American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 © Global Society of Scientific Research and Researchers http://asrjetsjournal.org/ Change Detection, Monitoring and Mapping the Conditions of Rufiji Delta Using Remote Sensed Data Gosbert Msogoya a*, Job Chaula b a Kilombero Agricultural Training and Research Institute P/Bag, KATRIN, Ifakara, +255, Tanzania bUniversity, P.O.Box 35176, Dar es salaam, 255, Tanzania aEmail: msogoyagt@gmail.com bEmail: jobchaula@gmail.com Abstract The study focused on change detection, monitoring and mapping of water, forests, grasslands and wetlands resources in Rufiji Delta using remote sensed data. The general objective of the study was to complement the insitu methods for change detection with remote sensed data and GIS technique for monitoring and mapping wetlands forest, water and grasslands in Rufiji Delta. Specific objectives were to determine current status of the forests, water and wetland in Rufiji Delta using Land sat Images and develop a map of Rufiji Delta using remote sensed data. The study used LandSat Images of 1997, 2000 and 2007 which were processed using ERDAS computer software. Unsupervised classification approach specifically the Iterative Self-Organizing Data Analysis (ISODATA) algorithm was utilized. The selected AIO were subjected to Matrix Union Analysis which the techniques employed when attempting to reveal the change detection. There was an average of 7% and 12% of forest histograms that changed into grassland histograms and of grassland histogram that changed into forest histogram, respectively between years of 1997 to 2007. There was average loss of 1.73% and 1.86%% of grassland histograms that changed into wetlands histograms and wetland histograms that changed into grasslands histogram, respectively between years of 1997 to 2007. There was 0.8% and 7% average loss of histograms of wetlands which change into forests histograms and average loss of forests histograms into wetland histograms, respectively between years of 1997 to 2007. There was an average percentage loss of 0.12% and 0.26% of histograms of forests histograms that changed into water histograms and water histograms that change into forests histograms, respectively between years of 1997 to 2007. The results shows that there is declining trend in forests reservoir by 6.2% which changes into wetlands such situation in future will encourage water lodging conditions, soil erosion and secondary salinization during heavy rains. ------------------------------------------------------------------ * Corresponding author. 1 http://asrjetsjournal.org/ American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 The map of Rufiji Delta shows that Kiomboi, Mchinga Msufini and Nyamisati village are very close to the delta and Indian Ocean hence susceptible to on flash and sea level rise floods. Thus planning of site selection of dams and tracking canal to manage the expected floods should well establish in these villages. This study has con- firmed that there is opportunity for using remote sensed data for change detection, monitoring and management of natural resources in Rufiji Delta. However the information from unsupervised classification should be used as preliminary findings that need to be supplemented with supervised classification. Keywords: Change detection in Rufiji Delta; Remote sensing for Monitoring; mapping natural resources. 1. Introduction The complexity nature of human disturbances coupled with aggressive climate change impacts in Rufiji Basin are necessitating for modern methods of monitoring and management of natural resources in the basin. In par- ticular, the in situ methods used in Rufiji Basin for monitoring and management of natural resources in needs to be supplemented with modern methods remote sensed data and geographic information systems (GIS). In situ methods are time consuming, tedious, fatigues and expensive. The development of digital change detection era and regular acquisition of digital data of the Earth surface in multispectral bands allowed scientists to get rela- tively consistent data over time and to characterize changes over relatively large area for the first time. Among many approach of change detection vector analysis of spectral changes is based on the spatial representation of change in a spectral space [1] has the advantage to process concurrently any number of spectral bands. Thus, when a pixel undergoes a change between two dates, its position in n-dimensional spectral space is expected to change. This change is represented by a vector which is defined by two factors, the direction which provides information about the nature of change and the magnitude which provides information about the level of change. Change detection through remote sensed data offer quick and reliable means for determining and monitoring land-cover changes in large geographic extents and with high temporal coverage [2]. However, application of remote sensed data and GIS for change detection and monitoring, management natural resources in Rufiji Delta is diminutively reported. Remote sensed data and GIS provide information even inaccessible areas which can’t be reached by in situ methods. Incorporating the application of remote sensed data in determining the change detection, monitoring and mapping of resources in Rufiji Delta is very imperative since it’s reported that, the mangrove forests are heavily exploited for both the export market and local use [3]. Thus, the overall objective of the research was t complement the in situ methods for change detection with remote sensed data and GIS technique for monitoring and mapping wetlands forest, water and grasslands in Rufiji Delta. The specific objec- tives were to determine current status and map the forests, water and wetland in Rufiji Delta using Land sat Im- ages of 1997, 2002 and 2007. Time series analysis using LandSat image of 1997 and 2007 have provided the rate of loss of forest, wetland and water in the Rufiji Delta which is also crucial for planning and developing strategic intervention mechanism. In particular, the study has provided the preliminary findings that are vital for future planning of land use/land cover programs in village close to the delta. Moreover, mapping of the Delta has revealed that, villages close to the delta are prone to on flash and sea level rise floods which on other hand is calling for design of dams and tracking canal to manage floods. However, information obtained from unsuper- vised classification of images should be used as preliminary findings that need to be supplemented by findings from supervised classification approach which involves ground truthing. 2 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 2. Methodology 2.1 Description of the study area The Rufiji Delta is located about 200 km south of Dar es Salaam. Nine major tributaries form the Rufiji River basin, which extends for about 177,000 km2 and covers roughly 20 percent of Tanzania’s area [3]. There are some 43 islands in the Delta though the entire delta area is generally flat. An inventory carried out in 1989 shows that the mangroves of mainland Tanzania cover about 115,500 ha of which the Rufiji Delta alone is about 53,255 ha (40 percent of total). 2.2 Materials • LandSat Image of 1997, 2000 and 2007 • Digital Map of Coastal Region of Tanzania 2.3 Methods 2.3.1 Image preprocessing Unsupervised approach was utilized in this project, specifically the Iterative Self-Organizing Data Analysis (ISODATA) algorithm. A full scene of Landsat Thematic Mapper (TM) imagery of 1997, 2000 and 2007 was used in the study. The images encompassed seven bands (Blue, Green, Red, Near Infrared, Short-wave Infrared, Thermal Infrared and Mid Infrared). Land Sat Images was imported to ERDAS imagine for converting the file from TIFF format to Imagine (*img) using import module in ERDAS 10 computer software. Images were displayed and layer stacked using interpreter icon that was followed by selecting Utilities- layer stack tool in the ERDAS 10 computer software. The images were reprojected to Grid UTM zone 37 in Arc 1960 to register the output image into the real world coordinates. An Area of Interest (AOI) which is Rufiji Delta was created and stored for further analysis. Classification process which involves sorting pixels into a finite number of individual classes, or categories, of data based on their brightness values was done using unsupervised classification techniques. Unsupervised classification techniques involve assigning the pixels of an image to classes by calculating the distances between specific pixels within feature space, and assigning them to cluster-centers. Iterative Self-Organizing Data Analysis Technique (ISODATA) was used for assigning classes. This algorithm enabled grouping of pixels with similar spectral characteristics by deriving statistics (mean and standard deviation) of groups and assigning a class to each pixel according to its distance from mean [4]. In first step of unsupervised classification 30 classes with 5 iterations was selected and this was reduced to four classes. The classes were reduced to four classes which are water, forests, wetlands and grasslands. For the purpose of this study only the Rufiji Delta were selected as Area of Interest (AOI). Figure (1-3) represents the AOI for unsupervised classified images of 1997, 2000 and 2007, respectively. The selected AIO of Rufiji Delta was classified into four classes which are water, wetlands, forests and grasslands. The reduction of the 30 classes to classes were done using recode tool in the ERDAS 10.0 computer software. The selected AIO for image of 1997, 2000 and 2007 were subjected to Matrix Union Analysis. Matrix union analysis is the techniques employed when attempting to reveal the change detection [5]. In this approach, once the data base (layers and 3 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 attribute data) is assembled, the layers can be analyzed and new information extracted. Some information can be extracted simply by looking at the layers and visually comparing them to other layers [5]. In this study changes detection in class over time, post-classification change detection was employed using the matrix union function in ERDAS Imagine by methods developed by [5, 6] with some modification. In the matrix analysis the image of 1997 and 2000 was used followed by 1997 to 2007 and finally image of year 2000 and 20007. Figure 1 : Unsupervised Classification of LandSat Image 1997 Figure 2: Unsupervised Classification of LandSat Image 2000 4 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 Figure 3 : Unsupervised Classification of LandSat Image 2007 3. Results 3.1 Output of Matrix Union Analysis for Change Detection in Rufiji Delta Figure (4 - 6) are the output image of year 1997 versus 2000, 1997 versus 2007and 2000 versus 2007, respectively. Table (1 - 3) narrates the output of Matrix Union Analysis of Image of 1997 versus 2000, 2000 versus 2007 and 1997 versus 2007 of Rufiji Delta while Table (4 - 16) summarizes the computed percentage loss of wetlands, forests, water and grasslands histogram between years of 1997 to 2007. 3.2 Percentage Loss of Wetland, Forest, Water and Grasslands between Years of 1997 to 2007 3.2.1 The percentage loss of wetlands histograms into forests histograms and it’s versus Table (4) below summarizes the percentage loss of wetland histograms into forest histograms and percentage loss of wetland histograms into forest histogram. There was average percentage loss of wetlands histograms into forests histograms at the rate of 1.1% between years of 1997 to 2000, at the rate of 0.6% between years of 2000 to 2007, at the rate of 0.6% between years of 1997 to 2007. Thus, there is 0.8% average of histograms of wetlands which change into forests histograms. Besides, Table (5) shows that the average percentage loss of forests histograms into wetland histograms per year varies between 8, 6 and 7% for years between 1997 to 2007, 2000 to 2007 and 1997 to 2007, respectively. Thus, there is an average of 7% loss of forests histograms into wetland histograms between years of 1997 to 2007. When compared to average percentage loss of wetland 5 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 histograms into forests histograms the net loss of forest histograms by 6.2% to wetlands histograms between years of 1997 to 2007 is obtained. Figure 4 : Output of matrix union analysis of image 1997 versus 2000 Table 1: Output matrix union analysis of image of 1997 versus 2000 Rows Histogram Recode_1997 Recode_2000 Description 1 799946 1 1 Water (stable) 2 9205 1 2 Loss of water to forests 3 34444 1 3 Loss of water to wetlands 4 86109 1 4 Loss of water to grasslands 5 7102 2 1 Loss forests to water 6 917778 2 2 Forests (stable) 7 314964 2 3 Loss of forests to grassland 8 17724 2 4 Loss of forests to wetland 9 5007 3 1 Loss of grassland to water 10 264928 3 2 Loss of grassland to forest 11 221671 3 3 Grassland (stable) 12 40686 3 4 Loss of grassland to wetland 13 4016 4 1 Loss of wetland to water 14 30945 4 2 Loss of wetland to forests 15 85041 4 3 Loss of wetland to grassland 16 777558 4 4 Wetland ( stable) 6 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 Figure 5: Output of matrix union analysis of image of 1997 versus 2007 Table 2: Output of matrix union of 1997 versus 2007 Rows Histogram Recode_1997 Recode_2007 Description 1 567359 1 1 Water (stable) 2 17338 1 2 Loss of water to forests 3 42351 1 3 Loss of water to wetlands 4 26096 1 4 Loss of water to grasslands 5 8895 2 1 Loss forests to water 6 489081 2 2 Forests (stable) 7 403734 2 3 Loss of forests to grassland 8 14169 2 4 Loss of forests to wetland 9 9823 3 1 Loss of grassland to water 10 171856 3 2 Loss of grassland 11 153605 3 3 Grassland (stable) 12 47492 3 4 Loss of grassland to wetland 13 20036 4 1 Loss of wetland to water 14 40996 4 2 Loss of wetland to forests 15 77179 4 3 Loss of wetland to grassland 16 512033 4 4 Wetland ( stable) 7 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 Figure 6 : Output of matrix union analysis of image 2000 versus 2007 Table 3: Output of Matrix Union Analysis of Image of 2000 versus 2007 Rows Histogram Recode_2000 Recode_2007 Description 1 546375 1 1 Water (stable) 2 8192 1 2 Loss of water to forests 3 9834 1 3 Loss of water to wetlands 4 4380 1 4 Loss of water to grasslands 5 6491 2 1 Loss forests to water 6 500865 2 2 Forests (stable) 7 359623 2 3 Loss of forests to grassland 8 23584 2 4 Loss of forests to wetland 9 21903 3 1 Loss of grassland to water 10 182170 3 2 Loss of grassland 11 224019 3 3 Grassland (stable) 12 46904 3 4 Loss of grassland to wetland 13 30458 4 1 Loss of wetland to water 14 26972 4 2 Loss of wetland to forests 15 82741 4 3 Loss of wetland to grassland 16 524652 4 4 Wetland ( stable) 3.2.2 The percentage loss of forests histograms into water histograms it’s versus From the Table (4.6) above there was average percentage loss of forests histograms into water histograms at the 8 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 rate of 0.17% between years of 1997 to 2000, at the rate of 0.10% between years of 2000 to 2007, at the rate of 0.097% between years of 1997 to 2007. Thus, there is 0.12% average of histograms of forests histograms that changed into water histograms. Table 4: The Percentage Loss of wetland histograms into forest histograms for years between 1997 to 2000, 2000 to 2007 and 1997 to 2007 Year Percentage loss of wetlands histograms into forests histograms (%) Average Percentage loss per year ((%) 1997 – 2000 3.3 1.1 2000 – 2007 4.1 0.6 1997 – 2007 6.3 0.6 Total 13.7 2.3 Average 4.56 0.8 Table 5: The Percentage Loss of forests histograms into wetland histograms for years between 1997 to 2000, 2000 to 2007 and 1997 to 2007 Year Percentage loss of forests histograms into wetland histograms (%) Average Percentage loss per year (%) 1997 – 2000 25 8 2000 – 2007 40 6 1997 – 2007 73 7 Total 138 21 Average 46 7 Table 6: The Percentage Loss of forests histograms into water histograms for years between 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of forests histograms into water histograms (%) Average Percentage loss per year (%) 1997 – 2000 0.52 0.17 2000 – 2007 0.72 0.10 1997 – 2007 0.97 0.09 Total 2.21 0.36 Average 3.00 0.12 On contrary, the average percentage loss of water histograms into forests histograms was at the rate of 0.33% 9 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 between years of 1997 to 2000, at the rate of 0.20% between years of 2000 to 2007, at the rate of 0.26% between years of 1997 to 2007 (Table 7). Thus, there is 0.26% average of histograms of water histograms that change into forests histograms. There is net gain in forest histograms by 0.14% from wetlands histograms between years of 1997 to 2007. Table 7: The Percentage Loss of water histograms into forest histograms between years 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of water histograms into forests histograms (%) Average Percentage loss per year (%) 1997 – 2000 0.99 0.33 2000 – 2007 1.4 0.20 1997 – 2007 2.6 0.26 Total 4.99 0.79 Average 1.66 0.26 3.2.3 Percentage loss of water histograms into grasslands histograms and it’s versus From the Table (8) above there was average percentage loss of water histograms into grasslands histograms at the rate of 3.09% between years of 1997 to 2000, at the rate of 0.10% between of years 2000 to 2007, at the rate of 0.39% between years of 1997 to 2007. Thus, there is 1.19% average of histograms of water histograms that changed into grasslands histograms. Table 8: The Percentage Loss of Water histograms into Grasslands histogram between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of water histograms into grasslands histograms Average Percentage loss per year 1997 – 2000 9.26 3.09 2000 – 2007 0.77 0.10 1997 – 2007 3.99 0.39 Total 14.02 3.58 Average 4.67 1.19 On contrary, the average percentage loss of grasslands histograms into water histograms was at the rate of 0.31% between years of 1997 to 2000, at the rate of 0.66% between years of 2000 to 2007, at the rate of 0.26% between years of 1997 to 2007 (Table 9). Thus, there is an average of 0.52% histograms of water histograms that changed into grasslands histograms. Therefore, there, is net gain of grassland histograms by 0.67% from water histograms between years of 1997 to 2007. 10 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 Table 9: The Percentage Loss of Loss of grasslands histograms into water histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007 Year Percentage loss of grasslands histograms into water histograms (%) Average Percentage loss per year (%) 1997 – 2000 0.94 0.31 2000 – 2007 4.60 0.66 1997 – 2007 2.57 0.26 Total 8.11 1.57 Average 2.7 0.52 3.2.4 The percentage loss of wetlands histograms into water histograms and it’s versus From the Table (10) below there was average percentage loss of wetlands histograms into water histograms at the rate of 0.15% between years of 1997 to 2000, at the rate of 0.66% between years of 2000 to 2007, at the rate of 0.31% between years of 1997 to 2007. Thus, there is 0.37% average of histograms of wetlands histograms that changed into water histograms between years of 1997 to 2007. On contrary, the average percentage loss of loss of water histograms into wetlands histograms was at the rate of 5.30% between years of 1997 to 2000, at the rate of 0.25% between years of 2000 to 2007, at the rate of 0.65% between years of 1997 to 2007 (Table 11). Thus, there is 2.07% average of histograms of loss of water histograms into wetlands histograms. Therefore, there, is net gain of wetland histograms by 2% from water histograms between years of 1997 to 2007. Table 10: The Percentage Loss of wetland histograms into water histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of wetlands histograms into water histograms (%) Average Percentage loss per year (%) 1997 – 2000 0.45 0.15 2000 – 2007 4.58 0.66 1997 – 2007 3.08 0.31 Total 8.11 1.12 Average 2.70 0.37 3.2.5 The percentage loss of grasslands histograms into water histograms and it’s versus From the Table (12) below there was average percentage loss of grasslands histograms into water histograms at the rate of 0.22% between years of 1997 to 2000, at the rate of 0.66% between years of 2000 to 2007, at the rate of 0.45% between years of 1997 to 2007. Thus, there is 0.44% average of grasslands histograms that changed into water histograms between years of 1997 to 2007. On contrary, the average percentage loss of water histograms into grasslands histograms was at the rate of 3.09% between years of 1997 to 2000, at the rate of 11 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 0.11% between years of 2000 to 2007, at the rate of 0.40% between years of 1997 to 2007 (Table 13). Thus, there is 1.20% average of histograms of water histograms that change into grassland histograms. Therefore, there, is net gain of grassland histograms by 1.2% from water histograms between years of 1997 to 2007. Table 11: The Percentage Loss of water histograms into wetlands histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of histograms of water into wetland histograms (%) Average Percentage loss per year (%) 1997 – 2000 15.89 5.30 2000 – 2007 1.72 0.25 1997 – 2007 6.48 0.65 Total 24.09 6.20 Average 8.03 2.07 Table 12: The Percentage Loss of grasslands histograms into water histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of grasslands histograms into water histograms (%) Average Percentage loss per year (%) 1997 – 2000 0.66 0.22 2000 – 2007 4.61 0.66 1997 – 2007 4.52 0.45 Total 3.26 1.33 Average 1.09 0.44 Table 13: The Percentage Loss of water histograms into grasslands histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of water histograms into grasslands histograms (%) Average Percentage loss per year (%) 1997 to 2000 9.26 3.09 2000 to 2007 0.77 0.11 1997 to 2007 3.99 0.40 Total 14.01 3.6 Average 4.67 1.2 12 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 3.2.6 The Percentage Loss of grassland histograms into wetlands histograms and it’s versus From the Table (14) there was average percentage loss of grassland histograms that changed into wetlands histograms at the rate of 2.55% between years of 1997 to 2000, at the rate of 1.41% between years of 2000 to 2007, at the rate of 1.24% between years of 1997 to 2007. Thus, there is 1.73% average of grassland histograms that changed into wetlands histograms between years of 1997 to 2007. On contrary, the average percentage loss of wetland histograms into grasslands histograms was at the rate of 3.16% between years of 1997 to 2000, at the rate of 1.25% between years of 2000 to 2007, at the rate of 1.17% between years of 1997 to 2007 (Table 15). Thus, there is 1.86%% average of histograms of wetland histograms that changed into grasslands. Therefore, there, is net gain of grassland histograms by 0.13% from wetland histograms between years of 1997 to 2007. Table 14: The Percentage Loss of grassland histograms into wetlands histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of grasslands histograms into wetland histograms (%) Average Percentage loss per year (%) 1997 – 2000 7.64 2.55 2000 – 2007 9.88 1.41 1997 – 2007 12.41 1.24 Total 29.93 5.20 Average 9.98 1.73 Table 15: The Percentage Loss of wetland histograms into grasslands histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of wetland histograms into grasslands histograms (%) Average Percentage loss per year (%) 1997 to 2000 9.48 3.16 2000 to 2007 8.81 1.25 1997 to 2007 11.72 1.17 Total 30.01 5.58 Average 10.00 1.86 3.2.7 The Percentage Loss of grassland histograms into forest histograms and it’s versus From the Table (16) there was average percentage loss of forest histograms into grasslands at the rate of 8% between years of 1997 to 2000, at the rate of 6% between years of 2000 to 2007, at the rate of 8% between years of 1997 to 2007. Thus, there is an average of 7% of forest histograms that changed into grassland histograms 13 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 between years of 1997 to 2007. On contrary Table (17) presents the average percentage loss of grassland histograms into forest histograms which was at the rate of 15% between years of 1997 to 2000, at the rate of 5% between years of 2000 to 2007, at the rate of 17% between years of 1997 to 2007. Thus, there is an average of 12% of grassland histograms that changed into forest histograms between years of 1997 to 2007. Table 16: The percentage loss of forest histograms into grassland histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of forest histograms into grassland histograms (%) Average Percentage loss per year (%) 1997 to 2000 25 8 2000 to 2007 40 6 1997 to 2007 73 8 Total 138 22 Average 46 7 Table 17: The percentage loss of grassland histograms into forest histograms between years of 1997 to 2000, 2000 to 2007 and 1997 to 2007. Year Percentage loss of grassland histograms into forest histograms (%) Average Percentage loss per year (%) 1997 to 2000 45 15 2000 to 2007 38 5 1997 to 2007 50 17 Total 133 37 Average 44 12 4. Discussion and Conclusion 4.1 Loss of water histograms to wetlands grassland and forest histograms between years of 1997 to 2007 There is average of 0.37% and 2.07% of wetlands histograms that changed into water histograms and water histograms into wetlands histograms between years of 1997 to 2007, respectively. There, is net gain of wetland histograms by 2% from water histograms between years of 1997 to 2007. This imply that water reservoir are getting drier at average of 2%, thus the areas that were covered by water are currently drying with ability of supporting life of some plant species. There is an average 0.52% and 1.19% histograms of water histograms that changed into grasslands histograms and water histograms that changed into grasslands histograms, respectively. Thus, there is net gain of grassland histograms by 0.67% from water histograms between years of 1997 to 2007. 14 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 This entail that the area that were formerly covered by water have changed into grassland by 0.67% between years of 1997 to 2007. There is an average of 0.12% and 0.26% histograms of forests histograms that changed into water histograms and water histograms that change into forests histograms, respectively. There is net gain in forest histograms by 0.14% of water histograms between years of 1997 to 2007. These also imply that the water resources reservoirs are declining at the rate of 0.14% between years of 1997 to 2007. This trends shows that areas covered by water in the Rufiji Delta is decreasing which is in line with early research findings reported by [7]. 4.2 Change of forest histogram to wetland, water and grassland histogram from 1997 to 2007 There are 0.8% and 7% average loss of histograms of wetlands which change into forests histograms and average loss of forests histograms into wetland histograms between years of 1997 to 2007, respectively. By comparing the above average percentage loss a net loss of forest histograms by 6.2% to wetlands histograms between years of 1997 to 2007 is obtained. This imply that, there is declining trend in forests reservoir by 6.2% which changes into wetlands such situation in future will encourage water lodging conditions, soil erosion and secondary salinization during heavy rains. These finds conforms that there is decline of forested areas in the Rufiji Delta as reported in previous research findings by [7] which reported the decrease in the area coverage of mangroves by mentioning the considerable decrease in the density, height and canopy cover of the mangroves within the forests. More less similar, findings were reported by [7]) which reported that, mangrove ecosystems are being altered by uncontrolled human activities, mainly through overexploitation of mangrove wood for construction and fuel, and from cutting of substantial areas of mangroves for solar saltpans, agriculture and aquaculture (e.g. rice and shrimp ponds), industries, and urban and hotel developments. Moreover, [8] reported that, data on the extent of harvesting of mangrove forest products in the deltas is scanty and inconsistent, but project officials believe legal harvesting is high, and illegal harvesting is even higher. Besides [5] the increase in the market of hardwood from Tanzania, especially to China, has been a lucrative business to everyone who was involved in the business, from felling trees in the forest to transportation. The sitution has prolonging for some time since [8] reported the harvesting trends are likely to continue or even increase as long as new alternatives and opportunities are not found, and as far as ineffective management regimes are still in place [8] However, the future scenario may change for the better if sustainable approaches are adopted. Besides, there is an average percentage loss of 0.12% and 0.26% of histograms of forests histograms that changed into water histograms and water histograms that change into forests histograms, respectively. By comparing the two percentage loss a net gain in forest histograms by 0.14% from water histograms between years of 1997 to 2007 is obtained. This imply that, water reservoir areas that were covered by water are getting drier hence has the areas has encouraged plant regeneration and growth of shrubs and other small trees that tolerate water lodged conditions. In other words the areas which formerly were not supporting growth and development of plants due water lodging are currently getting off from water lodged conditions and supporting plant growth and development. There is an average of 7% and 12% of forest histograms that changed into grassland histograms and of grassland histogram that changed into forest histogram between years of 1997 to 2007. By comparing the two percentage loss a net gain in grasslands histogram by 5% from forest histograms between years of 1997 to 2007 is obtained. This implies that areas which were under gone deforestation recently its plants are regenerating and sprouting especially during the rainy season. 15 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 4.3 Change of wetlands histogram into grassland and water histogram between years of 1997 to 2007 There are average loss of 1.73% and 1.86%% of grassland histograms that changed into wetlands histograms and wetland histograms that changed into grasslands histogram between years of 1997 to 2007. By comparing the two average losses above, a net loss of wetlands by 0.13% to grassland is obtained between years of 1997 to 2007. This also implies that areas which were formerly covered by wetlands reservoirs in Rufiji Delta are currently changing to grasslands between years of 1997 to 2007. The decline of wetlands in Tanzania areas was early reported in 1990s by [9] and has both socio economic and ecological implication. [7] reported several reasons accounts for this decline in wetlands including the overexploitation of high-value species, the introduction of invasive non-native species, pollution and climate change. Although many diverse activities cause these direct threats, the specific proximate causes appear to be rooted in a smaller number of deeper root causes, or drivers which are lack of an integrated framework for natural resources management (NRM) and land use planning, conflicting and contradictory laws and policies, weak national capacity for environmental impact assessment, corruption, rapid population growth and lack of sustainable livelihood opportunities for poor, rural, small farmers and fishers [7]. Besides, there is an average of 0.37% 2.07% of histograms of wetlands histograms that changed into water histograms and water histograms that changed into wetlands histograms between years of 1997 to 2007. By comparing the two average losses, a net loss of 2% of histogram from water to wetland histogram between years of 1997 to 2007 is obtained. These imply that, the areas that were formerly covered by water have been changing into wetlands at rate of 2%. In other word there is declining in areas covered by water which is been converted into wetlands at rate of 2% per year between years of 1997 to 2007. There are 0.8% and 7% average loss of histograms of wetlands which change into forests histograms and average loss of forests histograms into wetland histograms between years of 1997 to 2007, respectively. By comparing the above average percentage loss a net gain of wetland by 6.2% from forest histograms between years of 1997 to 2007 is obtained. This also shows that forest reservoirs in the Rufiji Delta are declining at rate of 6.7% changing into wetlands which have been contributed by the overexploitation of forest in Rufiji Delta. 5. Recommendation and Conclusion 5.1 Recommendations • Further studies using supervised classification of satellite images for change detection on different land cover and land use (LCLU) units should be designed and implemented in the Rufiji Basin. • The change detection, monitoring and mapping programs of natural resources using remote sensed data should developed and implemented by RUBADA and other stakeholders in Rufiji Basin. • High average loss of forest histogram which implies that there are decline trends of forest areas in the Rufiji Delta is calling inventory and physical verification of important forests species as initiatives stages prior the conservation programs. • Dam and tracking canal for management of on flash and sea level rise floods should designed and installed in Nyamisati, Kiomboi and Mchinga village which prone to on flash and Sea level rise floods. • The human population and settlement rate in Nyamisati, Kiomboi and Mchinga village should be checked in order not to exceed the carrying capacity of the environments. 16 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 5.2 Conclusion Change detection for motoring and mapping conditions of Rufiji Delta has shown that forest, wetlands and water are declining at different rates. Thus, the change detection using remote sensed data should be encouraged and incorporated in programs for monitoring and mapping the natural resources in Rufiji Basin. Besides, the study has confirmed that there is an opportunity of complementing the remote sensed data and in situ methods for change detection, monitoring and mapping natural resources of Rufiji Delta. However this information from unsupervised classification should be used as preliminary findings that need to be verified with supervised classification of the image which encompass the ground truthing. Moreover, the use of remote sensed data should be encouraged in change detection and classifications of agriculture lands, reserves, parks and animal ranges for analysis of soil and water degradation in relation to climate change and human induced effects. Acknowledgements I wish to acknowledge my heartfelt thanks to Ministry of Agriculture, Food Security and Cooperation (MAFSC) for giving me study leave to pursue the Postgraduate Diploma in Geomatics. Besides, I wish to express my heartfelt gratitude to Ardhi University particularly to the Dean of School of Geospatial Science and Technology Professor E. Liwa for his everlasting support during the study. Also I wish to acknowledge the thanks to the Department of Geomatics for preparing good environmental for postgraduate studies in particular to Head of Geomatics Dr. J. Chaula. Additionally I wish to appreciate the tireless academic and moral support provided by the project coordinator Mr.I. Hemed a Lecturer in Department of Geomatics. Lastly but not least, I sincerely thank to my family for the inspirational moral support and encouragement, especially my dear wife Martha Julius, my sons; Othniel Msogoya and Gershomu Msogoya who both have kept praying for me in my absence. References [1]. Jerome Theau “Change Detection” in Springer of Geographic Information, 2nd ed, vol.1 Kresse, Danko (Eds.), Springer-Verlag Berlin Heidelberg, 2012, 175-184. [2]. Rahman. S, Hasan. S. M. R, Islam, M. A, MaitrA.M.K. “Temporal change detection of vegetation coverage of Dhaka using Remote Sensing”. International Journal of Geomatics and Geosciences. Vol 2 pp 481-490, February, 2012. [3]. Mainoya, I.R. “The Use of Mangrove and their products by local community in Tanzania. Status and Utilization of Mangroves”. Presented at the Conf. on save the mangrove ecosystems at University of Dar es Salaam, Tanzania. 1986. [4]. Salam Al-Tamimi* and Jawad Taleb Al-Bakr. “Comparison Between Supervised and Unsupervised Classifications for Mapping Land Use/Cover in Ajloun Area”. Jordan Journal of Agricultural Sciences, Volume 1, pp 73-83, October, 2005. [5]. Jerome Kimaro “Forest Cover and Land Use Change in Ngumburuni Forest Reserve, Rufiji District, Tanzania”. Journal of Environment and Ecology. Vol. 4, pp 113-125, December, 2013. [6]. Ranu Rani Sethi, Amiya SagarSahu, R.C.Srivastava, Madhumita Das, Ashwani Kumar and Jugal Kishore Tripathy (2014). “Monitoring Land Cover Changes in Coastal Tract of Odisha Using Landsat 17 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 16, No 1, pp 1-18 ETM+ Imager”. Current World Environment, Vol. 9, pp 430-436 (2014). [7]. USAID. “Tanzania Environmental Threats and Opportunities Assessment”. A report produced for review by the United States Agency for international Development (USAID). Prepared by USAID- Tanzania, pp 1-145, November, 2012 . [8]. A. Wood, P. Stedman-Edwards, and J. Mang, (editors). “The Root Causes of Biodiversity Loss”. World Wildlife Fund and Earthscan Publications Ltd, London, UK, 2001, pp 1-304. [9]. Kamukula GL and Crafter S.A . “Wetlands of Tanzania”. In Proceeding of a Seminar on Wetlands of Tanzania. Held in 27 -29 – November, 1991, pp. 154–236. 18 2.3.1 Image preprocessing