Received: 11 February 2009 / Accepted: 16 December 2009
Ó The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract Landcoverchange in the BrazilianAmazon depends on the spatial variability of political, socioeco- nomic and biophysical factors, as well as on the land use history and its actors. A regionalscaleanalysis was made in Rondoˆnia State to identify possible differences in landcoverchange connected to spatial policies of land occu- pation, size and year of establishment of properties, accessibility measures and soil fertility. The analysis was made based on remotesensingdata and householdleveldata gathered with a questionnaire. Both types of analyses indicate that the highest level of total deforestation is found inside agrarian projects, especially in those established more than 20 years ago. Even though deforestation rates are similar inside and outside official settlements, inside agrarian projects forest depletion can exceed 50% at the property level within 10–14 years after establishment. The data indicate that both small-scale and medium to large- scale farmers contribute to deforestation processes in Rondoˆnia State encouraged by spatial policies of land occupation, which provide better accessibility to forest fringes where soil fertility and forest resources are impor- tant determinants of location choice.
Land abandonment in the BLA is a consequence of a range of factors that have changed across the last decades since the inception of large scale deforestation in the region. Namely these are; reduced crop productivity as a consequence of poor soil fertility; lack of financial incentives, migratory patterns; non- traditional land uses and market fluctuations . At Manaus, in 2011, land abandonment resulted in SF that was comprised mainly of areas with advanced ASF (50% $16 years), although large areas of intermediate ASF (37% between 6–15 years) were also present (Figure 3A and Figure 6). This suggests that the conservation areas were not only effective at preserving MF but also SF areas. As mentioned before, deforested areas in this study area were abandoned mainly because of poor soil fertility, with settlers moving to the nearby state of Roraima . It is clear from Figure 3A that the higher proportion of advanced ASF in the study area is in the northern half of the site. These areas undergoing regeneration in 2011 had mostly short PALU (64%) as a consequence of intensification of land use and requirement for new land, and 65% was deforested only once. In comparison, the Santare´m site, which included parts of the Tapajo´s National Forest, had regrowth dominated by intermediate ASF in 2010. Those areas experienced essentially short PALU (88% #2 years) and low FC (52% deforested once). According to Brondizio and Moran, 2012 , land abandonment in the region, and associated regeneration, was high until 1999. At this point, large-scale soybean cultivation started, mainly because of the decay of primary and secondary roads, lack of social services, and limited access to water. On the other hand, land abandonment in Machadinho d’Oeste resulted in SF that was mainly in the initial (46%) and intermediate (41%) ASF classes, with these areas experiencing mostly (75%) short PALU and low FC (57%). This was a planned settlement and most of the deforested area is under agriculture and or pasture use, suggesting that a vast majority of the area undergoing SF could be indeed forest fallow that might be under cattle ranching use or being subjected to subsistence agriculture in a crop/fallow cycle .
Deforestation typically results in the replacement of forests by crop- lands and pastures but these are often abandoned after a few years and replaced with secondary forests. These serve to accumulate carbon and restore biodiversity lost previously during the initial deforestation pro- cess (Brown and Lugo, 1990). The age, structure and species composi- tion of secondary forests establishing on abandoned lands are a consequence of several factors, such as land use history, soil fertility and distance to mature primary forests (Chazdon, 2003). For this rea- son, knowledge of the age and land use history of areas under regener- ation is needed to better understand patterns of carbon accumulation and recovery (or otherwise) of biodiversity. Mapping the age of tropical secondary forests often relies on comparing time-series of landcover maps (including a secondary forest class) obtained from classi ﬁ cation of high-resolution optical data (Carreiras et al., 2014; Nelson et al., 2000; Prates-Clark et al., 2009). Other approaches use single-date re- mote sensing (often optical) data to map the age of secondary forests into classes (e.g., initial, intermediate and advanced secondary forests) (Lucas et al., 2000; Vieira et al., 2003). Recently, Chazdon et al. (2016) used an above-ground forest biomass map of the Neotropics (Baccini et al., 2012) in combination with ~ 1500 plots in secondary forests of known age to derive a large-scale map of the age of secondary forests. All these approaches rely on the availability of reference information about i) areas occupied by secondary forests (time-series approach) or ii) areas of known age or age class (single-date approach). The applica- tion of these methods is sometimes severely hampered by frequent cloud cover in tropical regions, thus leading to some regions having poor coverage by optical sensors. For this reason, all-weather Synthetic Aperture Radar (SAR) data are increasingly being used and promoted to improve land use/landcoverchange monitoring over tropical regions (Reiche et al., 2016). SAR data can provide information related to struc- tural parameters of the forest (above-ground biomass, canopy height) (Carreiras et al., 2012; Cartus et al., 2012; Lucas et al., 2010; Santoro et al., 2011) that could prove useful when retrieving the age of secondary forests and complement that provided by optical sensors.
Propulsion Laboratory 2009). Though data for the two bands was collected simultaneously, the coverage of the X-band is limited compared to the C-band and the processing into DEMs was done separately by the German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) (respectively). Some studies have found the X-band DEM to contain more outlier values and consequently to appear ‘noisier’ than the C-band DEM, and a direct comparison of the two DEMs revealed a systematic bias of higher error (around 10 m) in certain regions of the globe (Hoffmann and Walter 2006). SAR remotesensing has several advantages over photogrammetric methods of elevation data collection, including cloud penetration, potential night-time data collection, potential partial vegetation penetration, control over the angle of illumination, and the flexibility of different polarizations (Jensen 2007). Additionally, the SRTM 30° to 58° off-nadir swaths were collected from an altitude of 233 km yielding a wide swath width of 225 km (U.S. Geological Survey 2009). The DEM produced from the SRTM data has a spatial resolution of 1’ (1 arc second or approximately 30 m, degraded to 3’ outside the United States) and a vertical accuracy performance goal of ±16 m (Pierce et al. 2006). Global evaluations of vertical accuracy found the SRTM to have an average absolute height error of 9 m in North America (Rodriguez, Morris, and Belz 2006). However, in the mountainous, heavily forested region of Appalachia, this average error value may be higher (Bolstad and Stowe 1994; Blanchard, Rogan and Woodcock 2010). Comparisons to reference DEMs in West Virginia,
water and reservoir archived 100% for both producer and user accuracy because the signature of water is sufficient difference from vegetation and other landcover types. Deciduous forest and evergreen forest are also classified with high accuracy as 96 and 95% for producer accu- racy and 100 and 95% for user accuracy, respectively. The cropland (91.67% producer accu- racy, 73.33% user accuracy) and orchard (71.43% producer accuracy and 83.333 user accuracy) categories had moderate classification performance. Rangeland (62% producer accuracy) had lower accuracy as it was mixed with barren land and urban village type. The wetland cat- egory had poor classification performance except in the 2005 classified image where it had high accuracy performance. The uncertainty of classification among forests, agricultural lands and wetlands occurred due to similar spectral reflectance of green vegetation. This confu- sion usually occurs when using moderate spatial resolution images such as Landsat satellite images to classify areas that have heterogeneous LULC .
Abstract: Changes in landcover are inevitable phenomena that occur in all parts of the world. Landcover changes can occur due to natural phenomena that include runoff, soil erosion and sedimentation besides man-made phenomena that include deforestation, urbanization and conversion of land covers to suit human needs. Several works on change detection have been carried out elsewhere, however there were lack of effort in analyzing the issues that affect the performance of existing change detection techniques. The study presented in this paper aims to detect changes of land covers by using remotesensing satellite data. The study involves detection of landcover changes using remotesensing techniques. This makes use satellite data taken at different times over a particular area of interest. The data has resolution of 30 m and records surface reflectance at approximately 0.4 to 0.7 micrometers wavelengths. The study area is located in Selangor, Malaysia and occupied with tropical land covers including coastal swamp water, sediment plumes, urban, industry, water, bare land, cleared land, oil palm, rubber and coconut. Initially, region of interests (ROI) were drawn on each of the land covers in order to extract the training pixels. Landsat satellite bands 1, 2, 3, 4, 5 and 7 were then used as the input for the three supervised classification methods namely Support Vector Machine (SVM), Maximum Likelihood (ML) and Neural Network (NN). Different sizes of training pixels were used as the input for the classification methods so that the performance can be better understood. The accuracy of the classifications was then assessed by analyzing the classifications with a set of reference pixels using a confusion matrix. The classification methods were then used to identify the conversion of landcover from year 2000 to 2005 within the study area. The outcomes of the landcoverchange detection were reported in terms quantitative and qualitative analyses. The study shows that SVM gives a more accurate and realistic landcoverchange detection compared to ML and NN mainly due to not being much influenced by the size of the training pixels. The findings of the study serve as important input for decision makers in managing natural resources and environment in the tropics systematically and efficiently.
Table 5 lists the UA and PA for all the three classification levels considering the best OA value between the k-NN and Random Forest algorithm in the classification procedure. For the various land covers among the three levels, forest category is one of the best classified with high PA and UA above 95%. For instance, the conifer got highest accuracy in Level 2 and Level 3, followed by the deciduous and the mixed forest. At the acquired date of image, the cropland including wheat, barley, rapeseeds, corn and other types show various maturity, i.e. a few corn fields were newly planted whereas several wheat or barley grew mature enough for harvest. Therefore, compared to the classification between cropland and grassland in Level 2, serious misclassification existed in Level 3 category among the different crop fields. The confusion matrix in Table 6 shows the validation data for the 14 classes of Level 3. Because of limited reference pixels from the field campaign, the validation pixels of oat did not exceed the recommended number of 50. Here it was kept for the comparison need. The matrix provides detail information about the mixture of different crops with similar growing characteristics, such as barley and wheat, triticale and wheat. All in all, this single-date Landsat 8 image did not provide sufficient information for distinguishing between detailed croplands, and at least one other image at spring or autumn will be needed to supplement the classification. However, such an (cloudfree) image was not available for this year.
Land use/landcover (LU/LC) changes in Dindigul District, Tamil Nadu, were determined during the period 2000 to 2016 using Geospatial technology. Landsat imageries were used to study the past and present land use/ landcover changes. The study results show that there is an increasing trend in the built-up area, especially in urban built-up.
enhancements and registration was performed on the images. Supervised classification was performed by using maximum likelihood method. Land-use classes; woodland, grassland, cultivated land, bare soil, rivers, dams, water ponds, built-up area, tailing dams and open cast mines were identified from satellite data and field surveys. Results showed that in the last three decades open cast mines, tailing dams; mine dumps and return water ponds have increased extensively in the Rustenburg region; vegetation has undergone a general decrease; woodland and grassland have been changed to cultivated land. An expansion of the built-up area can be explained by the fact that there was increase in the development of transport networks; settlements developed over the years due to the immigration of mine workers in the area. Consequently, the landscape became highly disturbed due to increased mining and agricultural activities.
In a change detection context, image fusion has been used to detect land use and landcoverchange over urban areas. For example, Zhou, Huang  compared three methods for landcover classification of shaded areas from high spatial resolution imagery in an urban envi- ronment, including combined spectral information, lin- ear-correlation correction and multisource data fusion. The results indicated that data fusion achieved better accuracy when compared with combined spectral infor- mation and linear-correlation correction. Zeng, Zhang  presented the results of different temporal SAR and optical image fusion algorithms for landcoverchange detection. The data used in their research were from SPOT-5 imagery and RADARSAT-1. Peijun, Sicong  used feature and decision level fusion to combine simple change detectors and to build an automatic change detec- tion procedure. This method was tested with multi-tem- poral CBERS and HJ-1 images. The results were satis- factory and more effective than other methods.
The main point is that process understanding is used to confirm LCLU change: it has to have sufficient spatial and state dynamics captured over appropriate timeframes to be recorded. It is also used to identify more nuanced aspects of change (Zhu, 2017). Previously, LCLU change identification focused on depletions, punctual removals, and so on. However, changes in within‐LCLU class condition now provide a deeper picture of the characteristics of dynamic processes. These include monitoring forest harvesting and wildfire (White et al., 2017), comparisons of regional rates of forest recovery (Frazier, Coops, Wulder, Hermosilla, & White, 2018), and capturing the changing states of forest dynamics (Gómez et al., 2011). Knowledge of process is increasingly used to inform landcover labeling (Wulder et al., 2018). For instance, knowledge of time since disturbance informs on successional stage following harvest or wildfire, distinguishing between classes and allowing for the application of rules to promote logical transitions (Gómez et al., 2016). Within‐year information can also be used to infer subclass processes, in‐ creasing the categorical depth of landcover mapping (Pasquarella, Holden, & Woodcock, 2018).
migration of illegal wood extraction to the northeast of Machadinho d’Oeste, into Colniza municipality in Mato Grosso State.
2.4.2 Database of potential location factors
Potential land use/cover proximate drivers and location factors were selected based on the review of previous land use studies, fieldwork information from 2001 and 2006 and data availability. The selected variables include biophysical, accessibility and socioeconomic aspects, as well as public policies. The first exploratory models included 55 variables, but only 38 had significant contributions in the final models (see Table 2.2). Classes of the categorical variables geomorphology, lithology and soil types are counted each as a unique variable. The grid database was built at a spatial resolution of 250 x 250 m, the highest resolution possible with the available data. This resolution is an exact multiplier of the average size of lots in the agrarian projects (2000 x 500 m). The original scale and resolution of the variables selected were quite different; especially biophysical variables have a different spatial variability than socioeconomic data and the accessibility measures. This suggests that some loss of information took place during the data aggregation process. In a preliminary test all data was aggregated to 500 m resolution. Initial analysis demonstrated similar patterns and correlation between deforested areas, secondary forest and location factors indicating limited loss of information. These results are consistent to other studies at multiple scales (Veldkamp and Fresco, 1997; Walsh et al., 1999). Therefore, it was decided not to change the data resolution and use only the 250 m resolution data.
change has become a central component in current strategies for managing natural resources and monitoring environmental changes. This involves development of spatial and temporal database and analysis techniques. The advancement in the concept of vegetation mapping has greatly increased research on land use/ landcoverchange thus providing an accurate evaluation of the spread and health of the world’s forest, grassland, and agricultural resources has become an important priority. Viewing the Earth from space is now crucial to the understanding of the influence of man’s activities on his natural resource base over time. In situations of rapid and often unrecorded land use change, observations of the earth from space provide objective information of human utilization of the landscape. Over the past years, data from Earth sensing satellites has become vital in mapping the Earth’s features and infrastructures, managing natural resources and studying environmental change. Further, satellite remotesensingdata have been successfully used to estimate Leaf Area Index (LAI), based on the relationship between LAI and the Normalized Difference Vegetation Index (NDVI) (Kale et al., 2005). An accurate forest cover-type and/or land-classification system is essential to providing
Numerious studies have been carried out on LULCC in Malaysia e.g study by Jusoff (2009), Sujaul et al.,(2010), Tan et al., (2011). Reynolds et al., (2011) and Aburas et al.,(2015). But only, a limited research has been conducted on LULCC in Iskandar Malaysia (Deilami et al., 2014; Majid and Hardy, 2010). The results obtained indicate that forest regions have been reduced and their function has changed (Wicke et al., 2011). However, none of these studies has analyzed the impacts of these changes on land surface temperature (LST) and any simulation and prediction study has not been performed with regard to future effects of LULCC on LST. This study, will investigate the relationship between LST and LULCC during both day and night times and required analyses have been done to find the probability of existence of UHI in a long term (from 1989 to 2014), and it also made prediction about day and night LST changes by 2025 based on LULCC.
Geographical information systems (GIS) and remotesensing are well-established information technologies, the value of which for applications in land and natural resources management are now widely recognized. Current technologies such as geographical information systems (GIS) and remotesensing provide a cost effective and accurate alternative to understand landscape dynamics. Digital change detection techniques based on multi-temporal and multi- spectral remotely sensed data have demonstrated an enormous potential to understand landscape dynamics- detect, identify, map, and monitor differences in land use and landcover patterns over time.
Forest coverage change w as perform ed over Peninsular M alaysia using Landsat TM and ET M + images. In addition to Landsat, the ALOS Palsar radar image w as also used to discrim inate palm oil area from forested areas. A lot o f developm ent and log production to generate income occurred since 1990. Therefore, the period o f tim e from 1990 to 2010 is an im portant period to study the changes o f forest cover w ith the available data from Landsat . H owever, due to the heavy cloud cover only tw o sub regions w ith less cloud cover w ere selected (K uala Lum pur and Iskandar M alaysia) to analyse the deforestation and degradation o f forest cover betw een 1990 and 2010.
The present research work is essential job for the populated part of the Indian Sundarban, because of very active estuarine and sedimentary process operated this area rapidly and active delta building operation is also operating here very rigorously. Beside this, very high pressure of population throughout the GBD (Ganga Brahamaputra Delta), influence the whole set up of LULC. In this regards application of Geoinformatics in LULC classification will be only proper way out for this operation. The major objectives of our study to find out the a complete land use and landcover map during 1973 - 2014, and identify the major problems of the study area and suggest the suitable recommendations through literature survey, application of remotesensing, interpretation of satellite data through ERDAS and ArcGIS Software. Remotesensing and GIS have covered wide range of applications in the fields of agriculture, environments, and integrated eco-environment assessment. Several researchers have focused on LU/LC studies because of their adverse effects on ecology of the area and vegetation. Present study area witnesses a change in the years 1973, 1989, 2000 & 2014 due to population increase substantially.
Landcover suitability images were derived to determine the transition suitability of each pixel for each land use/cover type. The suitability criteria for vegetation, degraded vegetation, bare and degraded soil and water was based on temporal analysis of landcover trends from 1972 to 2006. A similar technique was used by Ye and Bai (2008) to derive suitability images. The state-and-transition model used in rangeland ecology was used to understand the processes underlying landcoverchange dynamics (Briske et al., 2005). These principles were applied to determine suitable sites for intact vegetation and degraded vegetation, bare and degraded soil because state-and-transition models accommodate greater complexity by considering vegetation dynamics in response to multiple drivers and by characterizing transitions to alternative stable states on individual ecological sites (Briske et al., 2005). Vegetation dynamics are characterized by continuous reversible and discontinuous non-reversible trends (Wu and Loucks, 1995; Watson et al., 1996; Illius and O’Connor, 1999). The occurrence of continuous and reversible vegetation dynamics is dominant in stable vegetation states. Discontinuous and non-reversible dynamics result once one stable state replaces another, when thresholds have been exceeded. Ecological thresholds are difficult to identify since ecosystem modification often imposes a series of feedback mechanisms that maintain or reinforce the altered state and limits reversal to the previous stable state (Archer et al., 2001; Scheffer et al., 2001; van de Koppel et al., 2002). It is noteworthy however, that vegetation dynamics exhibit complex trends difficult to model without simplifications. Given that predictive vegetation mapping is based on the ecological niche theory and gradient analysis. This study therefore assumes that suitable sites for vegetation are ecological niches in which vegetation established itself in the past when anthropogenic effects were minimal and climatic factors favourable. The distribution of settlements in the Keiskamma catchment is characterised by a mixture of land tenure systems that exist in the region (Ruhiiga, 2000; Bank and Minkley, 2005). Such complexities are difficult to model without simplifications.
ABSTRACT: Natural resources play an important role in the economic growth and development of the region, more so, the degree of development is a function of natural resource occurrence and its utilization and sustenance for future population needs and demands. Information on land use/landcover in the form of maps and statistical data is very vital for spatial planning, management and utilization of land for agricultural, forestry, pasture, urban-industrial, Environmental studies, economic production, etc. Keeping this in view, land use categories are mapped by using on- screen visual interpretation techniques in Arc GIS environment. For detection of land use classes taken the multi dated satellite data of Land-sat has been used for the study.
high degree of user control. Training points were repeatedly selected from the whole study area by drawing a polygon around training sites of interests. Land use / Landcover classes of these training points were extracted with respect to general knowledge obtained from topographic maps and field visits. The pixel based supervised image classification with maximum likelihood classification algorithm was used to map the land-use/cover classes (Lillesand and Kiefer, 2000). 80-100 training sites referring to each of the classes were collected.