessibility and hostile condition. Remote sensing serves as an important tool to provide up-to date baseline information which is the primary requirement for the conservation planning of mangroves. In this study, supervised classification by maximum likelihood classifier (MLC) has been used to classify LANDSAT TM and LANDSAT ETM satellite data. This algorithm is used for computing likelihood of unknown measurement vector belonging to unknown classes based on Bayesian equation. Image spectra for various mangrove species were also generated from hyperspectral image. During field visits, GPS locations of five dominant mangrove species with appreciable distribution were taken and image spectra were generated for the same points from hyperion image. The result of this classification shows that, in 1999 total mangrove forest accounted for 55.01 % of the study area which has been reduced to 50.63 % in the year 2010. Avicennia sp. is found as most dominating species followed by Excoecaria sp. and Phoenix sp. but the aerial distribution of Avicennia sp., Bruguiera sp. and Ceriops sp. has reduced. In this classification technique the overall accuracy and Kappa value for 1999 and 2010 are 80 % and 0.77, 85.71 % and 0.81 respectively.

Feature level fusion for enhanced geological mapping of ophiolile complex using ASTER and Landsat TM data

Chromite ore deposit occurrence is related to ophiolite complexes as a part of the oceanic crust and provides a good opportunity for lithological mapping using remote sensing data. The main contribution of this paper is a novel approaches to discriminate different rock units associated with ophiolite complex using the Feature Level Fusion technique on ASTER and Landsat TM satellite data at regional scale. In addition this study has applied spectral transform approaches, consisting of Spectral Angle Mapper (SAM) to distinguish the concentration of high-potential areas of chromite and also for determining the boundary between different rock units. Results indicated both approaches show superior outputs compared to other methods and can produce a geological map for ophiolite complex rock units in the arid and the semi-arid region. The novel technique including feature level fusion and Spectral Angle Mapper (SAM) discriminated ophiolitic rock units and produced detailed geological maps of the study area. As a case study, Sikhoran ophiolite complex located in SE, Iran has been selected for image processing techniques. In conclusion, a suitable approach for lithological mapping of ophiolite complexes is demonstrated, this technique contributes meaningfully towards economic geology in terms of identifying new prospects.

Predictive Analysis for Vegetation Biomass Assessment in Western Ghat Region (WG) Using Geospatial Techniques

Forests play a critical role in ecological functioning, global warming and climate change through its unique potential to capture and hold carbon (C). Biomass is one of the indicator of the status of forests hence accurate assessment and biomass mapping is important for sustainable forest management. The objectives of this study is to estimate above ground biomass (AGB) from field inventory data and to map AGB combining field inventory data, remote sensing and geo-statistical model. In the present study stratified random sampling were used for estimation of biomass in which 59 plots were laid down in different homogenous strata depending on the NDVI values for the region of Maharashtra Western Ghats. The above ground biomass from field ranged from 0.05 to 271 t-dry wt ha−1 in which trees added maximum towards total biomass followed by shrubs and herbs. This paper evaluates the best vegetation indices to estimate biomass. This study was carried out by using Landsat TM satellite data and field inventory data in the Ratnagiri district of Maharashtra, India. A significant correlation was observed between biomass and vegetation indices. The best fit regression equation developed from field above ground biomass and NDVI with R2 value of 0.61 was used for spectral modeling to estimate the geospatial distribution of AGB in the entire region. The results of spatial predictions Geostatistical technique and remotely sensed data as auxiliary variables were compared using statistical error methods. This study employed Mean error, Root-Mean-Square error, Average Standard error and Root-Mean Square Standardized error. The ME, RMSE, Average Standard error and Root-Mean Square Standardized error was 0.078, 8.032, 7.982 and 0.967 respectively. The results showed that cokriging technique is one of the geostatistical method for spatial predictions of biomass in the studied region. The present study revealed that remote sensing technique combined with field sampling provides quick and reliable estimates of above ground biomass and carbon pool and can be used as baseline information for further temporal studies of biomass status of the region and in planning of forest and natural resources management.

Development of Remote Sensing Based Models for Surface Water Quality

The objectives of this paper were to develop, evaluate, and apply the remote sensing based models for Canadian Water Quality Index (CWQI) and turbidity for the Bow River of Alberta. We used 31 scenes of Landsat‐5 TM satellite data to establish the relationship between the planetary reflectance and the monthly ground measured data for the period of 5 years (i.e. 2006–2010). The four spectral bands (i.e. blue, green, red, and near infrared) were used to obtain the most suitable models from 26 different band combinations. The co‐efficients of determination on the basis of red band were 0.91 for the CWQI model and 0.82 for the turbidity model. The best‐fit models were validated with ground measured data and found that: 72% of the data showed 100% matching for the CWQI model and 83% of the data for the turbidity model. The Landsat‐5 TM based CWQI and turbidity models were applied on all the scenes to obtain five CWQI classes (i.e. excellent, good, fair, marginal and poor), and six classes of turbidity (i.e. 0–10 NTU, 10–20 NTU, 20–30 NTU, 30–40 NTU, 40–50 NTU, >50 NTU). On the basis of percentages obtained for CWQI and turbidity classes, the ranks of years in terms of water quality from best to worst were: 2009, 2006, 2008, 2010, and 2007 respectively. The variation of river water quality in different years of interest was associated with the climatic changes. The most deteriorated water quality noted in two natural sub‐regions included mixed grass and dry mixed grass, which could be related to irrigation‐based farming.

Dynamic Assessment of Soil Erosion Risk Using Landsat TM and HJ Satellite Data in Danjiangkou Reservoir Area, China

Danjiangkou reservoir area is the main water source and the submerged area of the Middle Route South-to-North Water Transfer Project of China. Soil erosion is a factor that significantly influences the quality and transfer of water from the Danjiangkou reservoir. The objective of this study is to assess the water erosion (rill and sheet erosion) risk and dynamic change trend of spatial distribution in erosion status and intensity between 2004 and 2010 in the Danjiangkou reservoir area using a multicriteria evaluation method.The multicriteria evaluation method synthesizes the vegetation fraction cover, slope gradient, and land use. Based on the rules and erosion risk assessment results of the study area in 2004 and 2010, the research obtained the conservation priority map. This study result shows an improvement in erosion status of the study area, the eroded area decreased from 32.1% in 2004 to 25.43% in 2010. The unchanged regions dominated the study area and that the total area of improvement grade erosion was larger than that of deterioration grade erosion. The severe, more severe, and extremely severe areas decreased by 4.71%, 2.28%, and 0.61% of the total study area, respectively. The percentages of regions where erosion grade transformed from extremely severe to slight, light and moderate were 0.18%, 0.02%, and 0.30%, respectively. However, a deteriorated region with a 2,897.60 km2 area was still observed. This area cannot be ignored in the determination of a general governance scheme. The top two conservation priority levels cover almost all regions with severe erosion and prominent increase in erosion risk, accounting for 7.31% of the study area. The study results can assist government agencies in decision making for determining erosion control areas, starting regulation projects, and making soil conservation measures.